In-home Gait Research Articles

In-home Gait Abnormality Detection through Footstep-Induced Floor Vibration Sensing and Person-Invariant Contrastive Learning.

Detecting gait abnormalities is crucial for assessing fall risks and early identification of neuromusculoskeletal disorders such as Parkinson's and stroke. Traditional assessments in gait clinics are infrequent and pose barriers, particularly for disadvantaged populations. Previous efforts have explored sensor-based approaches for in-home gait assessments, yet they face limitations such as visual obstructions (cameras), limited coverage (pressure mats), and the need for device carrying (wearables and insoles). To overcome these limitations, we introduce an in-home gait abnormality detection system using footstep-induced floor vibrations, enabling low-cost, non-intrusive, device-free gait health monitoring. The main research challenge is the high uncertainty in floor vibrations due to gait variations among people, making it challenging to develop a generalizable model for new patients. To address this, we analyze time-frequency-domain features of floor vibration data during specific gait phases and develop a feature transformation method through contrastive learning to address the between-people gait variation challenge. Our method transforms the features from vibrations to an embedding space where samples from different people stay close to each other (robust to people variation) while normal and abnormal gait samples are far apart (sensitive to gait abnormality). After that, gait abnormalities are detected by a downstream classifier after feature transformation. We evaluated our approach through a real-world walking experiment with 21 participants and achieved an 85% to 95% mean accuracy in detecting various gait abnormalities. This novel method overcomes prior limitations in in-home gait assessments, offering accessible gait abnormality detection without the need for intrusive devices or labels for new patients.

AI-Powered Noncontact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing

In this work, we present a cloud-based system for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. To train deep learning models, we utilize range-Doppler maps generated from a dataset of real-life in-home activities. The performance of several deep learning models is evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. The overall accuracy of the GRU model for classifying in-home physical activities of trained subjects is 93%, with 86% accuracy for a new subject. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices.

Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

BackgroundFrailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA).MethodsDPA was acquired for 48 h from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables.ResultsOne hundred twenty-six older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step- and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p < 0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity.DiscussionEveryday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty.Trial registrationThe clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229.

Comparison of gait speeds from wearable camera and accelerometer in structured and semi-structured environments.

A feasibility study was conducted to investigate the use of a wearable gait analysis system for classifying gait speed using a low-cost wearable camera in a semi-structured indoor setting. Data were collected from 19 participants who wore the system during indoor walk sequences at varying self-determined speeds (slow, medium, and fast). Gait parameters using this system were compared with parameters obtained from a vest comprising of a single triaxial accelerometer and from a marker-based optical motion-capture system. Computer-vision techniques and signal processing methods were used to generate frequency-domain gait parameters from each gait-recording device, and those parameters were analysed to determine the effectiveness of the different measurement systems in discriminating gait speed. Results indicate that the authors’ low-cost, portable, vision-based system can be effectively used for in-home gait analysis.

FRAILTY ASSESSMENT BASED ON THE QUALITY OF DAILY WALKING

Frailty is an increasingly recognized geriatric syndrome resulting in age-related decline in reserve across multiple physiologic systems. An impaired physical function is a prime indicator of frailty. In this study, we aim to implement a body-worn sensor to characterize the quantity and quality of everyday walking, and establish associations between gait impairment and frailty. Daily physical activity was acquired for 48 hours from 125 older adults (≥65 years; 44 non-frail, 60 pre-frail, and 21 frail based on the Fried gold standard) using a tri-axial accelerometer motion-sensor. Continuous purposeful walks (≥60s) without pauses were identified from time-domain acceleration data. Power spectral density (PSD) analysis was performed to define higher gait variability, which was identified by a shorter and wider PSD peak. Association between frailty and gait parameters was assessed using multivariable nominal logistic models with frailty as the dependent variable, and demographic parameters along with the gait parameters as the independent variables. Stride times, PSD gait variability, and total and maximum continuous purposeful walking duration were significantly different between non-frail and pre-frail/frail groups (p<0.05). Using a step-wise model with the above qualitative and quantitative gait parameters as predictors, the pre-frail/frail group (vs. non-frail) was identified with 71.4% sensitivity and 75.4% specificity. Everyday walking characteristics were found to be accurate determinants of frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the stages of frailty. In-home gait analysis is advantageous over clinical gait analysis as it enables cost- and space-effective continuous monitoring.

Ecological Gait as a Fall Indicator in Older Adults: A Systematic Review.

