Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke

Accelerometer-based gait characteristics and their discrimination of gait independence in inpatients with subacute stroke

BACKGROUND

Gait analysis using inertial measurement devices can identify multifaceted gait disorders after a stroke. Although the usefulness of gait assessment using inertial measurement devices has been reported, its accuracy in discriminating gait independence in patients hospitalized for subacute stroke has not yet been validated.

RESEARCH QUESTION

Can trunk acceleration indices discriminate between dependent and independent walking in patients with subacute stroke?

METHODS

Thirty-five patients with subacute stroke (mean ± standard deviation, 75.5 ± 9.8 years, 19 males), who were able to understand instructions, had a premorbid modified Rankin scale <3, and were able to walk 16 m straight ahead under supervision were included. The stride regularity, harmonic ratio, and normalized root mean square of trunk accelerations were measured in three directions (mediolateral, vertical, and anterioposterior) during comfortable walking. The Functional Ambulation Categories were used as the dependent variable to classify the patients into two groups (dependent and independent walking groups), and each trunk acceleration index was used as the independent variable to calculate the area under the curve using receiver operating characteristic curves.

RESULTS

Twelve patients were in the dependent group and 23 were in the independent group. The normalized root mean square in both the mediolateral and vertical directions were excellent discriminators of walking independence, with an area under the curve greater than 0.8. The cutoff values (sensitivity/specificity) were 2.20 m2/s2 (0.783/0.833) and 2.82 m2/s2 (0.739/0.833), respectively.

SIGNIFICANCE

The magnitude of vertical and lateral acceleration during gait in patients with subacute stroke, has excellent accuracy in discriminating between dependent and independent gaits. The results of this study will be useful for inexperienced clinicians working with stroke patients presenting with gait disturbances to accurately determine gait independence based on objective data.

Validity of an android device for assessing mobility in people with chronic stroke and hemiparesis: a cross-sectional study

BACKGROUND

Incorporating instrument measurements into clinical assessments can improve the accuracy of results when assessing mobility related to activities of daily living. This can assist clinicians in making evidence-based decisions. In this context, kinematic measures are considered essential for the assessment of sensorimotor recovery after stroke. The aim of this study was to assess the validity of using an Android device to evaluate kinematic data during the performance of a standardized mobility test in people with chronic stroke and hemiparesis.

METHODS

This is a cross-sectional study including 36 individuals with chronic stroke and hemiparesis and 33 age-matched healthy subjects. A simple smartphone attached to the lumbar spine with an elastic band was used to measure participants' kinematics during a standardized mobility test by using the inertial sensor embedded in it. This test includes postural control, walking, turning and sitting down, and standing up. Differences between stroke and non-stroke participants in the kinematic parameters obtained after data sensor processing were studied, as well as in the total execution and reaction times. Also, the relationship between the kinematic parameters and the community ambulation ability, degree of disability and functional mobility of individuals with stroke was studied.

RESULTS

Compared to controls, participants with chronic stroke showed a larger medial-lateral displacement (p = 0.022) in bipedal stance, a higher medial-lateral range (p < 0.001) and a lower cranio-caudal range (p = 0.024) when walking, and lower turn-to-sit power (p = 0.001), turn-to-sit jerk (p = 0.026) and sit-to-stand jerk (p = 0.001) when assessing turn-to-sit-to-stand. Medial-lateral range and total execution time significantly correlated with all the clinical tests (p < 0.005), and resulted significantly different between independent and limited community ambulation patients (p = 0.042 and p = 0.006, respectively) as well as stroke participants with significant disability or slight/moderate disability (p = 0.024 and p = 0.041, respectively).

CONCLUSION

This study reports a valid, single, quick and easy-to-use test for assessing kinematic parameters in chronic stroke survivors by using a standardized mobility test with a smartphone. This measurement could provide valid clinical information on reaction time and kinematic parameters of postural control and gait, which can help in planning better intervention approaches.

Biochemical, biomechanical and imaging biomarkers of ischemic stroke: Time for integrative thinking

Stroke is one of the leading causes of adult disability affecting millions of people worldwide. Post-stroke cognitive and motor impairments diminish quality of life and functional independence. There is an increased risk of having a second stroke and developing secondary conditions with long-term social and economic impacts. With increasing number of stroke incidents, shortage of medical professionals and limited budgets, health services are struggling to provide a care that can break the vicious cycle of stroke. Effective post-stroke recovery hinges on holistic, integrative and personalized care starting from improved diagnosis and treatment in clinics to continuous rehabilitation and support in the community. To improve stroke care pathways, there have been growing efforts in discovering biomarkers that can provide valuable insights into the neural, physiological and biomechanical consequences of stroke and how patients respond to new interventions. In this review paper, we aim to summarize recent biomarker discovery research focusing on three modalities (brain imaging, blood sampling and gait assessments), look at some established and forthcoming biomarkers, and discuss their usefulness and complementarity within the context of comprehensive stroke care. We also emphasize the importance of biomarker guided personalized interventions to enhance stroke treatment and post-stroke recovery.

Smartphone-based gait and balance assessment in survivors of stroke: a systematic review

PURPOSE

Gait and balance impairments are associated with falls and reduced quality of life among survivors of stroke (SS). Effective methods to assess these impairments at-home and in-clinic can help reduce fall risks and improve functional outcomes. Smartphone technology may be able to evaluate these impairments. This review aims to summarize the validity, reliability, sensitivity, and specificity of smartphone applications for determining gait and balance disorders in SS.

METHOD

Database search through PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss was conducted to retrieve studies that explored the use of smartphone-based applications for assessing gait and balance disorders in SS. Two independent reviewers screened potential articles to determine eligibility for inclusion. Eligible studies were summarized for participant and study characteristics, validity, reliability, sensitivity, and specificity of smartphone assessments. Methodological quality assessment of studies was performed using the NIH Quality Assessment Tool.

RESULTS

Seven cross-sectional studies were included in the review. Quality assessment revealed all studies had low risk of bias. Three of the included studies examined the validity, four examined the reliability, and two examined the specificity and sensitivity of smartphone-based application assessments of gait and balance in SS. Studies revealed that smartphones were valid, reliable, specific, and sensitive. Six of the seven included studies intended their use for SS and one study for clinicians.

