A comparison study of local dynamic stability measures of daily life walking in older adult community-dwelling fallers and non-fallers

Professional long distance runners achieve high efficiency at the cost of weak orbital stability

Successful performance in long distance race requires both high efficiency and stability. Previous research has demonstrated the high running efficiency of trained runners, but no prior study quantitatively addressed their orbital stability. In this study, we evaluated the efficiency and orbital stability of 8 professional long-distance runners and compared them with those of 8 novices. We calculated the cost of transport and normalized mechanical energy to assess physiological and mechanical running efficiency, respectively. We quantified orbital stability using Floquet Multipliers, which assess how fast a system converges to a limit cycle under perturbations. Our results show that professional runners run with significantly higher physiological and mechanical efficiency but with weaker orbital stability compared to novices. This finding is consistent with the inevitable trade-off between efficiency and stability; increase in orbital stability necessitates increase in energy dissipation. We suggest that professional runners have developed the ability to exploit inertia beneficially, enabling them to achieve higher efficiency partly at the cost of sacrificing orbital stability.

Overview of Radar-Based Gait Parameter Estimation Techniques for Fall Risk Assessment

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults' mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations - in particular, the micro-Doppler signature and skeletal point estimates - is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.

The relationship between the local dynamic stability of gait to cognitive and physical performance in older adults: A scoping review

BACKGROUND

Local dynamic stability (LDS) has become accepted as a gait stability indicator. The deterioration of gait stability is magnified in older adults.

RESEARCH QUESTION

What is the current state in the field regarding rthe relationship between LDS and cognitive and/or physical function in older adults?

METHODS

A scoping review design was used to search for peer-reviewed literature or conference proceedings published through May 2023 for an association between LDS and cognitive (e.g., Montreal Cognitive Assessment) or physical performance (e.g., Timed Up & Go Test) in older adults. Only studies investigating gait stability via LDS during controlled walking, when dealing with a subject group consisting of healthy older adults, and quantifying LDS relationship to cognitive and/or physical measure were included. We analysed data from the studies in a descriptive manner.

RESULTS

In total, 814 potentially relevant articles were selected, of which 15 met the inclusion criteria. We identified 37 LDS quantifiers employed in LDS-cognition and/or LDS-physical performance relationship assessment. Nine measures of cognitive and 20 measures of physical performance were analysed. Most studies estimated LDS quantities using triaxial acceleration data. However, there was a variance in sensor placement and signal direction. Out of the 56 studied relationships of LDS to physical performance measures, sixteen were found to be relevant. Out of 22 studied relationships between LDS and cognitive measures, only two were worthwhile.

SIGNIFICANCE

Considering the heterogeneity of the utilized LDS (caused by different sensors locations, signals, and signal directions as well as variety of computational approaches to estimate LDS) and cognitive/physical measures, the results of this scoping review does not indicate a current need for a systematic review with meta-analysis. To assess the overall utility of LDS to reveal a relationship between LDS to cognitive and physical performance measures, an analysis of other subject groups would be appropriate.

Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors

Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson's disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI's minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.

Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff's Views on Utility and Effectiveness

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies

Background

Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait.

Research objective

This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms.

Methods

A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included.

Results and significance

Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.

Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review

Background: Maintaining a healthy gait into old age is key to preserving the quality of life and reducing the risk of falling. Nonlinear dynamic analyses (NDAs) are a promising method of identifying characteristics of people who are at risk of falling based on their movement patterns. However, there is a range of NDA measures reported in the literature. The aim of this review was to summarise the variety, characteristics and range of the nonlinear dynamic measurements used to distinguish the gait kinematics of healthy older adults and older adults at risk of falling. Methods: Medline Ovid and Web of Science databases were searched. Forty-six papers were included for full-text review. Data extracted included participant and study design characteristics, fall risk assessment tools, analytical protocols and key results. Results: Among all nonlinear dynamic measures, Lyapunov Exponent (LyE) was most common, followed by entropy and then Fouquet Multipliers (FMs) measures. LyE and Multiscale Entropy (MSE) measures distinguished between older and younger adults and fall-prone versus non-fall-prone older adults. FMs were a less sensitive measure for studying changes in older adults’ gait. Methodology and data analysis procedures for estimating nonlinear dynamic measures differed greatly between studies and are a potential source of variability in cross-study comparisons and in generating reference values. Conclusion: Future studies should develop a standard procedure to apply and estimate LyE and entropy to quantify gait characteristics. This will enable the development of reference values in estimating the risk of falling.