Falls represent a major threat for elders, affecting their life quality and expectancy. Clinical tests and questionnaires showed low diagnostic value with respect to fall risk. Modern sensor technology allows in-home gait assessments, with the possibility to register older adults' ecological mobility and, potentially, to improve accuracy in determining fall risk. Hence, we studied the correlation between standardized assessments and ecological gait measures, comparing their ability to identify fall risk and predict prospective falls. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement guidelines. From a total of 938 studies screened, nine articles with an observational study design were included. Evidence from selected works was subcategorized in (i) correlations between ecological and clinical measures and comparative statistics of (ii) prospective fall prediction and (iii) fall risk identification. A large number of correlations were observed between single ecological gait assessments and multiple clinical fall risk evaluations. Moreover, the combination of daily-life features and clinical tests outcomes seemed to improve diagnostic accuracy in fall risk identification and fall prediction. However, it was not possible to understand the extent of this enhancement due to the high variability in models' parameters. Evidence suggested that sensor-based ecological assessments of gait could boost diagnostic accuracy of fall risk measurement protocols if used in combination with clinical tests. Nevertheless, further studies are needed to understand what ecological features of gait should be considered and to standardize models' definition.

Doppler Radar System for In-Home Gait Characterization using Wavelet Transform Analysis.

The evaluation of walking speed plays an important role in gerontology as it reflects the health and functional status of older people. In this paper, we propose the use of a Doppler radar system with continuous wavelet transform (CWT) analysis for in-home gait characterization. A methodology based on an accurate 3D motion-capture camera system (Vicon) has been developed in order to validate the suitable mother wavelet. The CWT analysis with several mother wavelets has been applied to our experimental Doppler radar signals. The Pearson Correlation coefficient (ρ) has been computed between the gait speed signals obtained from the radar and those obtained from the Vicon system. Our outcomes suggest the use of Daubechies5 and Symlets7 wavelets giving a ρ values of 0.86 and 0.85 respectively with a mean square error value less than 0.05 m/s in comparison with the correct gait speed value.

Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler Signatures.

In this paper, we demonstrate the applicability of radar for gait classification with application to home security, medical diagnosis, rehabilitation, and assisted living. Aiming at identifying changes in gait patterns based on radar micro-Doppler signatures, this paper is concerned with solving the intra motion category classification problem of gait recognition. New gait classification approaches utilizing physical features, subspace features, and sum-of-harmonics modeling are presented and their performances are evaluated using experimental K-band radar data of four test subjects. Five different gait classes are considered for each person, including normal, pathological, and assisted walks. The proposed approaches are shown to outperform existing methods for radar-based gait recognition, which utilize physical features from the cadence-velocity data representation domain as in this paper. The analyzed gait classes are correctly identified with an average accuracy of 93.8%, where a classification rate of 98.5% is achieved for a single gait class. When applied to new data of another individual, a classification accuracy on the order of 80% can be expected. Radar micro-Doppler signatures and their Fourier transforms are well suited to capture changes in gait. Five different walking styles are recognized with high accuracy. Radar-based sensing of gait is an emerging technology with multi-faceted applications in security and health care industries. We show that radar, as a contact-less sensing technology, can supplement existing gait diagnostic tools with respect to long-term monitoring and reproducibility of the examinations.

