Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Clinical indications and protocol considerations for selecting initial body weight support levels in gait rehabilitation: a systematic review

BACKGROUND

Body weight support (BWS) training devices are frequently used to improve gait in individuals with neurological impairments, but guidance in selecting an appropriate level of BWS is limited. Here, we aim to describe the initial BWS levels used during gait training, the rationale for this selection and the clinical goals aligned with BWS training for different diagnoses.

METHOD

A systematic literature search was conducted in PubMed, Embase and Web of Science, including terms related to the population (individuals with neurological disorders), intervention (BWS training) and outcome (gait). Information on patient characteristics, type of BWS device, BWS level and training goals was extracted from the included articles.

RESULTS

Thirty-three articles were included, which described outcomes using frame-based (stationary or mobile) and unidirectional ceiling-mounted devices on four diagnoses (multiple sclerosis (MS), spinal cord injury (SCI), stroke, traumatic brain injury (TBI)). The BWS levels were highest for individuals with MS (median: 75%, IQR: 6%), followed by SCI (median: 40%, IQR: 35%), stroke (median: 30%, IQR: 4.75%) and TBI (median: 15%, IQR: 0%). The included studies reported eleven different training goals. Reported BWS levels ranged between 30 and 75% for most of the training goals, without a clear relationship between BWS level, diagnosis, training goal and rationale for BWS selection. Training goals were achieved in all included studies.

CONCLUSION

Initial BWS levels differ considerably between studies included in this review. The underlying rationale for these differences was not clearly motivated in the included studies. Variation in study designs and populations does not allow to draw a conclusion on the effectiveness of BWS levels. Hence, it remains difficult to formulate guidelines on optimal BWS settings for different diagnoses, BWS devices and training goals. Further efforts are required to establish clinical guidelines and to experimentally investigate which initial BWS levels are optimal for specific diagnoses and training goals.

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors

BACKGROUND AND OBJECTIVES

Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored.

METHODS

We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden.

RESULTS

The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden.

CONCLUSIONS

We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.

Gait characterization in rare bone diseases in a real-world environment - A comparative controlled study

BACKGROUND

Rare bone diseases (RBD) cause physical and sensory disability that affects quality of life. Mobility challenges are common for people with RBDs, and travelling to gait analysis labs can be very complex. Smartphone sensors could provide remote monitoring.

RESEARCH QUESTION

This study aimed to search for and identify variables that can be used to discriminate between people with RBD and healthy people by using built-in smartphone sensors in a real-world setting.

METHODS

In total, 18 participants (healthy: n=9; RBD: n=9), controlled by age and sex, were included in this cross-sectional study. A freely available App (Phyphox) was used to gather data from built-in smartphone sensors (accelerometer & gyroscope) at 60 Hz during a 15-min walk on a level surface without turns or stops. Temporal gait parameters like cadence, mean stride time and, coefficient variance (CoVSt) and nonlinear analyses, as the largest Lyapunov exponent (LLE) & sample entropy (SE) in the three accelerometer axes were used to distinguish between the groups and describe gait patterns.

RESULTS

The LLE (p=0.04) and the SE of the z-axis (p=0.01), which are correlated with balance control during walking and regularity of the gait, are sufficiently sensitive to distinguish between RBD and controls.

SIGNIFICANCE

The use of smartphone sensors to monitor gait in people with RBD allows for the identification of subtle changes in gait patterns, which can be used to inform assessment and management strategies in larger cohorts.

A novel method for accurate division of the gait cycle into seven phases using shank angular velocity

BACKGROUND

Accurate detection of gait events is crucial for gait analysis, enabling the assessment of gait patterns and abnormalities. Inertial measurement unit (IMU) sensors have gained traction for event detection, mainly focusing on initial contact (IC) and toe-off (TO) events. However, effective detection of other key events such as heel rise (HR), feet adjacent (FA), and tibia vertical (TBV) is essential for comprehensive gait analysis.

RESEARCH QUESTION

Can a novel IMU-based method accurately detect HR, TO, FA, and TBV events, and how does its performance compare with existing methods?

METHODS

We developed and validated an IMU-based method using cumulative mediolateral shank angular velocity (CSAV) for event detection. A dataset of nearly 25,000 gait cycles from healthy adults walking at varying speeds and footwear conditions was used for validation. The method's accuracy was assessed against force plate and motion capture data and compared with existing TO detection methods.

RESULTS

The CSAV method demonstrated high accuracy in detecting TO, FA, and TBV events and moderate accuracy in HR event detection. Comparisons with existing TO detection methods showcased superior performance. The method's stability across speed and shoe variations underscored its robustness.

SIGNIFICANCE

This study introduces a highly accurate IMU-based method for detecting gait events needed to divide the gait cycle into seven phases. The effectiveness of the CSAV method in capturing essential events across different scenarios emphasizes its potential applications. Although HR event detection can be further improved, the precision of the CSAV method in TO, FA, and TBV detection advance the field. This study bridges a critical gap in IMU-based gait event detection by introducing a method for subdividing the swing phase into its subphases. Further research can focus on refining HR detection and expanding the method's utility across diverse gait contexts, thereby enhancing its clinical and scientific significance.

Closed-Loop Wearable Device Network of Intrinsically-Controlled, Bilateral Coordinated Functional Electrical Stimulation for Stroke

Innovative functional electrical stimulation has demonstrated effectiveness in enhancing daily walking and rehabilitating stroke patients with foot drop. However, its lack of precision in stimulating timing, individual adaptivity, and bilateral symmetry, resulted in diminished clinical efficacy. Therefore, a closed-loop wearable device network of intrinsically controlled functional electrical stimulation (CI-FES) system is proposed, which utilizes the personal surface myoelectricity, derived from the intrinsic neuro signal, as the switch to activate/deactivate the stimulation on the affected side. Simultaneously, it decodes the myoelectricity signal of the patient's healthy side to adjust the stimulation intensity, forming an intrinsically controlled loop with the inertial measurement units. With CI-FES assistance, patients' walking ability significantly improved, evidenced by the shift in ankle joint angle mean and variance from 105.53° and 28.84 to 102.81° and 17.71, and the oxyhemoglobin concentration tested by the functional near-infrared spectroscopy. In long-term CI-FES-assisted clinical testing, the discriminability in machine learning classification between patients and healthy individuals gradually decreased from 100% to 92.5%, suggesting a remarkable recovery tendency, further substantiated by performance on the functional movement scales. The developed CI-FES system is crucial for contralateral-hemiplegic stroke recovery, paving the way for future closed-loop stimulation systems in stroke rehabilitation is anticipated.

