Sensor-based Gait Research Articles

Manifestations of walking fatigability in people with multiple sclerosis based on gait quality and distance walked during the six minutes walking test

BackgroundDistance walking fatigability (DWF) in people with multiple sclerosis (pwMS) is defined as a decrease in the distance walking over time. However, declines in gait quality (i.e., gait quality fatigability- GQF) may occur independently or alongside DWF. Objectivei) to investigate how walking fatigability manifests and its prevalence in pwMS; ii) to describe the temporal pattern of the changes of specific gait characteristics during the 6-minute walking test (6MWT) MethodsEighty-eight pwMS (EDSS 4[0–6.5], 49[21–70] years) and 47 healthy controls (HC- 46[25–60] years) performed the 6MWT wearing inertial measurement units. Gait characteristics (stride length, sensor-based gait speed, cadence, double support, step duration, stance phase, step duration asymmetry, step duration variability, foot-strike, toe-off, and leg circumduction) and walking distance were recorded in 1-minute intervals. A fatigability index was calculated by comparing the last and first minute of the 6MWT to identify abnormal worsening based on cutoff scores. The manifestation of walking fatigability was counted. The temporal pattern of worsening of gait characteristics during the 6MWT was examined in pwMS exceeding the cutoff values, compared to pwMS without abnormal changes and HC, using a two-way ANOVA (group vs. minutes) ResultsThirty-five pwMS presented both DWF and GQF, 2 presented isolated DWF, 27 presented isolated GQF, and 24 presented non-walking fatigability. PwMS having GQF presented worsening in gait characteristics (cadence, step duration, step duration variability, or toe-off angle) from minute 2 onwards of the 6MWT, while HCs and pwMS without abnormal changes stabilized gait from minute 2 towards the end of the 6MWT. ConclusionWalking fatigability in pwMS manifests not only as a decrease in walking distance but also as changes in gait quality. Understanding changes in gait characteristics during walking can help tailor rehabilitation interventions.

Sensor-Based Gait and Balance Assessment in Healthy Adults: Analysis of Short-Term Training and Sensor Placement Effects.

While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses.

Effects of physiotherapy and home-based training in parkinsonian syndromes: protocol for a randomised controlled trial (MobilityAPP)

IntroductionGait and mobility impairment are pivotal signs of parkinsonism, and they are particularly severe in atypical parkinsonian disorders including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). A pilot...

The impact of anxiety on gait impairments in Parkinson’s disease: insights from sensor-based gait analysis

BackgroundSensor-based gait analysis provides a robust quantitative tool for assessing gait impairments and their associated factors in Parkinson’s disease (PD). Anxiety is observed to interfere with gait clinically, but this has been poorly investigated. Our purpose is to utilize gait analysis to uncover the effect of anxiety on gait in patients with PD.MethodsWe enrolled 38 and 106 PD patients with and without anxiety, respectively. Gait parameters were quantitively examined and compared between two groups both in single-task (ST) and dual-task (DT) walking tests. Multiple linear regression was applied to evaluate whether anxiety independently contributed to gait impairments.ResultsDuring ST, PD patients with anxiety presented significantly shorter stride length, lower gait velocity, longer stride time and stance time, longer stance phase, smaller toe-off (TO) and heel-strike (HS) angles than those without anxiety. While under DT status, the differences were diminished. Multiple linear regression analysis demonstrated that anxiety was an independent factor to a serials of gait parameters, particularly ST-TO (B = -2.599, (-4.82, -0.38)), ST-HS (B = -2.532, (-4.71, -0.35)), ST-TO-CV (B = 4.627, (1.71, 7.64)), ST-HS-CV(B = 4.597, (1.66, 7.53)), ST stance phase (B = 1.4, (0.22, 2.58)), and DT stance phase (B = 1.749, (0.56, 2.94)).ConclusionOur study discovered that anxiety has a significant impact on gait impairments in PD patients, especially exacerbating shuffling steps and prolonging stance phase. These findings highlight the importance of addressing anxiety in PD precision therapy to achieve better treatment outcomes.