CONCLUSION

Preliminary evidence supports that smartphone-based gait and balance assessments are valid, reliable, sensitive, and specific in SS in laboratory settings. Future research is needed to test smartphone-based gait and balance assessments in home settings and determine optimal wear sites for assessments.IMPLICATIONS FOR REHABILITATIONSmartphone-based gait and balance assessments are feasible, valid and reliable for survivors of strokeThe findings may guide future research to standardize the use of smartphone to assess gait and balance in this population.The remote use of smartphone-based assessments to predict fall risk in survivors of stroke needs to be explored.

Watch your step: A pilot study of smartphone use effect on young females' gait performance while walking up and down stairs and escalators

The increasingly ubiquitous use of smartphones has made distracted walking common, not only on flat ground, but also on stairs. Available information regarding changes in gait performance while walking and using a smartphone in different environments is still lacking. We aimed to investigate the differences in gait behavior and subjective walking confidence while walking up and down stairs and escalators, with and without smartphone use. A field experiment involving 32 female adults was conducted at a subway station. Gait parameters collected included step frequency, acceleration root mean square, step variability, step regularity, and step symmetry. The results showed that walking task, walking environment, and walking direction significantly affected gait performance and walking confidence. Overall, playing games or texting while walking down escalators resulted in the lowest walking confidence and the largest gait performance decrement: slower step frequency; reduced root mean square; decreased step regularity and step symmetry; and increased step variability. Step frequency, step variability, and step regularity significantly correlated with walking confidence. Smartphone use while walking on stairs and escalators significantly affects gait behavior and might increase the risk of falls. Interventions and prevention are needed to increase safety education and hazard warnings for the general population.

Use of accelerometers in determining risk of falls in individuals post-stroke: A systematic review

OBJECTIVE

The aim of this systematic review was to determine if the diagnostic capabilities of wearable accelerometers enhanced, provided earlier detection, or improved fall risk assessment when evaluating individuals with chronic stroke.

DATA SOURCES

CINAHL and PubMed databases were searched for articles from 2015 to 2023 utilizing key terms.

REVIEW METHODS

A team of researchers reviewed articles for bias via the Quality in Prognostic Studies tool, and further analyzed the data to answer the research question.

RESULTS

Four studies were included in the systematic review. When utilizing an accelerometer, the vertical axis was most predictive of falls, followed by the medio-lateral axis and the anterior-posterior axis. L2-3 was the most common accelerometer placement for fall risk assessment, however no uniformity existed in the literature on placement, number of accelerometers, or type. It was determined that gait symmetry, the Timed Up and Go, Berg Balance Scale, and Longitudinal Aging Study Amsterdam best predicted falls risk.

CONCLUSION

Based on limited available evidence, clinicians should continue to perform a comprehensive examination and evaluation for fall risk, that includes the use of a combination of evidence-based outcome measures and gait characteristics to develop an individualized plan of care for individuals post-stroke. However, further research is necessary to determine the added value of accelerometers as well as type, applicability of data, and placement.

Multimodal video and IMU kinematic dataset on daily life activities using affordable devices

Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient gross motor tracking solutions for daily life activities recognition and kinematic analysis. The dataset includes 13 activities registered using a commodity camera and five inertial sensors. The video recordings were acquired in 54 subjects, of which 16 also had simultaneous recordings of inertial sensors. The novelty of dataset lies in: (i) the clinical relevance of the chosen movements, (ii) the combined utilization of affordable video and custom sensors, and (iii) the implementation of state-of-the-art tools for multimodal data processing of 3D body pose tracking and motion reconstruction in a musculoskeletal model from inertial data. The validation confirms that a minimally disturbing acquisition protocol, performed according to real-life conditions can provide a comprehensive picture of human joint angles during daily life activities.

Gait Monitoring and Analysis: A Mathematical Approach

Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson's, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson's disease by monitoring their gait due to wearable devices that can accurately detect a person's movements. In our study, about 50 people were involved in the trial (20 with Parkinson's disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the "gait quality" based on the measure of entropy obtained by applying the Fourier transform.

Comparative Analysis of Fall Risk Assessment Features in Community-Elderly and Stroke Survivors: Insights from Sensor-Based Data

Fall-risk assessment studies generally focus on identifying characteristics that affect postural balance in a specific group of subjects. However, falls affect a multitude of individuals. Among the groups with the most recurrent fallers are the community-dwelling elderly and stroke survivors. Thus, this study focuses on identifying a set of features that can explain fall risk for these two groups of subjects. Sixty-five community dwelling elderly (forty-nine female, sixteen male) and thirty-five stroke-survivors (twenty-two male, thirteen male) participated in our study. With the use of an inertial sensor, some features are extracted from the acceleration data of a Timed Up and Go (TUG) test performed by both groups of individuals. A short-form berg balance scale (SFBBS) score and the TUG test score were used for labeling the data. With the use of a 100-fold cross-validation approach, Relief-F and Extra Trees Classifier algorithms were used to extract sets of the top 5, 10, 15, 20, 25, and 30 features. Random Forest classifiers were trained for each set of features. The best models were selected, and the repeated features for each group of subjects were analyzed and discussed. The results show that only the stand duration was an important feature for the prediction of fall risk across all clinical tests and both groups of individuals.

A scoping review on recent trends in wearable sensors to analyze gait in people with stroke: From sensor placement to validation against gold-standard equipment

The purpose of the review is to evaluate wearable sensor placement, their impact and validation of wearable sensors on analyzing gait, primarily the postural instability in people with stroke. Databases, namely PubMed, Cochrane, SpringerLink, and IEEE Xplore were searched to identify related articles published since January 2005. The authors have selected the articles by considering patient characteristics, intervention details, and outcome measurements by following the priorly set inclusion and exclusion criteria. From a total of 1077 articles, 142 were included in this study and classified into functional fields, namely postural stability (PS) assessments, physical activity monitoring (PA), gait pattern classification (GPC), and foot drop correction (FDC). The review covers the types of wearable sensors, their placement, and their performance in terms of reliability and validity. When employing a single wearable sensor, the pelvis and foot were the most used locations for detecting gait asymmetry and kinetic parameters, respectively. Multiple Inertial Measurement Units placed at different body parts were effectively used to estimate postural stability and gait pattern. This review article has compared results of placement of sensors at different locations helping researchers and clinicians to identify the best possible placement for sensors to measure specific kinematic and kinetic parameters in persons with stroke.