A Novel Gait Event Detection Algorithm Using a Thigh-Worn Inertial Measurement Unit and Joint Angle Information

This paper describes the development and evaluation of a novel, threshold-based gait event detection algorithm utilizing only one thigh inertial measurement unit (IMU) and unilateral, sagittal plane hip and knee joint angles. The algorithm was designed to detect heel strike (HS) and toe off (TO) gait events, with the eventual goal of detection in a real-time exoskeletal control system. The data used in the development and evaluation of the algorithm were obtained from two gait databases, each containing synchronized IMU and ground reaction force (GRF) data. All database subjects were healthy individuals walking in either a level-ground, urban environment or a treadmill lab environment. Inertial measurements used were three-dimensional thigh accelerations and three-dimensional thigh angular velocities. Parameters for the TO algorithm were identified on a per-subject basis. The GRF data were utilized to validate the algorithm's timing accuracy and quantify the fidelity of the algorithm, measured by the F1-Score. Across all participants, the algorithm reported a mean timing error of -41±20 ms with an F1-Score of 0.988 for HS. For TO, the algorithm reported a mean timing error of -1.4±21 ms with an F1-Score of 0.991. The results of this evaluation suggest that this algorithm is a promising solution to inertial based gait event detection; however, further refinement and real-time evaluation are required for use in exoskeletal control.

Using In-Shoe Inertial Measurement Unit Sensors to Understand Daily-Life Gait Characteristics in Patients With Distal Radius Fractures During 6 Months of Recovery: Cross-Sectional Study

BACKGROUND

A distal radius fracture (DRF) is a common initial fragility fracture among women in their early postmenopausal period, which is associated with an increased risk of subsequent fractures. Gait assessments are valuable for evaluating fracture risk; inertial measurement units (IMUs) have been widely used to assess gait under free-living conditions. However, little is known about long-term changes in patients with DRF, especially concerning daily-life gait. We hypothesized that, in the long term, the daily-life gait parameters in patients with DRF could enable us to reveal future risk factors for falls and fractures.

OBJECTIVE

This study assessed the spatiotemporal characteristics of patients with DRF at 4 weeks and 6 months of recovery.

METHODS

We recruited 16 women in their postmenopausal period with DRF as their first fragility fracture (mean age 62.3, SD 7.0 years) and 28 matched healthy controls (mean age 65.6, SD 8.0 years). Daily-life gait assessments and physical assessments, such as hand grip strength (HGS), were performed using an in-shoe IMU sensor. Participants' results were compared with those of the control group, and their recovery was assessed for 6 months after the fracture.

RESULTS

In the fracture group, at 4 weeks after DRF, lower foot height in the swing phase (P=.049) and higher variability of stride length (P=.03) were observed, which improved gradually. However, the dorsiflexion angle in the fracture group tended to be lower consistently during 6 months (at 4 weeks: P=.06; during 6 months: P=.07). As for the physical assessments, the fracture group showed lower HGS at all time points (at 4 weeks: P<.001; during 6 months: P=.04), despite significant improvement at 6 months (P<.001).

CONCLUSIONS

With an in-shoe IMU sensor, we discovered the recovery of spatiotemporal gait characteristics 6 months after DRF surgery without the participants' awareness. The consistently unchanged dorsiflexion angle in the swing phase and lower HGS could be associated with fracture risk, implying the high clinical importance of appropriate interventions for patients with DRF to prevent future fractures. These results could be applied to a screening tool for evaluating the risk of falls and fractures, which may contribute to constructing a new health care system using wearable devices in the near future.

Technology and physical activity for preventing cognitive and physical decline in older adults: Protocol of a pilot RCT

BACKGROUND

Cognitive frailty, defined as having both physical frailty and cognitive impairment that does not satisfy the criteria for Major Neurocognitive Disorder, represents an elevated risk for morbidity. Hence, it is crucial to mitigate such risks. Physical activity interventions have been found effective in protecting against physical frailty and cognitive deterioration. This pilot RCT examines if smartwatches and mobile phone applications can help to increase physical activity, thereby improving physical and cognitive outcomes.

METHODS

Older individuals (n = 60) aged 60 to 85 years old will have their physical activity tracked using a smartwatch. The subjects will be randomized into two arms: one group will receive daily notification prompts if they did not reach the recommended levels of PA; the control group will not receive prompts. Outcome variables of physical activity level, neurocognitive scores, and physical frailty scores will be measured at baseline, T1 (3 months), and T2 (6 months). Sleep quality, levels of motivation, anxiety, and depression will be controlled for in our analyses. We hypothesize that the intervention group will have higher levels of physical activity resulting in improved cognitive and physical outcomes at follow-up. This study was approved by the National University of Singapore's Institutional Review Board on 17 August 2020 (NUS-IRB Ref. No.: H-20-038).

DISCUSSION

Wearable sensors technology could prove useful by facilitating self-management in physical activity interventions. The findings of this study can justify the use of technology in physical activity as a preventive measure against cognitive frailty in older adults. This intervention also complements the rapidly rising use of technology, such as smartphones and wearable health devices, in our lives today.

REGISTRATION DETAILS

This study has been retrospectively registered on clinicaltrials.gov on 5th January 2021 (NCT Identifier: NCT04692974), after the first participant was recruited.

Validation of portable in-clinic video-based gait analysis for prosthesis users

Despite the common focus of gait in rehabilitation, there are few tools that allow quantitatively characterizing gait in the clinic. We recently described an algorithm, trained on a large dataset from our clinical gait analysis laboratory, which produces accurate cycle-by-cycle estimates of spatiotemporal gait parameters including step timing and walking velocity. Here, we demonstrate this system generalizes well to clinical care with a validation study on prosthetic users seen in therapy and outpatient clinics. Specifically, estimated walking velocity was similar to annotated 10-m walking velocities, and cadence and foot contact times closely mirrored our wearable sensor measurements. Additionally, we found that a 2D keypoint detector pretrained on largely able-bodied individuals struggles to localize prosthetic joints, particularly for those individuals with more proximal or bilateral amputations, but after training a prosthetic-specific joint detector video-based gait analysis also works on these individuals. Further work is required to validate the other outputs from our algorithm including sagittal plane joint angles and step length. Code for the gait transformer and the trained weights are available at https://github.com/peabody124/GaitTransformer .

Accuracy validation of a wearable IMU-based gait analysis in healthy female

OBJECTIVE

The aim of this study was to assess the accuracy and test-retest reliability of a wearable inertial measurement unit (IMU) system for gait analysis in healthy female compared to a gold-standard optoelectronic motion capture (OMC) system.

METHODS

In our study, we collected data from 5 healthy young females. Participants were attached with markers from both the OMC system and the IMU system simultaneously. Data was collected when participants walked on a 7 m walking path. Each participant performed 50 repetitions of walking on the path. To ensure the collection of complete gait cycle data, a gait cycle was considered valid only if the participant passed through the center of the walking path at the same time that the OMC system detected a valid marker signal. As a result, 5 gait cycles that met the standards of the OMC system were included in the final analysis. The stride length, cadence, velocity, stance phase and swing phase of the spatio-temporal parameters were included in the analysis. A generalized linear mixture model was used to assess the repeatability of the two systems. The Wilcoxon rank-sum test for continuous variables was used to compare the mean differences between the two systems. For evaluating the reliability of the IMU system, we calculated the Intra-class Correlation Coefficient (ICC). Additionally, Bland-Altman plots were used to compare the levels of agreement between the two systems.