Evaluation of movement and brain activity

Clinical neurophysiology studies can contribute important information about the physiology of human movement and the pathophysiology and diagnosis of different movement disorders. Some techniques can be accomplished in a routine clinical neurophysiology laboratory and others require some special equipment. This review, initiating a series of articles on this topic, focuses on the methods and techniques. The methods reviewed include EMG, EEG, MEG, evoked potentials, coherence, accelerometry, posturography (balance), gait, and sleep studies. Functional MRI (fMRI) is also reviewed as a physiological method that can be used independently or together with other methods. A few applications to patients with movement disorders are discussed as examples, but the detailed applications will be the subject of other articles.

Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review

Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.

Phase-Dependent Effects of Closed-Loop Tactile Feedback on Gait Stability in Parkinson's Disease

Gait disturbances in people with Parkinson's disease (PD) are a major cause for functional dependence and have recently been shown to be the largest risk factor for falls, institutionalization and death in PD. The use of external cues has been successful at improving gait in people with PD, but the effect of external cues on gait stability is unclear. We examined whether different forms of cueing, open-loop and closed-loop, influenced the local dynamic stability of three critical phases of gait. Forty-three adults with PD completed six, two-minute long walking trials in the following cued conditions: no cue (B), open-loop cueing, fixed auditory cue (OL), closed-loop cueing, tactile feedback delivered to wrist when the ipsilateral foot contacted with the ground (CL). Conditions were performed with and without a cognitive task. Kinematic data were recorded with inertial sensors. Only CL cueing was associated with changes in trunk stability, and these changes were only evident during the weight transfer phase of gait. Both OL and CL caused reductions in overall gait speed, stride length, and an increase in stride time. While CL cueing significantly influenced local dynamic stability during weight transfer, it remains unknown whether these changes are associated with more or less global stability. Future research will explore the clinical implications.

Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments

BACKGROUND

Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient's habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.

METHODS

A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review.

RESULTS

Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm's accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.

CONCLUSION

This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.

Deep Learning for Fall Risk Assessment With Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters

Fall risk assessment is essential to predict and prevent falls in geriatric populations, especially patients with life-long conditions like neurological disorders. Inertial sensor-based pervasive gait analysis systems have become viable means to facilitate continuous fall risk assessment in non-hospital settings. However, a gait analysis system is not sufficient to detect the characteristics leading to increased fall risk, and powerful inference models are required to detect the underlying factors specific to fall risk. Machine learning models and especially the recently proposed deep learning methods offer the needed predictive power. Deep neural networks have the potential to produce models that can operate directly on the raw data, thus alleviating the need for feature engineering. However, the domain knowledge inherent in the well-established spatio-temporal gait parameters are still valuable to help a model achieve high inference accuracies. In this study, we explore deep learning methods, specifically long short-term memory (LSTM) neural networks, for the problem of fall risk assessment. We utilize sequences of spatio-temporal gait parameters extracted by an inertial sensor-based gait analysis system as input features. To quantify the performance of the proposed approach, we compare it with more traditional machine learning methods. The proposed LSTM model, trained with a gait dataset collected from 60 neurological disorder patients, achieves a superior classification accuracy of 92.1% on a separate test dataset collected from 16 patients. This study serves as one of the first attempts to employ deep learning approaches in this domain and the results demonstrate their potential.

Comparison of selected measures of gait stability derived from center of pressure displacement signal during single and dual-task treadmill walking

Steady state gait dynamics has been examined using the measures of regularity, local dynamic stability, and variability. This study investigates the relationship between these measures under increasing cognitive loads. Participants walked on an instrumented treadmill at 1 m/s under walk only and two dual-task conditions. The secondary tasks were visuomotor cognitive games (VCG) of increasing difficulty level. The center of pressure displacement in the mediolateral direction (ML COP-D) and cognitive game performance were recorded for analysis. The following measures were calculated: (1) sample entropy (SampEn) and quantized dynamical entropy (QDE) of the ML COP-D, (2) short-term largest Lyapunov exponent (LLE) of the ML COP-D, and (3) variability of inter-stride spatio-temporal gait variables. Entropy and variability measures significantly increased from walk only to both dual-task conditions. Whereas, the short-term LLE increased only during the easy VCG task. No measure was sensitive to the difficulty level of the VCG tasks. The variability of heel strike positions in the mediolateral direction was positively correlated with SampEn and QDE. However, there were no significant correlations between the short-term LLE and either variability measures or entropy measures. These findings confirm that each of these measures is representative of a different aspect of human gait dynamics.

Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring

Background

The traditional evaluation of gait in the laboratory during structured testing has provided important insights, but is limited by its “snapshot” character and observation in an unnatural environment. Wearables enable monitoring of gait in real-world environments over a week. Initial findings show that in-lab and real-world measures differ. As a step towards better understanding these gaps, we directly compared in-lab usual-walking (UW) and dual-task walking (DTW) to daily-living measures of gait.

Methods

In-lab gait features (e.g., gait speed, step regularity, and stride regularity) derived from UW and DTW were compared to the same gait features during daily-living in 150 elderly fallers (age: 76.5 ± 6.3 years, 37.6% men). In both settings, features were extracted from a lower-back accelerometer. In the real-world setting, subjects were asked to wear the device for 1 week and pre-processing detected 30-s daily-living walking bouts. A histogram of all walking bouts was determined for each walking feature for each subject and then each subject’s typical (percentile 50, median), worst (percentile 10) and the best (percentile 90) values over the week were determined for each feature. Statistics of reliability were assessed using Intra-Class correlations and Bland-Altman plots.

Results

As expected, in-lab gait speed, step regularity, and stride regularity were worse during DTW, compared to UW. In-lab gait speed, step regularity, and stride regularity during UW were significantly higher (i.e., better) than the typical daily-living values (p < 0.001) and different (p < 0.001) from the worst and best values. DTW values tended to be similar to typical daily-living values (p = 0.205, p = 0.053, p = 0.013 respectively). ICC assessment and Bland-Altman plots indicated that in-lab values do not reliably reflect the daily-walking values.

Conclusions

Gait values measured during relatively long (30-s) daily-living walking bouts are more similar to the corresponding values obtained in the lab during dual-task walking, as compared to usual walking. Still, gait performance during most daily-living walking bouts is worse than that measured during usual and dual-tasking in the lab. The values measured in the lab do not reliably reflect daily-living measures. That is, an older adult’s typical daily-living gait cannot be estimated by simply measuring walking in a structured, laboratory setting.

The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review

BACKGROUND

Quantitative gait analysis is essential for evaluating walking and running patterns for markers of pathology, injury, or other gait characteristics. It is expected that the portability, affordability, and applicability of wearable devices to many different populations will have contributed advancements in understanding the real-world gait patterns of walkers and runners. Therefore, the purpose of this systematic review was to identify how wearable devices are being used for gait analysis in out-of-lab settings.

METHODS

A systematic search was conducted in the following scientific databases: PubMed, Medline, CINAHL, EMBASE, and SportDiscus. Each of the included articles was assessed using a custom quality assessment. Information was extracted from each included article regarding the participants, protocol, sensor(s), and analysis.

RESULTS

A total of 61 articles were reviewed: 47 involved gait analysis during walking, 13 involved gait analysis during running, and one involved both walking and running. Most studies performed adequately on measures of reporting, and external and internal validity, but did not provide a sufficient description of power. Small, unobtrusive wearable devices have been used in retrospective studies, producing unique measures of gait quality. Walking, but not running, studies have begun to use wearable devices for gait analysis among large numbers of participants in their natural environment.

CONCLUSIONS

Despite the advantages provided by the portability and accessibility of wearable devices, more studies monitoring gait over long periods of time, among large numbers of participants, and in natural walking and running environments are needed to analyze real-world gait patterns, and would facilitate prospective, subject-specific, and subgroup investigations. The development of wearables-specific metrics for gait analysis provide insights regarding the quality of gait that cannot be determined using traditional components of in-lab gait analyses. However, guidelines for the usability of wearable devices and the validity of wearables-based measurements of gait quality need to be established.

Gait Stability Has Phase-Dependent Dual-Task Costs in Parkinson's Disease

Dual-task (DT) paradigms have been used in gait research to assess the automaticity of locomotion, particularly in people with Parkinson’s disease (PD). In people with PD, reliance on cortical control during walking leads to greater interference between cognitive and locomotor tasks. Yet, recent studies have suggested that even healthy gait requires cognitive control, and that these cognitive contributions occur at specific phases of the gait cycle. Here, we examined whether changes in gait stability, elicited by simultaneous cognitive DTs, were specific to certain phases of the gait cycle in people with PD. Phase-dependent local dynamic stability (LDS) was calculated for 95 subjects with PD and 50 healthy control subjects during both single task and DT gait at phases corresponding to (1) heel contact—weight transfer, (2) toe-off—early swing, and (3) single-support—mid swing. PD-related DT interference was evident only for the duration of late swing and LDS during the heel contact—weight transfer phase of gait. No PD-related DT costs were found in other traditional spatiotemporal gait parameters. These results suggest that PD-related DT interference occurs only during times where cortical activity is needed for planning and postural adjustments. These results challenge our understanding of DT costs while walking, particularly in people with PD, and encourage researchers to re-evaluate traditional concepts of DT interference.