Short-term effects of pedaling exercise combined with integrated volitional control electrical stimulation in an older patient hospitalized for subacute stroke: ABA single-case design

[Purpose] The purpose of this study was to examine effects on gait indices produced by a short-term intervention of pedaling combined with integrated volitional control electric stimulation in an older patient with stroke. [Participant and Methods] This study was a single-case ABA (A-control, B-treatment) design. Each phase lasted four consecutive days (12 days total). Ten minutes of pedaling were performed daily. In Phase B, pedaling was combined with integrated volitional control electric stimulator on the rectus femoris of the affected side. The primary outcomes were the coefficient of variation, a measure of stride time homogeneity during gait; and the root mean square, a measure of trunk sway in the triaxial direction (mediolateral, vertical, anteroposterior) during gait. Assessments were measured before the intervention (day 0) and after the end of each phase (days 4, 8, and 12). [Results] Changes from the previous coefficient of variation were +1.13%, -3.95%, and +0.82% in Phases A, B, and A', respectively, with the greatest improvement occurring after Phase B. The root mean square improved the most with -5.13 for mediolateral after Phase B, -3.33 for vertical, and -6.99 for anteroposterior after Phase A. [Conclusion] A short-term intervention consisting of pedaling combined with integrated volitional control electric stimulation may contribute to the improvement of gait abnormalities.

Relative and absolute reliability of gait variables obtained from gait analysis with trunk acceleration in community-dwelling individuals with chronic stroke: a pilot study

[Purpose] This pilot study aimed to investigate the relative and absolute reliability of variables obtained from an acceleration-based gait analysis conducted at comfortable and maximal gait speeds in individuals with chronic stroke. [Participants and Methods] This study included 25 community-dwelling individuals with chronic stroke. The participants wore triaxial accelerometers, while an observed walking trial was performed at comfortable and maximal speeds on two separate days 1 week apart. Relative reliability was evaluated using the intraclass correlation coefficient, and absolute reliability was evaluated using the Bland-Altman analysis, standard error of measurement, and minimal detectable change. [Results] The intraclass correlation coefficient of gait varied according to the acceleration-based gait analysis, ranging from 0.70 to 0.99. The Bland-Altman analysis revealed no systematic bias in both comfortable and maximal gait speed conditions. Most of the minimal detectable changes were smaller at maximal gait speed than at comfortable gait speed. [Conclusion] Acceleration-based gait analysis is a reliable method, particularly in maximal gait speed conditions. It may be used to assess the effect of rehabilitation interventions in individuals with chronic stroke.

Relationship between gait regularity and harmony, and gait speed at discharge in inpatients with subacute stroke

[Purpose] Accelerometry indices are a promising and simple method to quantify gait stability. However, the long-term relationship between gait stability and walking ability in patients with stroke has not been fully investigated. The purpose of this study was to longitudinally examine the relationship between gait regularity and harmony at admission and gait speed at discharge in inpatients with subacute mild stroke. [Participants and Methods] Sixteen patients with subacute stroke (median age, 69.5 years [1st-3rd interquartile range, 58.0-73.8 years]; 13 males) were enrolled in the study. A Spearman's rank correlation coefficient was calculated for step regularity, stride regularity, the harmonic ratio at admission, and the walking speed at discharge. We also calculated the partial rank order correlation, controlling for balance ability. [Results] The vertical step regularity, harmonic ratio, and anterior-posterior harmonic ratio were all positively correlated with the walking speed at discharge. Positive correlations with vertical step regularity and harmonic ratio were found in partial rank order correlations when controlled for balance ability. [Conclusion] Vertical step regularity and gait harmony had predictive validity for discharge gait speed in patients with subacute stroke.

Adaptive Inertial Sensor-Based Step Length Estimation Model

Pedestrian dead reckoning (PDR) using inertial sensors has paved the way for developing several approaches to step length estimation. In particular, emerging step length estimation models are readily available to be utilized on smartphones, yet they are seldom formulated considering the kinematics of the human body during walking in combination with measured step lengths. We present a new step length estimation model based on the acceleration magnitude and step frequency inputs herein. Spatial positions of anatomical landmarks on the human body during walking, tracked by an optical measurement system, were utilized in the derivation process. We evaluated the performance of the proposed model using our publicly available dataset that includes measurements collected for two types of walking modes, i.e., walking on a treadmill and rectangular-shaped test polygon. The proposed model achieved an overall mean absolute error (MAE) of 5.64 cm on the treadmill and an overall mean walked distance error of 4.55% on the test polygon, outperforming all the models selected for the comparison. The proposed model was also least affected by walking speed and is unaffected by smartphone orientation. Due to its promising results and favorable characteristics, it could present an appealing alternative for step length estimation in PDR-based approaches.

Walking orientation randomness metric (WORM) score: pilot study of a novel gait parameter to assess walking stability and discriminate fallers from non-fallers using wearable sensors

Background

Musculoskeletal disorders can contribute to injurious falls and incur significant societal and healthcare burdens. Identification of fallers from non-fallers through wearable-based gait analysis can facilitate timely intervention to assist mobility and prevent falls whilst improving care and attention for high fall-risk patients. In this study, we use wearable sensor-based gait analysis to introduce a novel variable to assess walking stability in fallers and non-fallers – the Walking Orientation Randomness Metric. The WORM score quantifies the stability, or ‘figure-of-eight’ motion of a subject’s trunk during walking as an indicator of a falls-predictive (pathological) gait.