RESULTS

The measurements of Spatio-temporal parameters, including the stance phase (P = 0.78, 0.13, L-R), swing phase (P = 0.78, 0.13, L-R), velocity (P = 0.14, 0.13, L-R), cadence (P = 0.53, 0.22, L-R), stride length (P = 0.05, 0.19, L-R), by the IMU system and OMC system were similar. Which suggested that IMU and OMC systems could be used interchangeably for gait measurements. The intra-rater reliability showed an excellent correlation for the stance phase, swing phase, velocity and cadence (Intraclass Correlation Coefficient, ICC > 0.9) for both systems. However, the correlation of stride length was poor (ICC = 0.36, P = 0.34, L) to medium (ICC = 0.56, P = 0.22, R). Additionally, the measurements of IMU systems were repeatable.

CONCLUSIONS

The results of IMU system and OMC system shown good repeatability. Wearable IMU system could analyze gait data accurately. In particular, the measurement of stance phase, swing phase, velocity and cadence showed excellent reliability. IMU system provided an alternative measurement to OMC for gait analysis. However, the measurement of stride length by IMU needs further consideration.

Gait characteristics in patients with distal radius fracture using an in-shoe inertial measurement system at various gait speeds

BACKGROUND

Distal radius fractures (DRF) commonly occur in early postmenopausal females as the first fragility fracture. Although the incidence of DRF in this set of patients may be related to a lower ability to control their balance and gait, the detailed gait characteristics of DRF patients have not been examined.

RESEARCH QUESTION

Is it possible to identify the physical and gait features of DRF patients using in-shoe inertial measurement unit (IMU) sensors at various gait speeds and to develop a machine learning (ML) algorithm to estimate patients with DRF using gait?

METHODS

In this cross-sectional case control study, we recruited 28 postmenopausal females with DRF as their first fragility fracture and 32 age-matched females without a history of fragility fractures. The participants underwent several physical and gait tests. In the gait performance test, the participants walked 16 m with the in-shoe IMU sensor at slower, preferred, and faster speeds. The gait parameters were calculated by the IMU, and we applied the ML technique using the extreme gradient boosting (XGBoost) algorithm to predict the presence of DRF.

RESULTS

The fracture group showed lower hand grip strength and lower ability to change gait speed. The difference in gait parameters was mainly observed at faster speeds. The amplitude of the change in the parameters was small in the fracture group. The XGBoost model demonstrated reasonable accuracy in predicting DRFs (area under the curve: 0.740), and the most relevant variable was the stance time at a faster speed.

SIGNIFICANCE

Gait analysis using in-shoe IMU sensors at different speeds is useful for evaluating the characteristics of DRFs. The obtained gait parameters allow the prediction of fractures using the XGBoost algorithm.

The validity of smartphone-based spatiotemporal gait measurements during walking with and without head turns: Comparison with the GAITRite® system

Smartphone accelerometry has potential to provide clinicians with specialized gait analysis not available in most clinical settings. The Gait&Balance Application (G&B App) uses smartphone accelerometry to assess spatiotemporal gait parameters under two conditions: walking looking straight ahead and walking with horizontal head turns. This study investigated the validity of G&B App gait parameters compared with the GAITRite® pressure-sensitive walkway. Healthy young and older adults (age range 21-85 years) attended a single session where a smartphone was secured over the lumbosacral junction. Data were collected concurrently with the app and GAITRite® systems as participants completed the two walking conditions. Spatiotemporal gait parameters for 54 participants were determined from both systems and agreement evaluated with partial Pearson's correlation coefficients and limits of agreement. The results demonstrated moderate to excellent validity for G&B App measures of step time (rp 0.97, 95 % CI [0.96, 0.98]), walking speed (rp 0.83 [0.78, 0.87]), and step length (rp 0.74, [0.66, 0.80]) when walking looking straight ahead, and results were comparable with head turns. The validity of walking speed and step length measures was influenced by sex and height. G&B App measures of step length variability, step time variability, step length asymmetry, and step time asymmetry had poor validity. The G&B App has potential to provide valid measures of unilateral and bilateral step time, unilateral and bilateral step length, and walking speed, under two walking conditions in healthy young and older adults. Further research should validate this tool in clinical conditions and optimise the algorithm for demographic characteristics.

Sonification for Personalised Gait Intervention

Mobility challenges threaten physical independence and good quality of life. Often, mobility can be improved through gait rehabilitation and specifically the use of cueing through prescribed auditory, visual, and/or tactile cues. Each has shown use to rectify abnormal gait patterns, improving mobility. Yet, a limitation remains, i.e., long-term engagement with cueing modalities. A paradigm shift towards personalised cueing approaches, considering an individual's unique physiological condition, may bring a contemporary approach to ensure longitudinal and continuous engagement. Sonification could be a useful auditory cueing technique when integrated within personalised approaches to gait rehabilitation systems. Previously, sonification demonstrated encouraging results, notably in reducing freezing-of-gait, mitigating spatial variability, and bolstering gait consistency in people with Parkinson's disease (PD). Specifically, sonification through the manipulation of acoustic features paired with the application of advanced audio processing techniques (e.g., time-stretching) enable auditory cueing interventions to be tailored and enhanced. These methods used in conjunction optimize gait characteristics and subsequently improve mobility, enhancing the effectiveness of the intervention. The aim of this narrative review is to further understand and unlock the potential of sonification as a pivotal tool in auditory cueing for gait rehabilitation, while highlighting that continued clinical research is needed to ensure comfort and desirability of use.

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022

Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.

Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review

In recent years, marked progress has been made in wearable technology for human motion and posture recognition in the areas of assisted training, medical health, VR/AR, etc. This paper systematically reviews the status quo of wearable sensing systems for human motion capture and posture recognition from three aspects, which are monitoring indicators, sensors, and system design. In particular, it summarizes the monitoring indicators closely related to human posture changes, such as trunk, joints, and limbs, and analyzes in detail the types, numbers, locations, installation methods, and advantages and disadvantages of sensors in different monitoring systems. Finally, it is concluded that future research in this area will emphasize monitoring accuracy, data security, wearing comfort, and durability. This review provides a reference for the future development of wearable sensing systems for human motion capture.

Walking Bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back

Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as "walking" or "non-walking". One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.

The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches

Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.