Local dynamic stability during gait for predicting falls in elderly people: A one-year prospective study

Computing the local dynamic stability using accelerometer data from inertial sensors has recently been proposed as a gait measure which may be able to identify elderly people at fall risk. However, the assumptions supporting this potential were concluded as most studies implement a retrospective fall history observation. The aim of this study was to evaluate the potential of local dynamic stability for fall risk prediction in a cohort of subjects over the age of 60 years using a prospective fall occurrence observation. A total of 131 elderly subjects voluntarily participated in this study. The baseline measurement included gait stability assessment using inertial sensors and clinical examination by Tinetti Balance Assessment Tool. After the baseline measurement, subjects were observed for a period of one year for fall occurrence. Our results demonstrated poor multiple falls predictive ability of trunk local dynamic stability (AUC = 0.673). The predictive ability improved when the local dynamic stability was combined with clinical measures, a combination of trunk medial-lateral local dynamic stability and Tinetti total score being the best predictor (AUC = 0.755). Together, the present findings suggest that the medial-lateral local dynamic stability during gait combined with a clinical score is a potential fall risk assessment measure in the elderly population.

Effect of interactive cognitive motor training on gait and balance among older adults: A randomized controlled trial

BACKGROUND

Aging is a normal degenerative process that results in a decline in the gait and balance performance of older adults. Interactive cognitive motor training is an intervention that integrates cognitive and motor tasks to promote individuals' physical and cognitive fall risk factors. However, the additive effects of the interactive cognitive motor training on objective quantitative data and comprehensive descriptions of gait and balance warrants further investigation.

OBJECTIVES

To investigate the effect of interactive cognitive motor training on older adults' gait and balance from immediate to long-term time points.

DESIGN

A double-blind randomized control trial.

SETTINGS

Four senior service centers and community service centers in Taiwan.

PARTICIPANTS

62 older adults who met the inclusion criteria.

METHODS

The study participants were older adults without cognitive impairment, and they were randomly allocated to the experimental group or active control group. In both groups, older adults participated in three sessions of 30-min training per week for a total of 8 weeks, with the total number of training sessions being 24. The primary outcome was gait performance, which was measured using objective and subjective indicators. iWALK was used as an objective indicator to measure pace and dynamic stability; the Functional Gait Assessment was employed as a subjective indicator. The secondary outcome was balance performance, which was measured using iSWAY. A generalized estimating equation was used to identify whether the results of the two groups differ after receiving different intervention measures; the results were obtained from immediate to long-term posttests.

RESULTS

Stride length in the pace category of the experimental group improved significantly in immediate posttest (p = 0.01), 3-month follow-up (p = 0.01), and 6-month follow-up (p = 0.04). The range of motion of the leg exhibited significant improvement in immediate posttest (p = 0.04) and 3-month follow-up (p = 0.04). The Functional Gait Assessment result indicated that statistically significant improvement was observed in immediate posttest (p = 0.02) and 12-month follow-up (p = 0.01). The results of balance performance showed that the experimental group attained statistically significant improvement in centroid frequency in the immediate posttest (p = 0.02).

CONCLUSIONS

The research results validated that the 24 sessions of the interactive cognitive motor training intervention significantly improved gait and balance performance. Future studies should extend the sample to communities to promote the gait and balance performance of community-dwelling older adults without cognitive impairment and reduce their risk of falling and developing gait-related diseases.

Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking

Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME (p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers.

Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review

After decades of evolution, measuring instruments for quantitative gait analysis have become an important clinical tool for assessing pathologies manifested by gait abnormalities. However, such instruments tend to be expensive and require expert operation and maintenance besides their high cost, thus limiting them to only a small number of specialized centers. Consequently, gait analysis in most clinics today still relies on observation-based assessment. Recent advances in wearable sensors, especially inertial body sensors, have opened up a promising future for gait analysis. Not only can these sensors be more easily adopted in clinical diagnosis and treatment procedures than their current counterparts, but they can also monitor gait continuously outside clinics - hence providing seamless patient analysis from clinics to free-living environments. The purpose of this paper is to provide a systematic review of current techniques for quantitative gait analysis and to propose key metrics for evaluating both existing and emerging methods for qualifying the gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications.

