Clinical assessment of gait and functional mobility in Italian healthy and cognitively impaired older persons using wearable inertial sensors

Effect of 4-weeks exercise program using wearable hip-assist robot (EX1) in older adults: one group pre- and post- test

BACKGROUND

Older adults have muscle loss and are at risk of falling. Recently, research in the healthcare field has been actively conducted, and Samsung Electronics has developed EX1, a hip joint assisted robot for exercise. This study aimed to verify the effect of a 4-week combined exercise program applying EX1 on older adults.

METHODS

This study design was an evaluator-blinded, pre- and post-test. A total of 21 older adults performed an exercise program consisting of walking and fitness wearing EX1 for 50 min per session, 3 days a week during the 4-week exercise period. For comparison before and after participating in the exercise program, the spatio-temporal parameters, pelvic movement were analyzed by G-Walk, functional outcomes were evaluated by TUG, muscle power were evaluated by RUSI, and waist-hip ratio were analyzed by Inbody. All data were analyzed before and after exercise using paired t-test, and the statistical significance level was set at 0.05.

RESULTS

In spatio-temporal parameters, stride length showed statistically significant improvements after exercise with EX1 (P < 0.01). Also, propulsion showed statistically significant improvements after exercise with EX1 (P < 0.01) Regarding changes of the gait posture, there was a statistically significant improvement in pelvic movement (P < 0.05). In the functional evaluation, the time required was statistically significantly reduced in the timed up and go test (P < 0.05).

CONCLUSION

These results demonstrate that a 4-week exercise program with EX1 was effective in improving the functional gait of the elderly. However, because the participants were 21, it is difficult to generalize the results.

TRIAL REGISTRATION

Clinical Research Information Service, KCT0007367. Registered 08/06/2022.

Accelerometry applications and methods to assess standing balance in older adults and mobility-limited patient populations: a narrative review

Accelerometers provide an opportunity to expand standing balance assessments outside of the laboratory. The purpose of this narrative review is to show that accelerometers are accurate, objective, and accessible tools for balance assessment. Accelerometry has been validated against current gold standard technology, such as optical motion capture systems and force plates. Many studies have been conducted to show how accelerometers can be useful for clinical examinations. Recent studies have begun to apply classification algorithms to accelerometry balance measures to discriminate populations at risk for falls. In addition to healthy older adults, accelerometry can monitor balance in patient populations such as Parkinson's disease, multiple sclerosis, and traumatic brain injury. The lack of software packages or easy-to-use applications have hindered the shift into the clinical space. Lack of consensus on outcome metrics has also slowed the clinical adoption of accelerometer-based balance assessments. Future studies should focus on metrics that are most helpful to evaluate balance in specific populations and protocols that are clinically efficacious.

Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices

Significant advances in sensor technology and virtual reality (VR) offer new possibilities for early and effective detection of mild cognitive impairment (MCI), and this wealth of data can improve the early detection and monitoring of patients. In this study, we proposed a non-invasive and effective MCI detection protocol based on electroencephalogram (EEG), speech, and digitized cognitive parameters. The EEG data, speech data, and digitized cognitive parameters of 86 participants (44 MCI patients and 42 healthy individuals) were monitored using a wearable EEG device and a VR device during the resting state and task (the VR-based language task we designed). Regarding the features selected under different modality combinations for all language tasks, we performed leave-one-out cross-validation for them using four different classifiers. We then compared the classification performance under multimodal data fusion using features from a single language task, features from all tasks, and using a weighted voting strategy, respectively. The experimental results showed that the collaborative screening of multimodal data yielded the highest classification performance compared to single-modal features. Among them, the SVM classifier using the RBF kernel obtained the best classification results with an accuracy of 87%. The overall classification performance was further improved using a weighted voting strategy with an accuracy of 89.8%, indicating that our proposed method can tap into the cognitive changes of MCI patients. The MCI detection scheme based on EEG, speech, and digital cognitive parameters proposed in this study provides a new direction and support for effective MCI detection, and suggests that VR and wearable devices will be a promising direction for easy-to-perform and effective MCI detection, offering new possibilities for the exploration of VR technology in the field of language cognition.