A spinal cord neuroprosthesis for locomotor deficits due to Parkinson's disease

People with late-stage Parkinson's disease (PD) often suffer from debilitating locomotor deficits that are resistant to currently available therapies. To alleviate these deficits, we developed a neuroprosthesis operating in closed loop that targets the dorsal root entry zones innervating lumbosacral segments to reproduce the natural spatiotemporal activation of the lumbosacral spinal cord during walking. We first developed this neuroprosthesis in a non-human primate model that replicates locomotor deficits due to PD. This neuroprosthesis not only alleviated locomotor deficits but also restored skilled walking in this model. We then implanted the neuroprosthesis in a 62-year-old male with a 30-year history of PD who presented with severe gait impairments and frequent falls that were medically refractory to currently available therapies. We found that the neuroprosthesis interacted synergistically with deep brain stimulation of the subthalamic nucleus and dopaminergic replacement therapies to alleviate asymmetry and promote longer steps, improve balance and reduce freezing of gait. This neuroprosthesis opens new perspectives to reduce the severity of locomotor deficits in people with PD.

Robust and adaptive terrain classification and gait event detection system

Real-time gait event detection (GED) system can be utilized for gait analysis and tracking fitness activities. GED for various types of terrains (e.g., stair-walk, uneven surfaces, etc.) is still an open research problem. This study presents an inertial sensor-based approach for real-time GED system that works for diverse terrains in an uncontrolled environment. The GED system classifies three types of terrains, i.e., flat-walk, stair-ascend and stair-descend, with an average classification accuracy of 99%. It also accurately detects various gait events, including, toe-strike, heel-rise, toe-off, and heel-strike. It is computationally efficient, implemented on a low-cost microcontroller, works in real-time and can be used in portable rehabilitation devices for use in dynamic environments.

Assessment of reliability and validity of a handheld surface spine scanner for measuring trunk rotation in adolescent idiopathic scoliosis

PURPOSE

To assess the reliability and validity of a handheld scanner (SpineScan3D) for trunk rotation measurement in adolescent idiopathic scoliosis (AIS) subjects, as compared with Scoliometer.

METHODS

This was a cross-sectional study with AIS subjects recruited. Biplanar spine radiographs were performed using an EOS imaging system with coronal Cobb angle (CCA) determined. The angle of trunk rotation (ATR) was measured using Scoliometer. SpineScan3D was employed to assess the axial rotation of subjects' back at forward bending, recorded as surface tilt angle (STA). Intra- and inter-examiner repeats were conducted to evaluate the reliability of SpineScan3D.

RESULTS

97 AIS patients were recruited. Intra- and inter-examiner reliability of STA measures were good to excellent in major thoracic and lumbar curves (p < 0.001). A strong correlation was found between STA and ATR measures in both curve types (p < 0.001) with a standard error of the ATR estimate of between 1 and 2 degrees from linear regression models (R squared: 0.8-0.9, p < 0.001). A similar correlation with CCA was found for STA and ATR measures (r: 0.5-0.6, p < 0.002), which also demonstrated a similar sensitivity (72%-74%) and specificity (62%-77%) for diagnosing moderate to severe curves.

CONCLUSION

SpineScan3D is a handheld surface scanner with a potential of wide applications in subjects with AIS. The current study indicated that SpineScan3D is reliable and valid for measuring trunk rotation in AIS subjects, comparable to Scoliometer. Further studies are planned to investigate its measurements in coronal and sagittal planes and the potential of this device as a screening and monitoring tool.

TRIAL REGISTRATION NUMBER (DATE OF REGISTRATION)

HKUCTR-2288 (06 Dec 2017).

LEVEL OF EVIDENCE

Level III.

Foot characteristics of the daily-life gait in postmenopausal females with distal radius fractures: a cross-sectional study

BACKGROUND

Gait decline in older adults is related to falling risk, some of which contribute to injurious falls requiring medical attention or restriction of activity of daily living. Among injurious falls, distal radius fracture (DRF) is a common initial fragility fracture associated with the subsequent fracture risk in postmenopausal females. The recent invention of an inertial measurement unit (IMU) facilitates the assessment of free-living gait; however, little is known about the daily gait characteristics related to the risk of subsequent fractures. We hypothesized that females with DRF might have early changes in foot kinematics in daily gait. The aim of this study was to evaluate the daily-life gait characteristics related to the risk of falls and fracture.

METHODS

In this cross-sectional study, we recruited 27 postmenopausal females with DRF as their first fragility fracture and 28 age-matched females without a history of fragility fractures. The participants underwent daily gait assessments for several weeks using in-shoe IMU sensors. Eight gait parameters and each coefficient of variance were calculated. Some physical tests, such as hand grip strength and Timed Up and Go tests, were performed to check the baseline functional ability.

RESULTS

The fracture group showed lower foot angles of dorsiflexion and plantarflexion in the swing phase. The receiver operating characteristic curve analyses revealed that a total foot movement angle (TFMA) < 99.0 degrees was the risk of subsequent fracture.

CONCLUSIONS

We extracted the daily-life gait characteristics of patients with DRF using in-shoe IMU sensors. A lower foot angle in the swing phase, TFMA, may be associated with the risk of subsequent fractures, which may be effective in evaluating future fracture risk. Further studies to predict and prevent subsequent fractures from daily-life gait are warranted.

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

Individualized Learning-Based Ground Reaction Force Estimation in People Post-Stroke Using Pressure Insoles

Stroke is a leading cause of gait disability that leads to a loss of independence and overall quality of life. The field of clinical biomechanics aims to study how best to provide rehabilitation given an individual's impairments. However, there remains a disconnect between assessment tools used in biomechanical analysis and in clinics. In particular, 3-dimensional ground reaction forces (3D GRFs) are used to quantify key gait characteristics, but require lab-based equipment, such as force plates. Recent efforts have shown that wearable sensors, such as pressure insoles, can estimate GRFs in real-world environments. However, there is limited understanding of how these methods perform in people post-stroke, where gait is highly heterogeneous. Here, we evaluate three subject-specific machine learning approaches to estimate 3D GRFs with pressure insoles in people post-stroke across varying speeds. We find that a Convolutional Neural Network-based approach achieves the lowest estimation errors of 0.75 ± 0.24, 1.13 ± 0.54, and 4.79 ± 3.04 % bodyweight for the medio-lateral, antero-posterior, and vertical GRF components, respectively. Estimated force components were additionally strongly correlated with the ground truth measurements ( ). Finally, we show high estimation accuracy for three clinically relevant point metrics on the paretic limb. These results suggest the potential for an individualized machine learning approach to translate to real-world clinical applications.