Adherence to in-home gait retraining using feedback from a smartphone app

Purpose: A high knee adduction moment (KAM) has repeatedly been linked to progression and severity of medial knee osteoarthritis (OA). It places higher than normal loads on the medial compartment of the knee and has become a target for biomechanical intervention. To lower KAM and slow OA progression, gait modification and retraining have been explored. However, the challenge has been incorporating feasible, longitudinal methods for gait retraining. We recently demonstrated that healthy subjects can quickly respond to auditory feedback from a pressure-detecting shoe insole to lower their KAM by shifting their foot pressure medially. Encouraged by these findings, we started a randomized clinical trial enrolling subjects with medial knee OA and outfitting them with a sensor shoe insole and a smartphone feedback kit. Here, we report about our initial experience with adherence to a three-week long program using this technology for daily in-home gait retraining. We were specifically concerned that negative feedback may cause training avoidance and decreased adherence over time and therefore sought to evaluate if there were differences in adherence between this active group and one that handles similar technology without receiving feedback. Methods: Subjects with mild to moderate clinical and radiographic knee OA, BMI <38, and age >40 yrs were recruited and informed consent was obtained. At the beginning of their lab visit, all subjects received a standardized pair of flexible shoes, a pair of pressure-detecting shoe insoles (Moticon GmbH, Munich, Germany), which communicate wirelessly with a smartphone that generates auditory cues for subjects to follow. Subjects were randomized into two groups, Group 1 (‘active group’) which received and trained with auditory feedback cues, and Group 2 (‘passive control’) that interacted with the smartphone without receiving feedback. Both groups learned to connect the insole with the smartphone via a smartphone application. After lab-based instructions, subjects in Group 1 and Group 2 were sent home to complete a three-week, six-days-per-week self-directed feedback training or recording, respectively. All subjects were asked to document the duration of each session in their journal. Subjects were instructed to log a total of 15 min per day, which could be broken into smaller increments (e.g., 5-5-5 min). After 3 weeks of training, the journals were returned to study staff and daily logged minutes were summed. Missing entries were treated as “0”. Statistical analyses were performed using chi-square and repeated measures ANOVA. Results: The journal entries of the first 22 subjects were reviewed. Four subjects were excluded from analysis: one because he withdrew from the study (Group 1), two because journals were not returned (Group 1), and one because excessive training times were logged (143 min daily; Group 2). This left 18 subjects for analysis: 10 were in Group 1 (2M/8F, 61+/−11 yrs, KL = 2.4), and eight were in Group 2 (2M/6F, 62+/−9 yrs, KL = 2.6) (P > .343). On average, Group 1 trained 12.6 min/day, while Group 2 recorded 14.2 min/day during the three week period (P = .679). There was no Group effect on daily logged journal entries (P = .578), and both groups started similarly motivated (17.2 and 17.4 min/day during the first three days); however, with time, the logged journal times decayed (P = .003). Regression analysis suggested a daily reduction of 32 s (Group 1) and 38 s (Group 2), finishing clearly under 10 min/day on Day 18 (Figure). Conclusions: Our initial experience with self-driven, in-home gait retraining using a pressure insole and negative feedback is promising in terms of overall adherence. Although we saw a decrease in adherence over time, the difference was likely not related to negative feedback from training since the no-feedback group also experienced a similar trend in decreased adherence rates over 3 weeks. In that ‘no journal entry’ was treated equally to ‘no training’, a worst case scenario has been created. Treating ‘no journal entries’ as ‘lost data’ would increase the daily average session times to 17.3 and 16.7 min for Group 1 and 2, respectively, with both groups finishing at 12.5 min on Day 18. Nevertheless, continued follow up and encouragement techniques may be necessary to help maintain adherence.

Using Embedded Sensors in Independent Living to Predict Gait Changes and Falls.

This study explored using Big Data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents' Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait parameters continuously collected for residents who actually fell ( n = 13) and those who did not fall ( n = 10). We analyzed associations between participants' fall events ( n = 69) and pre-fall changes in in-home gait speed and stride length ( n = 2,070). Preliminary results indicate that a cumulative change in speed over time is associated with the probability of a fall ( p < .0001). The odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the odds of a resident falling within 3 weeks after no change in in-home gait speed. Results demonstrate using sensors to measure in-home gait parameters associated with the occurrence of future falls.

Comparison between passive vision-based system and a wearable inertial-based system for estimating temporal gait parameters related to the GAITRite electronic walkway

Quantitative gait analysis allows clinicians to assess the inherent gait variability over time which is a functional marker to aid in the diagnosis of disabilities or diseases such as frailty, the onset of cognitive decline and neurodegenerative diseases, among others. However, despite the accuracy achieved by the current specialized systems there are constraints that limit quantitative gait analysis, for instance, the cost of the equipment, the limited access for many people and the lack of solutions to consistently monitor gait on a continuous basis. In this paper, two low-cost systems for quantitative gait analysis are presented, a wearable inertial system that relies on two wireless acceleration sensors mounted on the ankles; and a passive vision-based system that externally estimates the measurements through a structured light sensor and 3D point-cloud processing. Both systems are compared with a reference clinical instrument using an experimental protocol focused on the feasibility of estimating temporal gait parameters over two groups of healthy adults (five elders and five young subjects) under controlled conditions. The error of each system regarding the ground truth is computed. Inter-group and intra-group analyses are also conducted to transversely compare the performance between both technologies, and of these technologies with respect to the reference system. The comparison under controlled conditions is required as a previous stage towards the adaptation of both solutions to be incorporated into Ambient Assisted Living environments and to provide continuous in-home gait monitoring as part of the future work.