Methods

WORM is calculated as the ‘figure-of-eight’ oscillation mapped out in the transverse-plane by the upper body’s centre-point during a walking bout. A sample of patients presenting to the Prince of Wales Hospital (Sydney, Australia) with a primary diagnosis of “falls for investigation” and age-matched healthy controls (non-fallers) from the community were recruited. Participants were fitted at the sternal angle with the wearable accelerometer, MetaMotionC (Mbientlab Inc., USA) and walked unobserved (at self-selected pace) for 5-50 m along an obstacle-free, carpeted hospital corridor.

Results

Participants comprised of 16 fallers (mean age: 70 + 17) and 16 non-fallers (mean age: 70 + 9) based on a recent fall(s) history. The (median) WORM score was 17-fold higher (p < 0.001) in fallers (3.64 cm) compared to non-fallers (0.21 cm). ROC curve analyses demonstrate WORM can discriminate fallers from non-fallers (AUC = 0.97). Diagnostic analyses (cut-off > 0.51 cm) show high sensitivity (88%) and specificity (94%).

Conclusion

In this pilot study we have introduced the WORM score, demonstrating its discriminative performance in a preliminary sample size of 16 fallers. WORM is a novel gait metric assessing walking stability as measured by truncal way during ambulation and shows promise for objective and clinical evaluation of fallers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05211-1.

Smartphone-based inertial sensors technology - Validation of a new application to measure spatiotemporal gait metrics

BACKGROUND

Smartphones are increasingly recognized as the future technology for clinical gait assessment.

RESEARCH QUESTION

To determine the concurrent validity of gait parameters obtained using the smartphone technology and application in a group of patients with musculoskeletal pathologies.

METHODS

Patients with knee, lower back, hip, or ankle pain were included in the study (n = 72). Spatiotemporal outcomes were derived from the walkway and the smartphone simultaneously. Pearson's correlations and limits of agreement (LoA) determined the association between the two methods.

RESULTS

Cadence and gait cycle time showed excellent correlation and agreement between the smartphone and the walkway (cadence: r = 0.997, LoA=1.4%, gait cycle time: r = 0.996, LoA = 1.6%). Gait speed, double-limb support and left and right step length demonstrated strong correlations and moderate agreement between methods (gait speed: r = 0.914, LoA=15.4%, left step length: r = 0.842, LoA = 17.0%, right step length: r = 0.800, LoA=16.4%). The left and right measures of single-limb support and stance percent showed a consistent 4% bias across instruments, yielding moderate correlation and very good agreement between the smartphone and the walkway (r = 0.532, LoA = 9% and r = 0.460, LoA=9.8% for left and right single-limb support; r = 0.463, LoA = 5.1% and r = 0.533, LoA = 4.4% for left and right stance).

SIGNIFICANCE

The examined application appears to be a valid tool for gait analysis, providing clinically significant metrics for the assessment of patients with musculoskeletal pathologies. However, additional studies should examine the technology amongst patients with severe gait abnormalities.

Increased Trailing Limb Angle is Associated with Regular and Stable Trunk Movements in Patients with Hemiplegia

OBJECTIVES

In post-stroke patients, shifts in the center of gravity may affect joint movement patterns of the paraplegic lower limb during walking. The impact of changes in ankle dorsiflexion angle and trailing limb angle due to slight weight-shifting is unknown. This study aimed to investigate the effect of the abovementioned parameters on gait characteristics measured by trunk acceleration.

MATERIALS AND METHODS

During walking, the ankle dorsiflexion angle and trailing limb angle were assessed using two-dimensional motion analysis. Shifts in the center of gravity were assessed to evaluate symmetry, regularity, and sway of trunk movements by calculating the harmonic ratio, autocorrelation coefficient, and root mean square using a wearable trunk accelerometer.

RESULTS

Ankle dorsiflexion angle showed a significant negative correlation with the root mean square of the anteroposterior axis (r = -0.460, p = 0.005). Trailing limb angle was significantly correlated with the autocorrelation coefficient of the vertical axis (r = 0.585, p < 0.001) and root mean square of the vertical (r = -0.579, p < 0.001), mediolateral (r = -0.474, p = 0.004), and anteroposterior axes (r = -0.548, p = 0.001). Trailing limb angle was a significant predictor (autocorrelation coefficient vertical axis, p = 0.001; root mean square vertical axis, p = 0.001; mediolateral axis, p = 0.007; anteroposterior axis, p = 0.001).

CONCLUSIONS

Trailing limb angle can indicate the acquisition of forward propulsion during walking; an increase in it may contribute to improvements of the regular vertical movement ability and stability of the center of gravity sway.

Walking through an aperture while penetrating from the paretic side improves safety managing the paretic side for individuals with stroke who had previous falls

BACKGROUND

Safety management of the paretic side of the body is critical for individuals with stroke. We previously reported that individuals with stroke who walk through an aperture while penetrating from the paretic side had fewer collisions with the frame of an aperture than did those penetrating from the non-paretic side. We observed spontaneous behavior of collision avoidance in our previous study; this study thus used penetration from the paretic and non-paretic sides as independent variables to confirm the usefulness of penetrating from the paretic side.

OBJECTIVE

This study aimed to (1) reconfirm whether walking through a narrow space while penetrating from the paretic side leads to reduced frequency of collision only for individuals with stroke with previous falls by manipulating the direction of penetration as independent variables and to (2) determine whether the behavioral or cognitive characteristics of passing through the aperture are observed in individuals with stroke who had previous falls.

METHODS

Individuals with stroke (12 with previous falls, 13 without) were required to walk through a narrow space while penetrating from the paretic and non-paretic sides. The collision rate and kinematic characteristics at the moment of crossing the aperture (body rotation angle, deviation of body's midpoint, and movement speed) were recorded as dependent variables. We also confirmed whether the participants expected collision after passing.

RESULTS

Individuals with stroke with previous falls were less likely to have a collision when penetrating from the paretic side. The stroke fall group was likely to experience more collisions because of deleterious changes in body rotation angle and movement speed in narrow apertures. Moreover, individuals with stroke have many unexpected collisions, but the decline in anticipatory ability was not unique to the stroke fall group.