Influence of age on force and re-lengthening dynamics after tetanic stimulation withdrawal in the tibialis anterior muscle

PURPOSE

During alternate movements across a joint, the changeover from one direction of rotation to the opposite may be influenced by the delay and rate of tension reduction and the compliance to re-lengthening of the previously active muscle group. Given the aging process may affect the above-mentioned factors, this work aimed to compare the dynamics of both the ankle torque decline and muscle re-lengthening, mirrored by mechanomyogram (MMG), in the tibialis anterior because of its important role in gait.

METHODS

During the relaxation phase, after a supramaximal 35 Hz stimulation applied at the superficial motor point, in 20 young (Y) and 20 old (O) subjects, the torque (T) and MMG dynamics characteristics were measured.

RESULTS

The T and MMG analysis provided: (I) the beginning of the decay after cessation of stimulation (T: 22.51 ± 5.92 ms [Y] and 51.35 ± 15.21 ms [O]; MMG: 27.38 ± 6.93 ms [Y] and 61.41 ± 18.42 ms [O]); (II) the maximum rate of reduction (T: - 110.4 ± 45.56 Nm/s [Y] and - 52.72 ± 32.12 Nm/s [O]; MMG: - 24.47 ± 10.95 mm/s [Y] and - 13.76 ± 6.54 mm/s [O]); (III) the muscle compliance, measuring the MMG reduction of every 10% reduction of torque (bin 20-10%: 15.69 ± 7.5[Y] and 10.8 ± 3.3 [O]; bin 10-0%: 22.12 ± 10.3 [Y] and 17.58 ± 5.6 [O]).

CONCLUSION

Muscle relaxation results are different in Y and O and can be monitored by a non-invasive method measuring physiological variables of torque and re-lengthening dynamics at the end of the electromechanical coupling previously induced by the neuromuscular stimulation.

EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors

Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user's daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid's integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.

Minding your steps: a cross-sectional pilot study using foot-worn inertial sensors and dual-task gait analysis to assess the cognitive status of older adults with mobility limitations

BACKGROUND

Cognitive impairment is a critical aspect of our aging society. Yet, it receives inadequate intervention due to delayed or missed detection. Dual-task gait analysis is currently considered a solution to improve the early detection of cognitive impairment in clinical settings. Recently, our group proposed a new approach for the gait analysis resorting to inertial sensors placed on the shoes. This pilot study aimed to investigate the potential of this system to capture and differentiate gait performance in the presence of cognitive impairment based on single- and dual-task gait assessments.

METHODS

We analyzed demographic and medical data, cognitive tests scores, physical tests scores, and gait metrics acquired from 29 older adults with mobility limitations. Gait metrics were extracted using the newly developed gait analysis approach and recorded in single- and dual-task conditions. Participants were stratified into two groups based on their Montreal Cognitive Assessment (MoCA) global cognitive scores. Statistical analysis was performed to assess differences between groups, discrimination ability, and association of gait metrics with cognitive performance.

RESULTS

The addition of the cognitive task influenced gait performance of both groups, but the effect was higher in the group with cognitive impairment. Multiple dual-task costs, dual-task variability, and dual-task asymmetry metrics presented significant differences between groups. Also, several of these metrics provided acceptable discrimination ability and had a significant association with MoCA scores. The dual-task effect on gait speed explained the highest percentage of the variance in MoCA scores. None of the single-task gait metrics presented significant differences between groups.

CONCLUSIONS

Our preliminary results show that the newly developed gait analysis solution based on foot-worn inertial sensors is a pertinent tool to evaluate gait metrics affected by the cognitive status of older adults relying on single- and dual-task gait assessments. Further evaluation with a larger and more diverse group is required to establish system feasibility and reliability in clinical practice.

TRIAL REGISTRATION

ClinicalTrials.gov (identifier: NCT04587895).