Automated in-home gait transfer time analysis using video cameras

Previous studies have shown that gait speed is an important measure of functional ability in the elderly. Continuous monitoring of the gait speed of older adults in their home environment may therefore allow the detection of changes in gait speed which could be predictive of health changes of the monitored person. In this study, a system consisting of multiple wall-mounted cameras that can automatically measure the time an older adult needs to cross a predefined transfer zone in the home environment is presented. The purpose of this study is the preliminary validation of the algorithm of the camera system which consists of several preprocessing steps and the automatic measurement of the transfer times. This validation is done through data collection in the homes of four older adults for periods varying from eight to twelve weeks. Trends in the measured transfer times are visualised and subsequently compared with the results of clinical assessments obtained during the acquisition period such as Timed-Get-Up-and-Go tests. The results indicate that it is possible to identify long-term trends in transfer times which can be indicative of adverse health-related events.

Slow Gait Speed and Risk of Mortality or Hospital Readmission After Myocardial Infarction in the Translational Research Investigating Underlying Disparities in Recovery from Acute Myocardial Infarction: Patients' Health Status Registry.

To determine the prognostic value of slow gait in predicting outcomes 1 year after acute myocardial infarction (AMI). Observational cohort with longitudinal follow-up. Twenty-four U.S. hospitals participating in the Translational Research Investigating Underlying disparities in recovery from acute Myocardial infarction: Patients' Health status Registry. Older adults (≥65) with in-home gait assessment 1 month after AMI (N = 338). Baseline characteristics and 1-year mortality or hospital readmission adjusted using Cox proportional hazards regression in older adults with slow (<0.8 m/s) versus preserved (≥0.8 m/s) gait speed. Slow gait was present in 181 participants (53.6%). Those with slow gait were older, more likely to be female and nonwhite, and had a higher prevalence of heart failure and diabetes mellitus. They were also more likely to die or be readmitted to the hospital within 1 year than those with preserved gait (35.4% vs 18.5%, log-rank P = .006). This association remained significant after adjusting for age, sex, and race (slow vs preserved gait hazard ratio (HR) = 1.76, 95% confidence interval (CI)=1.08-2.87, P = .02) but was no longer significant after adding clinical factors (HR = 1.23, 95% CI=0.74-2.04, P = .43). Slow gait, a marker of frailty, is common 1 month after AMI in older adults and is associated with nearly twice the risk of dying or hospital readmission at 1 year. Understanding its prognostic importance independent of comorbidities and whether routine testing of gait speed can improve care requires further investigation.

Automated extraction and validation of children's gait parameters with the Kinect.

BackgroundGait analysis for therapy regimen prescription and monitoring requires patients to physically access clinics with specialized equipment. The timely availability of such infrastructure at the right frequency is especially important for small children. Besides being very costly, this is a challenge for many children living in rural areas. This is why this work develops a low-cost, portable, and automated approach for in-home gait analysis, based on the Microsoft Kinect.MethodsA robust and efficient method for extracting gait parameters is introduced, which copes with the high variability of noisy Kinect skeleton tracking data experienced across the population of young children. This is achieved by temporally segmenting the data with an approach based on coupling a probabilistic matching of stride template models, learned offline, with the estimation of their global and local temporal scaling. A preliminary study conducted on healthy children between 2 and 4 years of age is performed to analyze the accuracy, precision, repeatability, and concurrent validity of the proposed method against the GAITRite when measuring several spatial and temporal children’s gait parameters.ResultsThe method has excellent accuracy and good precision, with segmenting temporal sequences of body joint locations into stride and step cycles. Also, the spatial and temporal gait parameters, estimated automatically, exhibit good concurrent validity with those provided by the GAITRite, as well as very good repeatability. In particular, on a range of nine gait parameters, the relative and absolute agreements were found to be good and excellent, and the overall agreements were found to be good and moderate.ConclusionThis work enables and validates the automated use of the Kinect for children’s gait analysis in healthy subjects. In particular, the approach makes a step forward towards developing a low-cost, portable, parent-operated in-home tool for clinicians assisting young children.

Average in-home gait speed: Investigation of a new metric for mobility and fall risk assessment of elders

A study was conducted to assess how a new metric, average in-home gait speed (AIGS), measured using a low-cost, continuous, environmentally mounted monitoring system, compares to a set of traditional physical performance instruments used for mobility and fall risk assessment of elderly adults. Sixteen participants were recruited from a local independent living facility. In addition to having their gait monitored continuously in their home for an average of eleven months, the participants completed a monthly clinical assessment consisting of a set of traditional assessment instruments: Habitual Gait Speed, Timed-Up and Go, Short Physical Performance Battery, Berg Balance Scale – short form, and Multidirectional Reach Test. A methodology is developed to assess which of these instruments may work well with the largest subset of older adults, is best suited for detecting changes in an individual over time, and most reliably captures the true mobility level of an individual. Using the ability of an instrument to predict how an individual would score on all the instruments as a metric, AIGS performs best, having better predictive ability than the traditional instruments. AIGS also displays the best agreement between observed and smoothed values, indicating it has the lowest intra-individual test–retest variability of the instruments. AIGS, measured continuously, during normal everyday activity, represents a significant shift in assessment methodology compared to infrequently assessed, traditional physical performance instruments. Continuous, in-home data may provide a more accurate and precise picture of the physical function of older adults, leading to improved mobility and fall risk assessment.