CONCLUSIONS

Penetrating a narrow space from the paretic side improved safety management of the paretic side in patients with previous falls despite poor adjustment to narrow apertures. Penetrating a narrow space from the paretic side may make it easier to view and pay attention to the paretic side.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.

The Effects of Combining Transcranial Direct Current Stimulation and Gait Training with Functional Electrical Stimulation on Trunk Acceleration During Walking in Patients with Subacute Stroke

OBJECTIVES

This study aimed to investigate whether the combination of transcranial direct current stimulation (tDCS) and gait training with FES affected walking speed and trunk accelerometry-based gait characteristics in patients with subacute stroke, compared with FES or tDCS gait training only.

MATERIALS AND METHODS

Stroke patients (n = 34; female 15; mean age, 72.5 ± 11.2 years; mean days poststroke, 38.7) with resultant paresis in the lower extremity (mean Fugl-Meyer score, 25.5) were enrolled. Patients were randomly assigned to one of three groups: combined anodal tDCS and gait training with FES (tDCS+FES, n = 11), anodal tDCS with gait training (tDCS, n = 11), or combined sham tDCS and gait training with FES (FES, n = 12). Participants received the intervention for 20 minutes and a 40-minute conventional rehabilitative intervention daily for a week. Patients' walking ability was evaluated using walking speed, harmonic ratio (HR), autocorrelation coefficient (AC), and root mean square (RMS) along each axis using a wearable trunk accelerometer.

RESULTS

The tDCS+FES group had a significantly greater change in AC in the anteroposterior axis and mediolateral axis than the FES and tDCS groups and FES group, respectively. There were no significant effects on walking speed or other parameters (HR and RMS) among the groups.

CONCLUSIONS

The combination of anodal tDCS and gait training with FES improved the post-stroke patients' gait regularity than FES gait training intervention only. Thus, combined tDCS and FES gait training, as a novel intervention, could be an important therapeutic tool in improving walking performance.

Objective falls-risk prediction using wearable technologies amongst patients with and without neurogenic gait alterations: a narrative review of clinical feasibility

OBJECTIVES

The present narrative review aims to collate the literature regarding the current use of wearable gait measurement devices for falls-risk assessment in neurological and non-neurological populations. Thereby, this review seeks to determine the extent to which the aforementioned barriers inhibit clinical use.

BACKGROUND

Falls contribute a significant disease burden in most western countries, resulting in increased morbidity and mortality with substantial therapeutic costs. The recent development of gait analysis sensor technologies has enabled quantitative measurement of several gait features related to falls risk. However, three main barriers to implementation exist: accurately measuring gait-features associated with falls, differentiating between fallers and non-fallers using these gait features, and the accuracy of falls predictive algorithms developed using these gait measurements.

METHODS

Searches of Medline, PubMed, Embase and Scopus were screened to identify 46 articles relevant to the present study. Studies performing gait assessment using any wearable gait assessment device and analysing correlation with the occurrence of falls during a retrospective or prospective study period were included. Risk of Bias was assessed using the Centre for Evidence Based Medicine (CEBM) Criteria.

CONCLUSIONS

Falls prediction algorithms based entirely, or in-part, on gait data have shown comparable or greater success of predicting falls than existing stratification scoring systems such as the 10-meter walk test or timed-up-and-go. However, data is lacking regarding their accuracy in neurological patient populations. Inertial measurement units (IMU) have displayed competency in obtaining and interpreting gait metrics relevant to falls risk. They have the potential to enhance the accuracy and efficiency of falls risk assessment in inpatient and outpatient setting.

Reliability of the portable gait rhythmogram in post-stroke patients

BACKGROUND

Gait analysis, such as portable gait rhythmogram (PGR), provides objective information that helps in the quantitative evaluation of human locomotion.

OBJECTIVE

The purpose of this study was to assess the reliability of PGR in post-stroke patients.

METHODS

Two raters (A and B) examined 48 post-stroke patients. To assess intra-rater reliability, rater A tested subjects on three separate occasions (Days 1, 2, and 3). To assess inter-rater reliability, raters A and B independently tested participants on the same occasion (Day 3).

RESULTS

There was no significant systematic bias between test occasions or raters. Intraclass correlation coefficient values were 0.93-0.97 for intra-rater reliability at both the comfortable speed and maximum speed, and 0.97-0.98 (comfortable speed) and 0.97-0.99 (maximum speed) for inter-rater reliability. The standard error was 1.25-1.49 (comfortable speed) and 1.62-1.77 (maximum speed) for intra-rater investigation, and 1.04-1.32 (comfortable speed) and 0.91-1.26 (maximum speed) for inter-rater investigation. At the 90% confidence level, the minimum detectable change ranged from 2.9-4.1%, and the error of an individual's score at a given time point ranged from ±2.1-2.9%.

CONCLUSIONS

Based on this excellent reliability of the PGR in post-stroke patients, it can be recommended as a simple test of gait analysis in this population.

These legs were made for propulsion: advancing the diagnosis and treatment of post-stroke propulsion deficits

Advances in medical diagnosis and treatment have facilitated the emergence of precision medicine. In contrast, locomotor rehabilitation for individuals with acquired neuromotor injuries remains limited by the dearth of (i) diagnostic approaches that can identify the specific neuromuscular, biomechanical, and clinical deficits underlying impaired locomotion and (ii) evidence-based, targeted treatments. In particular, impaired propulsion by the paretic limb is a major contributor to walking-related disability after stroke; however, few interventions have been able to target deficits in propulsion effectively and in a manner that reduces walking disability. Indeed, the weakness and impaired control that is characteristic of post-stroke hemiparesis leads to heterogeneous deficits that impair paretic propulsion and contribute to a slow, metabolically-expensive, and unstable gait. Current rehabilitation paradigms emphasize the rapid attainment of walking independence, not the restoration of normal propulsion function. Although walking independence is an important goal for stroke survivors, independence achieved via compensatory strategies may prevent the recovery of propulsion needed for the fast, economical, and stable gait that is characteristic of healthy bipedal locomotion. We posit that post-stroke rehabilitation should aim to promote independent walking, in part, through the acquisition of enhanced propulsion. In this expert review, we present the biomechanical and functional consequences of post-stroke propulsion deficits, review advances in our understanding of the nature of post-stroke propulsion impairment, and discuss emerging diagnostic and treatment approaches that have the potential to facilitate new rehabilitation paradigms targeting propulsion restoration.