Exercise with a wearable hip-assist robot improved physical function and walking efficiency in older adults

Wearable assistive robotics has emerged as a promising technology to supplement or replace motor functions and to retrain people recovering from an injury or living with reduced mobility. We developed delayed output feedback control for a wearable hip-assistive robot, the EX1, to provide gait assistance. Our purpose in this study was to investigate the effects of long-term exercise with EX1 on gait, physical function, and cardiopulmonary metabolic energy efficiency in elderly people. This study used parallel experimental (exercise with EX1) and control groups (exercise without EX1). A total of 60 community-dwelling elderly persons participated in 18 exercise intervention sessions during 6 weeks, and all participants were assessed at 5 time points: before exercise, after 9 exercise sessions, after 18 sessions, and 1 month and 3 months after the last session. The spatiotemporal gait parameters, kinematics, kinetics, and muscle strength of the trunk and lower extremities improved more after exercise with EX1 than in that without EX1. Furthermore, the effort of muscles over the trunk and lower extremities throughout the total gait cycle (100%) significantly decreased after exercise with EX1. The net metabolic energy costs during walking significantly improved, and functional assessment scores improved more in the experimental group than in the control group. Our findings provide evidence supporting the application of EX1 in physical activity and gait exercise is effective to improve age-related declines in gait, physical function, and cardiopulmonary metabolic efficiency among older adults.

The Reliability of the Timed Up and Go Test among Portuguese Elderly

Assessment of dynamic balance is typically completed through functional tests, such as the Timed Up and Go (TUG) test, which measures the time it takes for an individual to stand up from a chair, walk a set distance, turn around, and sit back down. This test has been validated in several countries. However, in the Portuguese population there is a gap on testing the reliability of this functional test in a sample of the elderly both living in the community or in nursing homes. Thus, this study aimed at examining the reliability of the TUG in a sample of Portuguese elderly. An Intraclass Correlation Coefficient (ICC) analysis was performed between the first time (T1) and the time score after 16 weeks (T2) in TUG test by 38 males and 79 females aged between 60 and 92 years. The results showed acceptable scores of ICC in community-dwelling and nursing home resident elderly in both moments. In addition, significant differences were found between these groups of older adults, showing that community-dwelling elderly show greater agility and balance capacity compared to those living in nursing homes. Thus, the TUG test can be applied in the Portuguese elderly in both community-dwelling and nursing home resident elderly.

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.

Using shoe-mounted inertial sensors and stepping exergames to assess the motor-cognitive status of older adults: A correlational study

Objective

Stepping exergames designed to stimulate physical and cognitive skills can provide important information concerning individuals’ performance. In this study, we investigated the potential of stepping and gameplay metrics to assess the motor-cognitive status of older adults.

Methods

Stepping and gameplay metrics were recorded in a longitudinal study involving 13 older adults with mobility limitations. Game parameters included games’ scores and reaction times. Stepping parameters included length, height, speed, and duration, measured by inertial sensors placed on the shoes while interacting with the exergames. Parameters measured on the first gameplay were correlated against standard cognitive and mobility assessments, including the Montreal Cognitive Assessment (MoCA), gait speed, and the Short Physical Performance Battery. Based on MoCA scores, patients were then stratified into two groups: cognitively impaired and healthy controls. The differences between the two groups were visually inspected, considering their within-game progression over the training period.

Results

Stepping and gameplay metrics had moderate-to-strong correlations with cognitive and mobility performance indicators: faster, longer, and higher steps were associated with better mobility scores; better cognitive games’ scores and reaction times, and longer and faster steps were associated with better cognitive performance. The preliminary visual analysis revealed that the group with cognitive impairment required more time to advance to the next difficulty level, also presenting slower reaction times and stepping speeds when compared to the healthy control group.

Conclusion

Stepping exergames may be useful for assessing the cognitive and motor status of older adults, potentially allowing assessments to be more frequent, affordable, and enjoyable. Further research is required to confirm results in the long term using a larger and more diverse sample.

Nurse-in-the-loop smart home detection of health events associated with diagnosed chronic conditions: A case-event series

Background

Telehealth and home-based care options significantly expanded during the SARS-CoV2 pandemic. Sophisticated, remote monitoring technologies now exist that support at-home care. Advances in the research of smart homes for health monitoring have shown these technologies are capable of recognizing and predicting health changes in near-real time. However, few nurses are familiar enough with this technology to use smart homes for optimizing patient care or expanding their reach into the home between healthcare touch points.

Objective

The objective of this work is to explore a partnership between nurses and smart homes for automated remote monitoring and assessing of patient health. We present a series of health event cases to demonstrate how this partnership may be harnessed to effectively detect and report on clinically relevant health events that can be automatically detected by smart homes.