Validity and reliability of a commercial wearable sensor system for measuring spatiotemporal gait parameters in a post-stroke population: the effects of walking speed and asymmetry

Objective.Commercial wearable sensor systems are a promising alternative to costly laboratory equipment for clinical gait evaluation, but their accuracy for individuals with gait impairments is not well established. Therefore, we investigated the validity and reliability of the APDM Opal wearable sensor system to measure spatiotemporal gait parameters for healthy controls and individuals with chronic stroke.Approach.Participants completed the 10 m walk test over an instrumented mat three times in different speed conditions. We compared performance of Opal sensors to the mat across different walking speeds and levels of step length asymmetry in the two populations.Main results. Gait speed and stride length measures achieved excellent reliability, though they were systematically underestimated by 0.11 m s-1and 0.12 m, respectively. The stride and step time measures also achieved excellent reliability, with no significant errors (median absolute percentage error <6.00%,p> 0.05). Gait phase duration measures achieved moderate-to-excellent reliability, with relative errors ranging from 4.13%-21.59%. Across gait parameters, the relative error decreased by 0.57%-9.66% when walking faster than 1.30 m s-1; similar reductions occurred for step length symmetry indices lower than 0.10.Significance. This study supports the general use of Opal wearable sensors to obtain quantitative measures of post-stroke gait impairment. These measures should be interpreted cautiously for individuals with moderate-severe asymmetry or walking speeds slower than 0.80 m s-1.

Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit

Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson's disease. We previously designed a rehabilitation assist device that can detect and classify a user's gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 ± 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation -0.9996, p < 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p < 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications.

Stepping Beyond Counts in Recovery of Total Hip Arthroplasty: A Prospective Study on Passively Collected Gait Metrics

Gait quality parameters have been used to measure recovery from total hip arthroplasty (THA) but are time-intensive and previously could only be performed in a lab. Smartphone sensor data and algorithmic advances presently allow for the passive collection of qualitative gait metrics. The purpose of this prospective study was to observe the recovery of physical function following THA by assessing passively collected pre- and post-operative gait quality metrics. This was a multicenter, prospective cohort study. From six weeks pre-operative through to a minimum 24 weeks post-operative, 612 patients used a digital care management application that collected gait metrics. Average weekly walking speed, step length, timing asymmetry, and double limb support percentage pre- and post-operative values were compared with a paired-sample t-test. Recovery was defined as the post-operative week when the respective gait metric was no longer statistically inferior to the pre-operative value. To control for multiple comparison error, significance was set at p < 0.002. Walking speeds and step length were lowest, and timing asymmetry and double support percentage were greatest at week two post-post-operative (p < 0.001). Walking speed (1.00 ± 0.14 m/s, p = 0.04), step length (0.58 ± 0.06 m/s, p = 0.02), asymmetry (14.5 ± 19.4%, p = 0.046), and double support percentage (31.6 ± 1.5%, p = 0.0089) recovered at 9, 8, 7, and 10 weeks post-operative, respectively. Walking speed, step length, asymmetry, and double support all recovered beyond pre-operative values at 13, 17, 10, and 18 weeks, respectively (p < 0.002). Functional recovery following THA can be measured via passively collected gait quality metrics using a digital care management platform. The data suggest that metrics of gait quality are most negatively affected two weeks post-operative; recovery to pre-operative levels occurs at approximately 10 weeks following primary THA, and follows a slower trajectory compared to previously reported step count recovery trajectories.

Stepping beyond Counts in Recovery of Total Knee Arthroplasty: A Prospective Study on Passively Collected Gait Metrics

Advances in algorithms developed from sensor-based technology data allow for the passive collection of qualitative gait metrics beyond step counts. The purpose of this study was to evaluate pre- and post-operative gait quality data to assess recovery following primary total knee arthroplasty. This was a multicenter, prospective cohort study. From 6 weeks pre-operative through to 24 weeks post-operative, 686 patients used a digital care management application to collect gait metrics. Average weekly walking speed, step length, timing asymmetry, and double limb support percentage pre- and post-operative values were compared with a paired-samples t-test. Recovery was operationally defined as when the respective weekly average gait metric was no longer statistically different than pre-operative. Walking speed and step length were lowest, and timing asymmetry and double support percentage were greatest at week two post-operative (p < 0.0001). Walking speed recovered at 21 weeks (1.00 m/s, p = 0.063) and double support percentage recovered at week 24 (32%, p = 0.089). Asymmetry percentage was recovered at 13 weeks (14.0%, p = 0.23) and was consistently superior to pre-operative values at week 19 (11.1% vs. 12.5%, p < 0.001). Step length did not recover during the 24-week period (0.60 m vs. 0.59 m, p = 0.004); however, this difference is not likely clinically relevant. The data suggests that gait quality metrics are most negatively affected two weeks post-operatively, recover within the first 24-weeks following TKA, and follow a slower trajectory compared to previously reported step count recoveries. The ability to capture new objective measures of recovery is evident. As more gait quality data is accrued, physicians may be able to use passively collected gait quality data to help direct post-operative recovery using sensor-based care pathways.

Real-Time Gait Event Detection with Adaptive Frequency Oscillators from a Single Head-Mounted IMU

Accurate real-time gait event detection is the basis for the development of new gait rehabilitation techniques, especially when utilizing robotics or virtual reality (VR). The recent emergence of affordable wearable technologies, especially inertial measurement units (IMUs), has brought forth various new methods and algorithms for gait analysis. In this paper, we highlight some advantages of using adaptive frequency oscillators (AFOs) over traditional gait event detection algorithms, implemented a real-time AFO-based algorithm that estimates the gait phase from a single head-mounted IMU, and validated our method on a group of healthy subjects. Gait event detection was accurate at two different walking speeds. The method was reliable for symmetric, but not asymmetric gait patterns. Our method could prove especially useful in VR applications since a head-mounted IMU is already an integral part of commercial VR products.

Waist-What? Can a single sensor positioned at the waist detect parameters of gait at a speed and distance reflective of older adults' activity?

One of the problems facing an ageing population is functional decline associated with reduced levels of physical activity (PA). Traditionally researcher or clinician input is necessary to capture parameters of gait or PA. Enabling older adults to monitor their activity independently could raise their awareness of their activitiy levels, promote self-care and potentially mitigate the risks associated with ageing. The ankle is accepted as the optimum position for sensor placement to capture parameters of gait however, the waist is proposed as a more accessible body-location for older adults. This study aimed to compare step-count measurements obtained from a single inertial sensor positioned at the ankle and at the waist to that of a criterion measure of step-count, and to compare gait parameters obtained from the sensors positioned at the two different body-locations. Step-count from the waist-mounted inertial sensor was compared with that from the ankle-mounted sensor, and with a criterion measure of direct observation in healthy young and healthy older adults during a three-minute treadmill walk test. Parameters of gait obtained from the sensors at both body-locations were also compared. Results indicated there was a strong positive correlation between step-count measured by both the ankle and waist sensors and the criterion measure, and between ankle and waist sensor step-count, mean step time and mean stride time (r = .802-1.0). There was a moderate correlation between the step time variability measures at the waist and ankle (r = .405). This study demonstrates that a single sensor positioned at the waist is an appropriate method for the capture of important measures of gait and physical activity among older adults.