Upper body and ankle strategies compensate for reduced lateral stability at very slow walking speeds

At the typical walking speeds of healthy humans, step placement seems to be the primary strategy to maintain gait stability, with ankle torques and upper body momentum providing additional compensation. The average walking speeds of populations with an increased risk of falling, however, are much slower and may require differing control strategies. The purpose of this study was to analyse mediolateral gait stability and the contributions of the different control strategies at very slow walking speeds. We analysed an open dataset including kinematics and kinetics from eight healthy subjects walking at speeds from 0.1 to 0.6 m s^-1 as well as a self-selected speed. As gait speed slowed, we found that the margin of stability (MoS) decreased linearly. Increased lateral excursions of the extrapolated centre of mass, caused by increased lateral excursions of the trunk, were not compensated for by an equivalent increase in the lateral centre of pressure, leading to decreased MoS. Additionally, both the ankle eversion torque and hip abduction torque at the minimum MoS event increased at the same rate as gait speed slowed. These results suggest that the contributions of both the ankle and the upper body to stability are more crucial than stepping at slow speeds, which have important implications for populations with slow gait and limited motor function.

A Music-Based Digital Therapeutic: Proof-of-Concept Automation of a Progressive and Individualized Rhythm-Based Walking Training Program After Stroke

BACKGROUND

The rhythm of music can entrain neurons in motor cortex by way of direct connections between auditory and motor brain regions.

OBJECTIVE

We sought to automate an individualized and progressive music-based, walking rehabilitation program using real-time sensor data in combination with decision algorithms.

METHODS

A music-based digital therapeutic was developed to maintain high sound quality while modulating, in real-time, the tempo (ie, beats per minute, or bpm) of music based on a user's ability to entrain to the tempo and progress to faster walking cadences in-sync with the progression of the tempo. Eleven individuals with chronic hemiparesis completed one automated 30-minute training visit. Seven returned for 2 additional visits. Safety, feasibility, and rehabilitative potential (ie, changes in walking speed relative to clinically meaningful change scores) were evaluated.

RESULTS

A single, fully automated training visit resulted in increased usual (∆ 0.085 ± 0.027 m/s, P = .011) and fast (∆ 0.093 ± 0.032 m/s, P = .016) walking speeds. The 7 participants who completed additional training visits increased their usual walking speed by 0.12 ± 0.03 m/s after only 3 days of training. Changes in walking speed were highly related to changes in walking cadence (R² > 0.70). No trips or falls were noted during training, all users reported that the device helped them walk faster, and 70% indicated that they would use it most or all of the time at home.

CONCLUSIONS

In this proof-of-concept study, we show that a sensor-automated, progressive, and individualized rhythmic locomotor training program can be implemented safely and effectively to train walking speed after stroke. Music-based digital therapeutics have the potential to facilitate salient, community-based rehabilitation.

Enabling Older Adults' Health Self-Management through Self-Report and Visualization-A Systematic Literature Review

Aging is associated with a progressive decline in health, resulting in increased medical care and costs. Mobile technology may facilitate health self-management, thus increasing the quality of care and reducing costs. Although the development of technology offers opportunities in monitoring the health of older adults, it is not clear whether these technologies allow older adults to manage their health data themselves. This paper presents a review of the literature on mobile health technologies for older adults, focusing on whether these technologies enable the visualization of monitored data and the self-reporting of additional information by the older adults. The systematic search considered studies published between 2009 and 2019 in five online databases. We screened 609 articles and identified 95 that met our inclusion and exclusion criteria. Smartphones and tablets are the most frequently reported technology for older adults to enter additional data to the one that is monitored automatically. The recorded information is displayed on the monitoring device and screens of external devices such as computers. Future designs of mobile health technology should allow older users to enter additional information and visualize data; this could enable them to understand their own data as well as improve their experience with technology.

Consensus on Shared Measures of Mobility and Cognition: From the Canadian Consortium on Neurodegeneration in Aging (CCNA)

Background

A new paradigm is emerging in which mobility and cognitive impairments, previously studied, diagnosed, and managed separately in older adults, are in fact regulated by shared brain resources. Deterioration in these shared brain mechanisms by normal aging and neurodegeneration increases the risk of developing dementia, falls, and fractures. This new paradigm requires an integrated approach to measuring both domains. We aim to identify a complementary battery of existing tests of mobility and cognition in community-dwelling older adults that enable assessment of motor-cognitive interactions.

Methods

Experts on mobility and cognition in aging participated in a semistructured consensus based on the Delphi process. After performing a scoping review to select candidate tests, multiple rounds of consultations provided structured feedback on tests that captured shared characteristics of mobility and cognition. These tests needed to be sensitive to changes in both mobility and cognition, applicable across research studies and clinics, sensitive to interventions, feasible to perform in older adults, been previously validated, and have minimal ceiling/floor effects.

Results

From 17 tests appraised, 10 tests fulfilled prespecified criteria and were selected as part of the “Core-battery” of tests. The expert panel also recommended a “Minimum-battery” of tests that included gait speed, dual-task gait speed, the Montreal Cognitive Assessment and Trail Making Test A&B.

Conclusions

A standardized assessment battery that captures shared characteristics of mobility and cognition seen in aging and neurodegeneration may increase comparability across research studies, detection of subtle or common reversible factors, and accelerate research progress in dementia, falls, and aging-related disabilities.