Bi-Directional Long Short-Term Memory-Based Gait Phase Recognition Method Robust to Directional Variations in Subject's Gait Progression Using Wearable Inertial Sensor.

Inertial Measurement Unit (IMU) sensor-based gait phase recognition is widely used in medical and biomechanics fields requiring gait data analysis. However, there are several limitations due to the low reproducibility of IMU sensor attachment and the sensor outputs relative to a fixed reference frame. The prediction algorithm may malfunction when the user changes their walking direction. In this paper, we propose a gait phase recognition method robust to user body movements based on a floating body-fixed frame (FBF) and bi-directional long short-term memory (bi-LSTM). Data from four IMU sensors attached to the shanks and feet on both legs of three subjects, collected via the FBF method, are processed through preprocessing and the sliding window label overlapping method before inputting into the bi-LSTM for training. To improve the model's recognition accuracy, we selected parameters that influence both training and test accuracy. We conducted a sensitivity analysis using a level average analysis of the Taguchi method to identify the optimal combination of parameters. The model, trained with optimal parameters, was validated on a new subject, achieving a high test accuracy of 86.43%.

Reliability of patient-specific gait profiles with inertial measurement units during the 2-min walk test in incomplete spinal cord injury

Most established clinical walking tests assess specific aspects of movement function (velocity, endurance, etc.) but are generally unable to determine specific biomechanical or neurological deficits that limit an individual’s ability to walk. Recently, inertial measurement units (IMU) have been used to collect objective kinematic data for gait analysis and could be a valuable extension for clinical assessments (e.g., functional walking measures). This study assesses the reliability of an IMU-based overground gait analysis during the 2-min walk test (2mWT) in individuals with spinal cord injury (SCI). Furthermore, the study elaborates on the capability of IMUs to distinguish between different gait characteristics in individuals with SCI. Twenty-six individuals (aged 22–79) with acute or chronic SCI (AIS: C and D) completed the 2mWT with IMUs attached above each ankle on 2 test days, separated by 1 to 7 days. The IMU-based gait analysis showed good to excellent test–retest reliability (ICC: 0.77–0.99) for all gait parameters. Gait profiles remained stable between two measurements. Sensor-based gait profiling was able to reveal patient-specific gait impairments even in individuals with the same walking performance in the 2mWT. IMUs are a valuable add-on to clinical gait assessments and deliver reliable information on detailed gait pathologies in individuals with SCI.Trial registration: NCT04555759.

Insole Pressure Sensors to Assess Post-Stroke Gait.

To confirm that the simplified insole does not affect the gait speed and to identify objective sensor-based gait parameters that correlate strongly with existing clinical gait assessment scales. Ten participants with gait impairment due to hemiplegic stroke were enrolled in this study. Pairs of insoles with four pressure sensors on each side were manufactured and placed in each shoe. Data were extracted during the 10-Meter Walk Test. Several sensor-derived parameters (for example stance time, heel_on-to-toe_peak time, and toe_peak pressure) were calculated and correlated with gait speed and lower extremity Fugl-Meyer (F-M) score. The insole pressure sensor did not affect gait, as indicated by a strong correlation (ρ=0.988) and high agreement (ICC=0.924) between the gait speeds with and without the insole. The parameters that correlated most strongly with highest β coefficients against the clinical measures were stance time of the non-hemiplegic leg (β=-0.87 with F-M and β=-0.95 with gait speed) and heel_on-to-toe_peak time of the non-hemiplegic leg (β=-0.86 with F-M and -0.94 with gait speed). Stance time of the non-hemiparetic leg correlates most strongly with clinical measures and can be assessed using a non-obtrusive insole pressure sensor that does not affect gait function. These results suggest that an insole pressure sensor, which is applicable in a home environment, may be useful as a clinical endpoint in post-stroke gait therapy trials.