Improving Accuracy of Real-Time Positioning and Path Tracking by Using an Error Compensation Algorithm against Walking Modes

Wide-range application scenarios, such as industrial, medical, rescue, etc., are in various demand for human spatial positioning technology. However, the existing MEMS-based sensor positioning methods have many problems, such as large accuracy errors, poor real-time performance and a single scene. We focused on improving the accuracy of IMU-based both feet localization and path tracing, and analyzed three traditional methods. In this paper, a planar spatial human positioning method based on high-resolution pressure insoles and IMU sensors was improved, and a real-time position compensation method for walking modes was proposed. To validate the improved method, we added two high-resolution pressure insoles to our self-developed motion capture system with a wireless sensor network (WSN) system consisting of 12 IMUs. By multi-sensor data fusion, we implemented dynamic recognition and automatic matching of compensation values for five walking modes, with real-time spatial-position calculation of the touchdown foot, enhancing the 3D accuracy of its practical positioning. Finally, we compared the proposed algorithm with three old methods by statistical analysis of multiple sets of experimental data. The experimental results show that this method has higher positioning accuracy in real-time indoor positioning and path-tracking tasks. The methodology can have more extensive and effective applications in the future.

Effects of Mobile Phone Use on Gait and Balance Control in Young Adults: A Hip-Ankle Strategy

BACKGROUND

This study aimed to derive the effects of walking while using a mobile phone on balance perturbation and joint movement among young adults.

METHODS

Sixteen healthy college students with no history of brain injury were tested. The participants were asked to walk under four different conditions: (1) walking, (2) browsing, (3) dialing, and (4) texting. Indicators related to balance control and lower limb kinematic/kinetic parameters were analyzed using the continuous relative phase and statistical nonparametric mapping methods.

RESULTS

Walking while using a mobile phone slowed participants' gait speed and reduced the cadence, stride length, and step length. The posterior tilt angle (0-14%, 57-99%), torque of the hip flexion (0-15%, 30-35%, 75-100%), and angle of the hip flexion (0-28%, 44-100%) decreased significantly. The activation of biceps femoris and gastrocnemius, hip stiffness, and ankle stiffness increased significantly. This impact on gait significantly differed among three dual tasks: texting > browsing > dialing.

CONCLUSION

Che overlap of walking and mobile phone use affects the gait significantly. The "hip-ankle strategy" may result in a "smooth" but slower gait, while this strategy was deliberate and tense. In addition, this adjustment also increases the stiffness of the hip and ankle, increasing the risk of fatigue. Findings regarding this effect may prove that even for young healthy adults, walking with mobile phone use induces measurable adjustment of the motor pattern. These results suggest the importance of simplifying the control of the movement.

Regional Time-Series Coding Network and Multi-View Image Generation Network for Short-Time Gait Recognition

Gait recognition is one of the important research directions of biometric authentication technology. However, in practical applications, the original gait data is often short, and a long and complete gait video is required for successful recognition. Also, the gait images from different views have a great influence on the recognition effect. To address the above problems, we designed a gait data generation network for expanding the cross-view image data required for gait recognition, which provides sufficient data input for feature extraction branching with gait silhouette as the criterion. In addition, we propose a gait motion feature extraction network based on regional time-series coding. By independently time-series coding the joint motion data within different regions of the body, and then combining the time-series data features of each region with secondary coding, we obtain the unique motion relationships between regions of the body. Finally, bilinear matrix decomposition pooling is used to fuse spatial silhouette features and motion time-series features to obtain complete gait recognition under shorter time-length video input. We use the OUMVLP-Pose and CASIA-B datasets to validate the silhouette image branching and motion time-series branching, respectively, and employ evaluation metrics such as IS entropy value and Rank-1 accuracy to demonstrate the effectiveness of our design network. Finally, we also collect gait-motion data in the real world and test them in a complete two-branch fusion network. The experimental results show that the network we designed can effectively extract the time-series features of human motion and achieve the expansion of multi-view gait data. The real-world tests also prove that our designed method has good results and feasibility in the problem of gait recognition with short-time video as input data.

Simplified Markerless Stride Detection Pipeline (sMaSDP) for Surface EMG Segmentation

To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject's Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson's Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies.

A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment

This paper presents a novel approach to creating a graphical summary of a subject's activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.

Evaluating Gait Impairment in Parkinson's Disease from Instrumented Insole and IMU Sensor Data

Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients' mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.

Do we still need to screen our patients?-Orthopaedic scoring based on motion tracking

PURPOSE

Orthopaedic scores are essential for the clinical assessment of movement disorders but require an experienced clinician for the manual scoring. Wearable systems are taking root in the medical field and offer a possibility for the convenient collection of motion tracking data. The purpose of this work is to demonstrate the feasibility of automated orthopaedic scorings based on motion tracking data using the Harris Hip Score and the Knee Society Score as examples.

METHODS

Seventy-eight patients received a clinical examination and an instrumental gait analysis after hip or knee arthroplasty. Seven hundred forty-four gait features were extracted from each patient's representative gait cycle. For each score, a hierarchical multiple regression analysis was conducted with a subsequent tenfold cross-validation. A data split of 70%/30% was applied for training/testing.

RESULTS

Both scores can be reproduced with excellent coefficients of determination R2 for training, testing and cross-validation by applying regression models based on four to six features from instrumental gait analysis as well as the patient-reported parameter 'pain' as an offset factor.

CONCLUSION

Computing established orthopaedic scores based on motion tracking data yields an automated evaluation of a joint function at the hip and knee which is suitable for direct clinical interpretation. In combination with novel technologies for wearable data collection, these computations can support healthcare staff with objective and telemedical applicable scorings for a large number of patients without the need for trained clinicians.

Physiological and Biomechanical Monitoring in American Football Players: A Scoping Review

American football is the sport with the highest rates of concussion injuries. Biomedical engineering applications may support athletes in monitoring their injuries, evaluating the effectiveness of their equipment, and leading industrial research in this sport. This literature review aims to report on the applications of biomedical engineering research in American football, highlighting the main trends and gaps. The review followed the PRISMA guidelines and gathered a total of 1629 records from PubMed (n = 368), Web of Science (n = 665), and Scopus (n = 596). The records were analyzed, tabulated, and clustered in topics. In total, 112 studies were selected and divided by topic in the biomechanics of concussion (n = 55), biomechanics of footwear (n = 6), biomechanics of sport-related movements (n = 6), the aerodynamics of football and catch (n = 3), injury prediction (n = 8), heat monitoring of physiological parameters (n = 8), and monitoring of the training load (n = 25). The safety of players has fueled most of the research that has led to innovations in helmet and footwear design, as well as improvements in the understanding and prevention of injuries and heat monitoring. The other important motivator for research is the improvement of performance, which has led to the monitoring of training loads and catches, and studies on the aerodynamics of football. The main gaps found in the literature were regarding the monitoring of internal loads and the innovation of shoulder pads.

Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness

BACKGROUND

In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes.

METHODS

Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy.

RESULTS

(1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke.

CONCLUSIONS

Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients' specific pathology outperform current control strategies.