The relationship between trunk acceleration parameters and kinematic characteristics during walking in patients with stroke

[Purpose] Limited literature has investigated the relationships between acceleration-based gait characteristics and kinematic information from motion analysis systems in gait analysis. The purpose of this study is to determine whether acceleration-based gait characteristics were associated with gait characteristics by motion analysis systems in patients with stroke. [Participants and Methods] Seventeen patients with stroke walked along a 10-m-long walkway at their comfortable speed. Trunk acceleration was measured with an accelerometer. Several reflective markers over bony landmarks on the lower extremities were used to capture movements. We evaluated the correlations of variables calculated between the trunk accelerometers and the motion analysis system. [Results] Walking speed was positively correlated with harmonic ratios along the anteroposterior axis and stride regularity along the vertical and anteroposterior axes. Harmonic ratios were associated with the stance phase percent on the unaffected side. Stride regularity was associated with the stance phase percent on both sides. Smaller interstride variability was associated with smaller peak ankle plantarflexion during both phases and greater peak ankle dorsiflexion during swing phase. Stride regularity is positively associated with maximal knee flexion during swing phase. [Conclusion] Relationships with spatiotemporal and joint kinematic parameters from the motion analysis system support the potential use of accelerometers.

Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances

Recent technologic advancements have enabled the creation of portable, low-cost, and unobtrusive sensors with tremendous potential to alter the clinical practice of rehabilitation. The application of wearable sensors to track movement has emerged as a promising paradigm to enhance the care provided to patients with neurologic or musculoskeletal conditions. These sensors enable quantification of motor behavior across disparate patient populations and emerging research shows their potential for identifying motor biomarkers, differentiating between restitution and compensation motor recovery mechanisms, remote monitoring, telerehabilitation, and robotics. Moreover, the big data recorded across these applications serve as a pathway to personalized and precision medicine. This article presents state-of-the-art and next-generation wearable movement sensors, ranging from inertial measurement units to soft sensors. An overview of clinical applications is presented across a wide spectrum of conditions that have potential to benefit from wearable sensors, including stroke, movement disorders, knee osteoarthritis, and running injuries. Complementary applications enabled by next-generation sensors that will enable point-of-care monitoring of neural activity and muscle dynamics during movement also are discussed.

IMPLEMENTATION OF A SMARTPHONE AS A WIRELESS ACCELEROMETER PLATFORM FOR QUANTIFYING HEMIPLEGIC GAIT DISPARITY IN A FUNCTIONALLY AUTONOMOUS CONTEXT

The utility of the smartphone, such as the iPhone, constitutes considerable potential for the advancement of the biomedical and healthcare industry. A notable feature of the iPhone is the capacity to combine the internal accelerometer sensor with a software application to enable the functionality of a wireless accelerometer platform. Preliminary research has demonstrated the iPhone’s ability to quantify features of healthy gait. The research applies a single iPhone mounted proximal to the lateral malleolus of the affected leg and subsequently the unaffected leg to ascertain quantified disparity of hemiplegic gait from an engineering proof of concept perspective. In order to maintain a consistent gait velocity, a constant velocity treadmill is incorporated into the research endeavor. Post-processing of the gait acceleration waveform is greatly facilitated through the use of a software automation program using Matlab that emphasizes on the rhythmicity of gait. Two gait parameters were obtained: stance-to-stance temporal disparity and stance-to-stance time-averaged acceleration, and demonstrated considerable accuracy, consistency, and reliability. As noted per the constant treadmill velocity, stance-to-stance temporal disparity for the affected and unaffected legs was established as not statistically significant. A statistical significance was determined for the stance-to-stance time-averaged acceleration regarding the affected and unaffected legs. The iPhone application represents a wireless accelerometer platform capable of identifying statistically significant and quantified disparity of hemiplegic gait features through automated post-processing in a functionally autonomous environment.

Management of a patient's gait abnormality using smartphone technology in-clinic for improved qualitative analysis: A case report

BACKGROUND AND PURPOSE

Qualitative analysis has its limitations as the speed of human movement often occurs more quickly than can be comprehended. Digital video allows for frame-by-frame analysis, and therefore likely more effective interventions for gait dysfunction. Although the use of digital video outside laboratory settings, just a decade ago, was challenging due to cost and time constraints, rapid use of smartphones and software applications has made this technology much more practical for clinical usage.

CASE DESCRIPTION

A 35-year-old man presented for evaluation with the chief complaint of knee pain 24 months status-post triple arthrodesis following a work-related crush injury. In-clinic qualitative gait analysis revealed gait dysfunction, which was augmented by using a standard IPhone® 3GS camera. After video capture, an IPhone® application (Speed Up TV®, https://itunes.apple.com/us/app/speeduptv/id386986953?mt=8 ) allowed for frame-by-frame analysis. Corrective techniques were employed using in-clinic equipment to develop and apply a temporary heel-to-toe rocker sole (HTRS) to the patient's shoe.

OUTCOMES

Post-intervention video revealed significantly improved gait efficiency with a decrease in pain. The patient was promptly fitted with a permanent HTRS orthosis. This intervention enabled the patient to successfully complete a work conditioning program and progress to job retraining.

DISCUSSION

Video allows for multiple views, which can be further enhanced by using applications for frame-by-frame analysis and zoom capabilities. This is especially useful for less experienced observers of human motion, as well as for establishing comparative signs prior to implementation of training and/or permanent devices.

Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis

OBJECTIVE

The number of mobile applications addressing health topics is increasing. Whether these apps underwent scientific evaluation is unclear. We comprehensively assessed papers investigating the diagnostic value of available diagnostic health applications using inbuilt smartphone sensors.

METHODS

Systematic Review-MEDLINE, Scopus, Web of Science inclusive Medical Informatics and Business Source Premier (by citation of reference) were searched from inception until 15 December 2016. Checking of reference lists of review articles and of included articles complemented electronic searches. We included all studies investigating a health application that used inbuilt sensors of a smartphone for diagnosis of disease. The methodological quality of 11 studies used in an exploratory meta-analysis was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the reporting quality with the 'STAndards for the Reporting of Diagnostic accuracy studies' (STARD) statement. Sensitivity and specificity of studies reporting two-by-two tables were calculated and summarised.