Signal enhancement and efficient DTW-based comparison for wearable gait recognition

The popularity of biometrics-based user identification has significantly increased over the last few years. User identification based on the face, fingerprints, and iris, usually achieves very high accuracy only in controlled setups and can be vulnerable to presentation attacks, spoofing, and forgeries. To overcome these issues, this work proposes a novel strategy based on a relatively less explored biometric trait, i.e., gait, collected by a smartphone accelerometer, which can be more robust to the attacks mentioned above. According to the wearable sensor-based gait recognition state-of-the-art, two main classes of approaches exist: 1) those based on machine and deep learning; 2) those exploiting hand-crafted features. While the former approaches can reach a higher accuracy, they suffer from problems like, e.g., performing poorly outside the training data, i.e., lack of generalizability. This paper proposes an algorithm based on hand-crafted features for gait recognition that can outperform the existing machine and deep learning approaches. It leverages a modified Majority Voting scheme applied to Fast Window Dynamic Time Warping, a modified version of the Dynamic Time Warping (DTW) algorithm with relaxed constraints and majority voting, to recognize gait patterns. We tested our approach named MV-FWDTW on the ZJU-gaitacc, one of the most extensive datasets for the number of subjects, but especially for the number of walks per subject and walk lengths. Results set a new state-of-the-art gait recognition rate of 98.82% in a cross-session experimental setup. We also confirm the quality of the proposed method using a subset of the OU-ISIR dataset, another large state-of-the-art benchmark with more subjects but much shorter walk signals.

The effects of an individualized smartphone-based exercise program on self-defined motor tasks in Parkinson’s disease: a long-term feasibility study

BackgroundExercise therapy is considered effective for the treatment of motor impairment in patients with Parkinson’s disease (PD). During the COVID-19 pandemic, training sessions were cancelled and the implementation of telerehabilitation concepts became a promising solution. The aim of this controlled interventional feasibility study was to evaluate the long-term acceptance and to explore initial effectiveness of a digital, home-based, high-frequency exercise program for PD patients. Training effects were assessed using patient-reported outcome measures combined with sensor-based and clinical scores.Methods16 PD patients (smartphone group, SG) completed a home-based, individualized training program over 6–8 months using a smartphone app, remotely supervised by a therapist, and tailored to the patient’s motor impairments and capacity. A control group (CG, n = 16) received medical treatment without participating in digital exercise training. The usability of the app was validated using System Usability Scale (SUS) and User Version of the Mobile Application Rating Scale (uMARS). Outcome measures included among others Unified Parkinson Disease Rating Scale, part III (UPDRS-III), sensor-based gait parameters derived from standardized gait tests, Parkinson’s Disease Questionnaire (PDQ-39), and patient-defined motor activities of daily life (M-ADL).ResultsExercise frequency of 74.5% demonstrated high adherence in this cohort. The application obtained 84% in SUS and more than 3.5/5 points in each subcategory of uMARS, indicating excellent usability. The individually assessed additional benefit showed at least 6 out of 10 points (Mean = 8.2 ± 1.3). From a clinical perspective, patient-defined M-ADL improved for 10 out of 16 patients by 15.5% after the training period. The results of the UPDRS-III remained stable in the SG while worsening in the CG by 3.1 points (24%). The PDQ-39 score worsened over 6–8 months by 83% (SG) and 59% (CG) but the subsection mobility showed a smaller decline in the SG (3%) compared to the CG (77%) without reaching significance level for all outcomes. Sensor-based gait parameters remained constant in both groups.ConclusionsLong-term training over 6–8 months with the app is considered feasible and acceptable, representing a cost-effective, individualized approach to complement dopaminergic treatment. This study indicates that personalized, digital, high-frequency training leads to benefits in motor sections of ADL and Quality of Life.