Gait Analysis to Monitor Fracture Healing of the Lower Leg

Fracture healing is typically monitored by infrequent radiographs. Radiographs come at the cost of radiation exposure and reflect fracture healing with a time lag due to delayed fracture mineralization following increases in stiffness. Since union problems frequently occur after fractures, better and timelier methods to monitor the healing process are required. In this review, we provide an overview of the changes in gait parameters following lower leg fractures to investigate whether gait analysis can be used to monitor fracture healing. Studies assessing gait after lower leg fractures that were treated either surgically or conservatively were included. Spatiotemporal gait parameters, kinematics, kinetics, and pedography showed improvements in the gait pattern throughout the healing process of lower leg fractures. Especially gait speed and asymmetry measures have a high potential to monitor fracture healing. Pedographic measurements showed differences in gait between patients with and without union. No literature was available for other gait measures, but it is expected that further parameters reflect progress in bone healing. In conclusion, gait analysis seems to be a valuable tool for monitoring the healing process and predicting the occurrence of non-union of lower leg fractures.

The effect of mobile application-based rehabilitation in patients with Parkinson's disease: A systematic review and meta-analysis

OBJECTIVE

Mobile app-based telerehabilitation is practical and cost-effective in neurological rehabilitation. The present systematic review aimed to investigate the effectiveness of mobile application-based rehabilitation in patients with Parkinson's Disease.

METHODS

Literature was searched via databases of "Web of Science (WoS), PubMed, Cochrane, Scopus and ScienceDirect". Physiotherapy Evidence Database (PEDro) and Revised Cochrane risk-of-bias tool for randomized trials (RoB2) were used to evaluate the quality analysis and risk of bias evaluation. Both narrative and quantitative synthesis were carried out.

RESULTS

A total of 2175 articles were screened (WoS=41, PubMed=42, Cochrane=84, Scopus=114, ScienceDirect=1894). A total of 5 studies were included in the systematic review following the screening and eligibility procedures. Two studies were enrolled in meta-analysis regarding the data homogeneity. PEDro scores of the trials ranged from 4 to 7 (median:6), indicating good quality. All studies were in the "some concerns" category. The mobile application-based intervention yielded better results on quality of life and patient adherence in two studies. Application-based rehabilitation was not superior to standard treatment on MiniBESTest (ES:0.15, 95 % CI: -0.33 to 0.26) and UPDRS III (ES:0.86, 95 % CI: -0.94 to 2.46) scores.

CONCLUSION

Mobile application-based rehabilitation is not superior to standard treatments in balance and disease severity. However, mobile technologies could be preferred to increase patient adherence and quality of life. The limited study and the low number of cases in the review may reduce the level of evidence for the results.

Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor Signals

Wearable sensors allow for the objective analysis of gait and motion both in and outside the clinical setting. However, it remains a challenge to apply such systems to highly diverse patient populations, including individuals with lower-limb amputations (LLA) that present with unique gait deviations and rehabilitation goals. This paper presents the development of a novel method using continuous gyroscope data from a single inertial sensor for person-specific classification of gait changes from a physiotherapist-led gait training session. Gyroscope data at the thigh were collected using a wearable gait analysis system for five LLA before, during, and after completing a gait training session. Data from able-bodied participants receiving no intervention were also collected. Models using dynamic time warping (DTW) and Euclidean distance in combination with the nearest neighbor classifier were applied to the gyroscope data to classify the pre- and post-training gait. The model achieved an accuracy of 98.65% ± 0.69 (Euclidean) and 98.98% ± 0.83 (DTW) on pre-training and 95.45% ± 6.20 (Euclidean) and 94.18% ± 5.77 (DTW) on post-training data across the participants whose gait changed significantly during their session. This study provides preliminary evidence that continuous angular velocity data from a single gyroscope could be used to assess changes in amputee gait. This supports future research and the development of wearable gait analysis and feedback systems that are adaptable to a broad range of mobility impairments.

Impact of Gait Events Identification through Wearable Inertial Sensors on Clinical Gait Analysis of Children with Idiopathic Toe Walking

Idiopathic toe walking (ITW) is a gait deviation characterized by forefoot contact with the ground and excessive ankle plantarflexion over the entire gait cycle observed in otherwise-typical developing children. The clinical evaluation of ITW is usually performed using optoelectronic systems analyzing the sagittal component of ankle kinematics and kinetics. However, in standardized laboratory contexts, these children can adopt a typical walking pattern instead of a toe walk, thus hindering the laboratory-based clinical evaluation. With these premises, measuring gait in a more ecological environment may be crucial in this population. As a first step towards adopting wearable clinical protocols embedding magneto-inertial sensors and pressure insoles, this study analyzed the performance of three algorithms for gait events identification based on shank and/or foot sensors. Foot strike and foot off were estimated from gait measurements taken from children with ITW walking barefoot and while wearing a foot orthosis. Although no single algorithm stands out as best from all perspectives, preferable algorithms were devised for event identification, temporal parameters estimate and heel and forefoot rocker identification, depending on the barefoot/shoed condition. Errors more often led to an erroneous characterization of the heel rocker, especially in shoed condition. The ITW gait specificity may cause errors in the identification of the foot strike which, in turn, influences the characterization of the heel rocker and, therefore, of the pathologic ITW behavior.

Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors

Gait analysis may serve various purposes related to health care, such as the estimation of elderly people's risk of falling. This paper is devoted to gait analysis based on data from depth sensors which are suitable for use both at healthcare facilities and in monitoring systems dedicated to household environments. This paper is focused on the comparison of three methods for spatiotemporal gait analysis based on data from depth sensors, involving the analysis of the movement trajectories of the knees, feet, and centre of mass. The accuracy of the results obtained using those methods was assessed for different depth sensors' viewing angles and different types of subject clothing. Data were collected using a Kinect v2 device. Five people took part in the experiments. Data from a Zebris FDM platform were used as a reference. The obtained results indicate that the viewing angle and the subject's clothing affect the uncertainty of the estimates of spatiotemporal gait parameters, and that the method based on the trajectories of the feet yields the most information, while the method based on the trajectory of the centre of mass is the most robust.

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation

We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.

Accuracy of the fully integrated Insole3's estimates of spatiotemporal parameters during walking

This study investigated the accuracy of the Insole3 wireless shoe device in estimating several clinically useful spatiotemporal parameters (STPs). Eleven subjects walked at slow (0.8-1.0 m/s) and moderate-paced (1.2-1.4 m/s) speeds. Data were simultaneously recorded using the Insole3 and an industry-standard, three-dimensional motion capture (MOCAP) system. An error analysis compared the resulting STP data from the two systems. The mean bias error (MBE) was generally lower for temporal variables, and somewhat higher, but acceptable, for spatial variables. The MBE for temporally-related cadence and cycle time were the lowest (less than ±0.45%), with 100% (110/110) of slow-paced walking trial values and 99.1% (109/110) of moderate-paced walking trial values within 5% of the MOCAP estimates. The MBE was highest for speed (3.23-4.91%) and stride length (3.68-4.63%), with between 52.7 and 69.1% of trial values falling within the 5% error range. Stance time and swing time ranged between -0.98 and 4.38% error for both walking conditions. The results of this study suggest that the Insole3 is a potential alternative to MOCAP for estimating several STPs, namely cadence, stance time, and cycle time, particularly for use outside of the laboratory setting.