Can Tai Chi training impact fractal stride time dynamics, an index of gait health, in older adults? Cross-sectional and randomized trial studies

Purpose

To determine if Tai Chi (TC) has an impact on long-range correlations and fractal-like scaling in gait stride time dynamics, previously shown to be associated with aging, neurodegenerative disease, and fall risk.

Methods

Using Detrended Fluctuation Analysis (DFA), this study evaluated the impact of TC mind-body exercise training on stride time dynamics assessed during 10 minute bouts of overground walking. A hybrid study design investigated long-term effects of TC via a cross-sectional comparison of 27 TC experts (24.5 ± 11.8 yrs experience) and 60 age- and gender matched TC-naïve older adults (50–70 yrs). Shorter-term effects of TC were assessed by randomly allocating TC-naïve participants to either 6 months of TC training or to a waitlist control. The alpha (α) long-range scaling coefficient derived from DFA and gait speed were evaluated as outcomes.

Results

Cross-sectional comparisons using confounder adjusted linear models suggest that TC experts exhibited significantly greater long-range scaling of gait stride time dynamics compared with TC-naïve adults. Longitudinal random-slopes with shared baseline models accounting for multiple confounders suggest that the effects of shorter-term TC training on gait dynamics were not statistically significant, but trended in the same direction as longer-term effects although effect sizes were very small. In contrast, gait speed was unaffected in both cross-sectional and longitudinal comparisons.

Conclusion

These preliminary findings suggest that fractal-like measures of gait health may be sufficiently precise to capture the positive effects of exercise in the form of Tai Chi, thus warranting further investigation. These results motivate larger and longer-duration trials, in both healthy and health-challenged populations, to further evaluate the potential of Tai Chi to restore age-related declines in gait dynamics.

Trial registration

The randomized trial component of this study was registered at ClinicalTrials.gov (NCT01340365).

Feature selection for elderly faller classification based on wearable sensors

BACKGROUND

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data.

METHODS

A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling.

RESULTS

The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier.

CONCLUSIONS

Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Monitoring of Gait Quality in Patients With Chronic Pain of Lower Limbs

Severe injuries of lower extremities often lead to chronic pain and reduced walking abilities. We postulated that measuring free-living gait can provide further information about walking ability in complement to clinical evaluations. We sought to validate a method that characterizes free gaits with a wearable sensor. Over one week, 81 healthy controls (HC) and 66 chronic lower limb pain patients (CLLPP) hospitalized for multidisciplinary rehabilitation wore a simple accelerometer (Actigraph). In the acceleration signals, steady 1-min walks detected numbered 7,835 (5,085 in CLLPP and 2,750 in HC). Five gait quality measures were assessed: movement intensity, cadence, stride regularity, and short-term and long-term local dynamic stability. Gait quality variables differed significantly between CLLPP and HC (4%-26%). Intraclass correlation coefficients revealed moderate to high repeatability (0.71-0.91), which suggests that seven days of measurement are sufficient to assess average gait patterns. Regression analyses showed significant association (R² = 0.44) between the gait quality variables and a clinical evaluation of walking ability, i.e., the 6-min walk test. Overall, the results show that the method is easy to implement, valid (high concurrent validity), and reliable to assess walking abilities ecologically.

Quantifying Variation in Gait Features from Wearable Inertial Sensors Using Mixed Effects Models

The emerging technology of wearable inertial sensors has shown its advantages in collecting continuous longitudinal gait data outside laboratories. This freedom also presents challenges in collecting high-fidelity gait data. In the free-living environment, without constant supervision from researchers, sensor-based gait features are susceptible to variation from confounding factors such as gait speed and mounting uncertainty, which are challenging to control or estimate. This paper is one of the first attempts in the field to tackle such challenges using statistical modeling. By accepting the uncertainties and variation associated with wearable sensor-based gait data, we shift our efforts from detecting and correcting those variations to modeling them statistically. From gait data collected on one healthy, non-elderly subject during 48 full-factorial trials, we identified four major sources of variation, and quantified their impact on one gait outcome-range per cycle-using a random effects model and a fixed effects model. The methodology developed in this paper lays the groundwork for a statistical framework to account for sources of variation in wearable gait data, thus facilitating informative statistical inference for free-living gait analysis.