Participants

25 participants with multiple chronic health conditions.

Methods

Ambient sensors were installed in the homes of 25 participants with multiple chronic health conditions. Motion, light, temperature, and door usage data were continuously collected from participants' homes. Descriptions of health events and participants' associated behaviors were captured via weekly nursing telehealth visits with study participants and used to analyze sensor data representing health events. Two cases of participants with congestive heart failure exacerbations, one case of urinary tract infection, two cases of bowel inflammation flares, and four cases of participants with sleep interruption were explored.

Results

For each case, clinically relevant health events aligned with changes from baseline in behavior data patterns derived from sensors installed in the participant's home. In some cases, the detected event was precipitated by additional behavior patterns that could be used to predict the event.

Conclusions

We found evidence in this case series that continuous sensor-based monitoring of patient behavior in home settings may be used to provide automated detection of health events. Nursing insights into smart home sensor data could be used to initiate preventive strategies and provide timely intervention.

Tweetable abstract

Nurses partnered with smart homes could detect exacerbations of health conditions at home leading to early intervention.

A Review of the Measurement of the Neurology of Gait in Cognitive Dysfunction or Dementia, Focusing on the Application of fNIRS during Dual-Task Gait Assessment

Poor motor function or physical performance is a predictor of cognitive decline. Additionally, slow gait speed is associated with poor cognitive performance, with gait disturbances being a risk factor for dementia. Parallel declines in muscular and cognitive performance (resulting in cognitive frailty) might be driven primarily by muscle deterioration, but bidirectional pathways involving muscle–brain crosstalk through the central and peripheral nervous systems are likely to exist. Following screening, early-stage parallel declines may be manageable and modifiable through simple interventions. Gait–brain relationships in dementia and the underlying mechanisms are not fully understood; therefore, the current authors critically reviewed the literature on the gait–brain relationship and the underlying mechanisms and the feasibility/accuracy of assessment tools in order to identify research gaps. The authors suggest that dual-task gait is involved in concurrent cognitive and motor activities, reflecting how the brain allocates resources when gait is challenged by an additional task and that poor performance on dual-task gait is a predictor of dementia onset. Thus, tools or protocols that allow the identification of subtle disease- or disorder-related changes in gait are highly desirable to improve diagnosis. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, cost-effective, safe, simple, portable, and non-motion-sensitive neuroimaging technique, widely used in studies of clinical populations such as people suffering from Alzheimer’s disease, depression, and other chronic neurological disorders. If fNIRS can help researchers to better understand gait disturbance, then fNIRS could form the basis of a cost-effective means of identifying people at risk of cognitive dysfunction and dementia. The major research gap identified in this review relates to the role of the central/peripheral nervous system when performing dual tasks.

Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram and point clouds for 19 human joints. We developed a multilayer Convolutional Neural Network (CNN) to recognize and classify five different gait patterns with an accuracy of 95.7 to 98.8% using MMW radar data. During training of the CNN algorithm, we used the extracted 3D coordinates of 25 joints using the Kinect V2 sensor and compared them with the point clouds data to improve the estimation. Finally, we performed a real-time simulation to observe the point cloud behavior for different activities and validated our system against the ground truth values. The proposed method demonstrates the ability to distinguish between different human activities to obtain clinically relevant gait information.

Prediction of Disorientation by Accelerometric and Gait Features in Young and Older Adults Navigating in a Virtually Enriched Environment

Objective

To determine whether gait and accelerometric features can predict disorientation events in young and older adults.

Methods

Cognitively healthy younger (18–40 years, n = 25) and older (60–85 years, n = 28) participants navigated on a treadmill through a virtual representation of the city of Rostock featured within the Gait Real-Time Analysis Interactive Lab (GRAIL) system. We conducted Bayesian Poisson regression to determine the association of navigation performance with domain-specific cognitive functions. We determined associations of gait and accelerometric features with disorientation events in real-time data using Bayesian generalized mixed effect models. The accuracy of gait and accelerometric features to predict disorientation events was determined using cross-validated support vector machines (SVM) and Hidden Markov models (HMM).