A 3D-Printed Capacitive Smart Insole for Plantar Pressure Monitoring

Gait analysis refers to the systematic study of human locomotion and finds numerous applications in the fields of clinical monitoring, rehabilitation, sports science and robotics. Wearable sensors for real-time gait monitoring have emerged as an attractive alternative to the traditional clinical-based techniques, owing to their low cost and portability. In addition, 3D printing technology has recently drawn increased interest for the manufacturing of sensors, considering the advantages of diminished fabrication cost and time. In this study, we report the development of a 3D-printed capacitive smart insole for the measurement of plantar pressure. Initially, a novel 3D-printed capacitive pressure sensor was fabricated and its sensing performance was evaluated. The sensor exhibited a sensitivity of 1.19 MPa−1, a wide working pressure range (<872.4 kPa), excellent stability and durability (at least 2.280 cycles), great linearity (R2=0.993), fast response/recovery time (142−160 ms), low hysteresis (DH<10%) and the ability to support a broad spectrum of gait speeds (30−70 steps/min). Subsequently, 16 pressure sensors were integrated into a 3D-printed smart insole that was successfully applied for dynamic plantar pressure mapping and proven able to distinguish the various gait phases. We consider that the smart insole presented here is a simple, easy to manufacture and cost-effective solution with the potential for real-world applications.

Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection

BACKGROUND

Vestibular deficits can impair an individual's ability to maintain postural and/or gaze stability. Characterizing gait abnormalities among individuals affected by vestibular deficits could help identify patients at high risk of falling and inform rehabilitation programs. Commonly used gait assessment tools rely on simple measures such as timing and visual observations of path deviations by clinicians. These simple measures may not capture subtle changes in gait kinematics. Therefore, we investigated the use of wearable inertial measurement units (IMUs) and machine learning (ML) approaches to automatically discriminate between gait patterns of individuals with vestibular deficits and age-matched controls. The goal of this study was to examine the effects of IMU placement and gait task selection on the performance of automatic vestibular gait classifiers.

METHODS

Thirty study participants (15 with vestibular deficits and 15 age-matched controls) participated in a single-session gait study during which they performed seven gait tasks while donning a full-body set of IMUs. Classification performance was reported in terms of area under the receiver operating characteristic curve (AUROC) scores for Random Forest models trained on data from each IMU placement for each gait task.

RESULTS

Several models were able to classify vestibular gait better than random (AUROC > 0.5), but their performance varied according to IMU placement and gait task selection. Results indicated that a single IMU placed on the left arm when walking with eyes closed resulted in the highest AUROC score for a single IMU (AUROC = 0.88 [0.84, 0.89]). Feature permutation results indicated that participants with vestibular deficits reduced their arm swing compared to age-matched controls while they walked with eyes closed.

CONCLUSIONS

These findings highlighted differences in upper extremity kinematics during walking with eyes closed that were characteristic of vestibular deficits and showed evidence of the discriminative ability of IMU-based automated screening for vestibular deficits. Further research should explore the mechanisms driving arm swing differences in the vestibular population.

Using Sensor Technology to Measure Gait Capacity and Gait Performance in Rehabilitation Inpatients with Neurological Disorders

The aim of this study was to objectively assess and compare gait capacity and gait performance in rehabilitation inpatients with stroke or incomplete spinal cord injury (iSCI) using inertial measurement units (IMUs). We investigated how gait capacity (what someone can do) is related to gait performance (what someone does). Twenty-two inpatients (11 strokes, 11 iSCI) wore ankle positioned IMUs during the daytime to assess gait. Participants completed two circuits to assess gait capacity. These were videotaped to certify the validity of the IMU algorithm. Regression analyses were used to investigate if gait capacity was associated with gait performance (i.e., walking activity and spontaneous gait characteristics beyond therapy time). The ankle positioned IMUs validly assessed the number of steps, walking time, gait speed, and stride length (r ≥ 0.81). The walking activity was strongly (r ≥ 0.76) related to capacity-based gait speed. Maximum spontaneous gait speed and stride length were similar to gait capacity. However, the average spontaneous gait speed was half the capacity-based gait speed. Gait capacity can validly be assessed using IMUs and is strongly related to gait performance in rehabilitation inpatients with neurological disorders. Measuring gait performance with IMUs provides valuable additional information about walking activity and spontaneous gait characteristics to inform about functional recovery.

Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection

Accelerometers are low-cost measurement devices that can readily be used outside the lab. However, determining isolated gait events from accelerometer signals, especially foot-off events during running, is an open problem. We outline a two-step approach where machine learning serves to predict vertical ground reaction forces from accelerometer signals, followed by force-based event detection. We collected shank accelerometer signals and ground reaction forces from 21 adults during comfortable walking and running on an instrumented treadmill. We trained one common reservoir computer using segmented data using both walking and running data. Despite being trained on just a small number of strides, this reservoir computer predicted vertical ground reaction forces in continuous gait with high quality. The subsequent foot contact and foot off event detection proved highly accurate when compared to the gold standard based on co-registered ground reaction forces. Our proof-of-concept illustrates the capacity of combining accelerometry with machine learning for detecting isolated gait events irrespective of mode of locomotion.

Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait

Walking on natural terrain while performing a dual-task, such as typing on a smartphone is a common behavior. Since dual-tasking and terrain change gait characteristics, it is of interest to understand how altered gait is reflected by changes in gait-associated neural signatures. A study was performed with 64-channel electroencephalography (EEG) of healthy volunteers, which was recorded while they walked over uneven and even terrain outdoors with and without performing a concurrent task (self-paced button pressing with both thumbs). Data from n = 19 participants (M = 24 years, 13 females) were analyzed regarding gait-phase related power modulations (GPM) and gait performance (stride time and stride time-variability). GPMs changed significantly with terrain, but not with the task. Descriptively, a greater beta power decrease following right-heel strikes was observed on uneven compared to even terrain. No evidence of an interaction was observed. Beta band power reduction following the initial contact of the right foot was more pronounced on uneven than on even terrain. Stride times were longer on uneven compared to even terrain and during dual- compared to single-task gait, but no significant interaction was observed. Stride time variability increased on uneven terrain compared to even terrain but not during single- compared to dual-tasking. The results reflect that as the terrain difficulty increases, the strides become slower and more irregular, whereas a secondary task slows stride duration only. Mobile EEG captures GPM differences linked to terrain changes, suggesting that the altered gait control demands and associated cortical processes can be identified. This and further studies may help to lay the foundation for protocols assessing the cognitive demand of natural gait on the motor system.