RESULTS

We screened 3296 references for eligibility. Eleven studies, most of them assessing melanoma screening apps, reported 17 two-by-two tables. Quality assessment revealed high risk of bias in all studies. Included papers studied 1048 subjects (758 with the target conditions and 290 healthy volunteers). Overall, the summary estimate for sensitivity was 0.82 (95 % CI 0.56 to 0.94) and 0.89 (95 %CI 0.70 to 0.97) for specificity.

CONCLUSIONS

The diagnostic evidence of available health apps on Apple's and Google's app stores is scarce. Consumers and healthcare professionals should be aware of this when using or recommending them.

PROSPERO REGISTRATION NUMBER

42016033049.

Multi-sensor assessment of dynamic balance during gait in patients with subacute stroke

The capacity to maintain upright balance by minimising upper body oscillations during walking, also referred to as gait stability, has been associated with a decreased risk of fall. Although it is well known that fall is a common complication after stroke, no study considered the role of both trunk and head when assessing gait stability in this population. The primary aim of this study was to propose a multi-sensor protocol to quantify gait stability in patients with subacute stroke using gait quality indices derived from pelvis, sternum, and head accelerations. Second, the association of these indices with the level of walking ability, with traditional clinical scale scores, and with fall events occurring within the six months after patients' dismissal was investigated. The accelerations corresponding to the three abovementioned body levels were measured using inertial sensors during a 10-Meter Walk Test performed by 45 inpatients and 25 control healthy subjects. A set of indices related to gait stability were estimated and clinical performance scales were administered to each patient. The amplitude of the accelerations, the way it is attenuated/amplified from lower to upper body levels, and the gait symmetry provide valuable information about subject-specific motor strategies, discriminate between different levels of walking ability, and correlate with clinical scales. In conclusion, the proposed multi-sensor protocol could represent a useful tool to quantify gait stability, support clinicians in the identification of patients potentially exposed to a high risk of falling, and assess the effectiveness of rehabilitation protocols in the clinical routine.

Intra-rater and inter-rater reliability of the portable gait rhythmogram in post-stroke patients

[Purpose] Gait analysis, such as portable gait rhythmogram (PGR) provides objective information that helps in the quantitative evaluation of human locomotion. The purpose of this study was to assess the reliability of PGR in post-stroke patients. [Subjects and Methods] Two raters (A and B) examined 44 post-stroke patients. To assess intra-rater reliability, rater A tested subjects on three separate occasions (Days 1, 2, and 3). To assess inter-rater reliability, raters A and B independently tested participants on the same occasion (Day 3). [Results] There was no significant systematic bias between test occasions or raters. Intraclass correlation coefficient values were 0.93−0.97 for intra-rater reliability at both the comfortable speed and maximum speed, and 0.97−0.98 (comfortable speed) and 0.87−0.99 (maximum speed) for inter-rater reliability. The standard error was 1.25−1.49 (comfortable speed) and 1.62−1.77 (maximum speed) for intra-rater investigation, and 1.04−1.32 (comfortable speed) and 0.91−1.26 (maximum speed) for inter-rater investigation. At the 90% confidence level, the minimum detectable change ranged from 2.9−4.1%, and the error of an individual’s score at a given time point ranged from ±2.1−2.9%. [Conclusion] Based on this excellent reliability of the PGR in post-stroke patients, it can be recommended as a simple test of gait analysis in this population.

Walking through Apertures in Individuals with Stroke

Objective

Walking through a narrow aperture requires unique postural configurations, i.e., body rotation in the yaw dimension. Stroke individuals may have difficulty performing the body rotations due to motor paralysis on one side of their body. The present study was therefore designed to investigate how successfully such individuals walk through apertures and how they perform body rotation behavior.

Method

Stroke fallers (n = 10), stroke non-fallers (n = 13), and healthy controls (n = 23) participated. In the main task, participants walked for 4 m and passed through apertures of various widths (0.9–1.3 times the participant’s shoulder width). Accidental contact with the frame of an aperture and kinematic characteristics at the moment of aperture crossing were measured. Participants also performed a perceptual judgment task to measure the accuracy of their perceived aperture passability.

Results and Discussion

Stroke fallers made frequent contacts on their paretic side; however, the contacts were not frequent when they penetrated apertures from their paretic side. Stroke fallers and non-fallers rotated their body with multiple steps, rather than a single step, to deal with their motor paralysis. Although the minimum passable width was greater for stroke fallers, the body rotation angle was comparable among groups. This suggests that frequent contact in stroke fallers was due to insufficient body rotation. The fact that there was no significant group difference in the perceived aperture passability suggested that contact occurred mainly due to locomotor factors rather than perceptual factors. Two possible explanations (availability of vision and/or attention) were provided as to why accidental contact on the paretic side did not occur frequently when stroke fallers penetrated the apertures from their paretic side.

Association of trunk control with mobility performance and accelerometry-based gait characteristics in hemiparetic patients with subacute stroke

Trunk control plays an important role in movement control and postural balance during functional activities. The purpose of this study was to investigate the association of trunk control early after stroke with mobility performance and quantitative gait characteristics derived from trunk accelerations. Fifteen patients with hemiparesis following stroke (median age, 61 years [range, 56-78 years]; median time since stroke, 9 days [range, 7-15 days]) participated in this cross-sectional observational study. Subjects were evaluated using the Trunk Impairment Scale (TIS), the short-form of the Berg Balance Scale (SF-BBS), an isometric knee extension strength test, the Timed Up and Go test (TUG), and a timed walking test. The linear acceleration of the lower trunk was recorded along the 3 axes during walking and quantified using the autocorrelation coefficient and harmonic ratio to assess the variability and smoothness of upper-body movement. The TIS total score had a significant correlation with TUG time. The coordination subscale score of the TIS was significantly correlated with TUG time, walking speed, and accelerometry variables in univariate analysis. The TIS coordination subscale score was significantly related to accelerometry variables in the partial correlation analysis adjusted for SF-BBS score and knee extension strength on the paretic and nonparetic side. These results indicate that trunk motor impairment after stroke is closely associated with poor mobility performance and trunk instability in gait. These findings support intensive rehabilitation treatment targeting trunk control to regain better mobility and stable gait in patients early after stroke.