Automated health alerts from Kinect-based in-home gait measurements.

A method for automatically generating alerts to clinicians in response to changes in in-home gait parameters is investigated. Kinect-based gait measurement systems were installed in apartments in a senior living facility. The systems continuously monitored the walking speed, stride time, and stride length of apartment residents. A framework for modeling uncertainty in the residents' gait parameter estimates, which is critical for robust change detection, is developed; along with an algorithm for detecting changes that may be clinically relevant. Three retrospective case studies, of individuals who had their gait monitored for periods ranging from 12 to 29 months, are presented to illustrate use of the alert method. Evidence suggests that clinicians could be alerted to health changes at an early stage, while they are still small and interventions may be most successful. Additional potential uses are also discussed.

Quantitative gait measurement with pulse-Doppler radar for passive in-home gait assessment.

In this paper, we propose a pulse-Doppler radar system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed and step time using Doppler radar. The gait parameters have been validated with a Vicon motion capture system in the lab with 13 participants and 158 test runs. The study revealed that for an optimal step recognition and walking speed estimation, a dual radar set up with one radar placed at foot level and the other at torso level is necessary. An excellent absolute agreement with intraclass correlation coefficients of 0.97 was found for step time estimation with the foot level radar. For walking speed, although both radars show excellent consistency they all have a system offset compared to the ground truth due to walking direction with respect to the radar beam. The torso level radar has a better performance (9% offset on average) in the speed estimation compared to the foot level radar (13%-18% offset). Quantitative analysis has been performed to compute the angles causing the systematic error. These lab results demonstrate the capability of the system to be used as a daily gait assessment tool in home environments, useful for fall risk assessment and other health care applications. The system is currently being tested in an unstructured home environment.

Unobtrusive, Continuous, In-Home Gait Measurement Using the Microsoft Kinect

A system for capturing habitual, in-home gait measurements using an environmentally mounted depth camera, the Microsoft Kinect, is presented. Previous work evaluating the use of the Kinect sensor for in-home gait measurement in a lab setting has shown the potential of this approach. In this paper, a single Kinect sensor and computer were deployed in the apartments of older adults in an independent living facility for the purpose of continuous, in-home gait measurement. In addition, a monthly fall risk assessment protocol was conducted for each resident by a clinician, which included traditional tools such as the timed up a go and habitual gait speed tests. A probabilistic methodology for generating automated gait estimates over time for the residents of the apartments from the Kinect data is described, along with results from the apartments as compared to two of the traditionally measured fall risk assessment tools. Potential applications and future work are discussed.

Toward a passive low-cost in-home gait assessment system for older adults.

In this paper, we propose a webcam-based system for in-home gait assessment of older adults. A methodology has been developed to extract gait parameters including walking speed, step time, and step length from a 3-D voxel reconstruction, which is built from two calibrated webcam views. The gait parameters are validated with a GAITRite mat and a Vicon motion capture system in the laboratory with 13 participants and 44 tests, and again with GAITRite for 8 older adults in senior housing. Excellent agreement with intraclass correlation coefficients of 0.99 and repeatability coefficients between 0.7% and 6.6% was found for walking speed, step time, and step length given the limitation of frame rate and voxel resolution. The system was further tested with ten seniors in a scripted scenario representing everyday activities in an unstructured environment. The system results demonstrate the capability of being used as a daily gait assessment tool for fall risk assessment and other medical applications. Furthermore, we found that residents displayed different gait patterns during their clinical GAITRite tests compared to the realistic scenario, namely a mean increase of 21% in walking speed, a mean decrease of 12% in step time, and a mean increase of 6% in step length. These findings provide support for continuous gait assessment in the home for capturing habitual gait.

Evaluation of an inexpensive depth camera for in-home gait assessment

An investigation of a new, inexpensive depth camera device, the Microsoft Kinect, for passive gait assessment in home environments is presented. In order to allow older adults to safely continue li...