Results

Bayesian analysis revealed strong evidence for the effect of gait and accelerometric features on disorientation. The evidence supported a relationship between executive functions but not visuospatial abilities and perspective taking with navigation performance. Despite these effects, the cross-validated percentage of correctly assigned instances of disorientation was only 72% in the SVM and 63% in the HMM analysis using gait and accelerometric features as predictors.

Conclusion

Disorientation is reflected in spatiotemporal gait features and the accelerometric signal as a potentially more easily accessible surrogate for gait features. At the same time, such measurements probably need to be enriched with other parameters to be sufficiently accurate for individual prediction of disorientation events.

Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards?

Aim

To investigate the feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system.

Findings

Wearable gait analysis can be implemented into geriatric wards, showing its readiness for a transformation from a pure research tool to a practically usable gait analysis system.

Message

Despite good transferability into clinical practice, future research should aim to increase functionality and applicability of wearable gait analysis systems in clinical contexts.

Purpose

We assess feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system.

Methods

Gait parameters of 83 patients (83.34 ± 5.88 years, 58/25 female/male) were recorded at admission and/or discharge to/from two geriatric inpatient wards. Gait parameters were tested for statistically significant differences between admission and discharge. Walking distance measured by a wearable gait analysis system was correlated with distance assessed by physiotherapists. Examiners rated usability using the system usability scale. Patients reported acceptability on a five-point Likert-scale.

Results

The total distance measures highly correlate (r = 0.89). System Usability Scale is above the median threshold of 68, indicating good usability. Majority of patients does not have objections regarding the use of the system. Among other gait parameters, mean heel strike angle changes significantly between admission and discharge.

Conclusion

Wearable gait analysis system is objectively and subjectively usable in a clinical setting and accepted by patients. It offers a reasonably valid assessment of gait parameters and is a feasible way for instrumented gait analysis.

The Impact of SARS-CoV-2 (COVID-19) and its Lockdown Measures on the Mental and Functional Health of Older Individuals

The effect of the COVID-19 on the physical and mental health of Italian older individuals displaying signs of cognitive deterioration has not been deeply investigated. This longitudinal study examined the impact of COVID-19 lockdown measures on the psychological well-being and motor efficiency of a sample of Italian community-dwellers with and without cognitive decline. Forty-seven participants underwent instrumental gait analysis performed in ecological setting using wearable sensors, and completed a battery of tasks assessing cognitive functioning and psychological well-being, before and after the full lockdown due to the COVID-19 spreading. A series of Multivariate Analyses of Variance (MANOVAs) documented that the superior gait performance of the cognitively healthy participants exhibited before the COVID-19 spread, vanished when they were tested at the end of the lockdown period. Moreover, before the outbreak of the COVID-19, cognitively healthy participants and those with signs of cognitive decline reported similar levels of psychological well-being, whereas, after the lockdown, the former group reported better coping, emotional competencies, and general well-being than the participants displaying signs of cognitive decline. In conclusion, the full COVID-19 outbreak had a significant impact on the mental and motor functioning of older individuals with and without signs of cognitive deterioration living in Italy.

A Pilot Study to Validate a Wearable Inertial Sensor for Gait Assessment in Older Adults with Falls

The high prevalence of falls and the enormous impact they have on the elderly population is a cause for concern. We aimed to develop a walking-monitor gait pattern (G-STRIDE) for older adults based on a 6-axis inertial measurement (IMU) with the application of pedestrian dead reckoning algorithms and tested its structural and clinical validity. A cross-sectional case–control study was conducted with 21 participants (11 fallers and 10 non-fallers). We measured gait using an IMU attached to the foot while participants walked around different grounds (indoor flooring, outdoor floor, asphalt, etc.). The G-STRIDE consisted of a portable inertial device that monitored the gait pattern and a mobile app for telematic clinical analysis. G-STRIDE made it possible to measure gait parameters under normal living conditions when walking without assessing the patient in the outpatient clinic. Moreover, we verified concurrent validity with convectional outcome measures using intraclass correlation coefficients (ICCs) and analyzed the differences between participants. G-STRIDE showed high estimation accuracy for the walking speed of the elderly and good concurrent validity compared to conventional measures (ICC = 0.69; p < 0.000). In conclusion, the developed inertial-based G-STRIDE can accurately classify older people with risk to fall with a significance as high as using traditional but more subjective clinical methods (gait speed, Timed Up and Go Test).