Inertial measurement unit sensor-based gait analysis in adults and older adults: A cross-sectional study

BackgroundGait assessment has been used in a wide range of clinical applications, and gait velocity is also a leading predictor of disease and physical functional aspects in older adults. Research questionThe study aim to examine the changes in IMU-based gait parameters according to age in healthy adults aged 50 and older, to analyze differences between aging patients. MethodsA total of 296 healthy adults (65.32 ± 6.74 yrs; 83.10 % female) were recruited. Gait assessment was performed using an IMU sensor-based gait analysis system, and 3D motion information of hip and knee joints was obtained using magnetic sensors. The basic characteristics of the study sample were stratified by age category, and the baseline characteristics between the groups were compared using analysis of variance (ANOVA). Pearson's correlation analysis was used to analyze the relationship between age as the dependent variable and several measures of gait parameters and joint angles as independent variables. ResultsThe results of this study found that there were significant differences in gait velocity and both terminal double support in the three groups according to age, and statistically significant differences in the three groups in hip joint angle and knee joints angle. In addition, it was found that the gait velocity and knee/hip joint angle changed with age, and the gait velocity and knee/hip joint angle were also different in the elderly and adult groups. ConclusionsWe found changes in gait parameters and joint angles according to age in healthy adults and older adults and confirmed the difference in gait velocity and joint angles between adults and older adults.

Feature Learning Networks for Floor Sensor-based Gait Recognition.

Deep learning (DL) has become a powerful tool in many image classification applications but often requires large training sets to achieve high accuracy. For applications where the available data are limited, this can become a severely limiting factor in model performance. To address this limitation, feature learning network approaches that integrate traditional feature extraction methods with DL frameworks have been proposed. In this study, the performances of traditional methods: discrete wavelet transform (DWT), discrete cosine transform (DCT), independent component analysis (ICA), and principal component analysis (PCA); and their corresponding feature networks based on a convolutional neural network (CNN) framework: ScatNet (wavelet scattering network), DCTNet, ICANet, and PCANet, were investigated for use in pressure-based footstep recognition when the limited sample size is available for person authentication. The results show that the feature learning networks (90.6% accuracy) achieved significantly better performance on average than the conventional feature extraction methods (79.7% accuracy) (p < 0.05). Among the different feature networks, PCANet provided the best verification performance, with an accuracy of 92.2%. Feature learning networks are simple and effective approaches that can be a promising solution for applications like floor-based gait recognition in a security access scenario (such as workspace environment and border control) when small amounts of data are available for training models to differentiate between a larger group of users.

Validity of an inertial sensor-based system for the assessment of spatio-temporal parameters in people with multiple sclerosis.

Gait variability in people with multiple sclerosis (PwMS) reflects disease progression or may be used to evaluate treatment response. To date, marker-based camera systems are considered as gold standard to analyze gait impairment in PwMS. These systems might provide reliable data but are limited to a restricted laboratory setting and require knowledge, time, and cost to correctly interpret gait parameters. Inertial mobile sensors might be a user-friendly, environment- and examiner-independent alternative. The purpose of this study was to evaluate the validity of an inertial sensor-based gait analysis system in PwMS compared to a marker-based camera system. A sample N = 39 PwMS and N = 19 healthy participants were requested to repeatedly walk a defined distance at three different self-selected walking speeds (normal, fast, slow). To measure spatio-temporal gait parameters (i.e., walking speed, stride time, stride length, the duration of the stance and swing phase as well as max toe clearance), an inertial sensor system as well as a marker-based camera system were used simultaneously. All gait parameters highly correlated between both systems (r > 0.84) with low errors. No bias was detected for stride time. Stance time was marginally overestimated (bias = -0.02 ± 0.03 s) and gait speed (bias = 0.03 ± 0.05 m/s), swing time (bias = 0.02 ± 0.02 s), stride length (0.04 ± 0.06 m), and max toe clearance (bias = 1.88 ± 2.35 cm) were slightly underestimated by the inertial sensors. The inertial sensor-based system captured appropriately all examined gait parameters in comparison to a gold standard marker-based camera system. Stride time presented an excellent agreement. Furthermore, stride length and velocity presented also low errors. Whereas for stance and swing time, marginally worse results were observed.

Stability of sensor-based gait parameters reassessed after a period of one year in people with multiple sclerosis

BackgroundCurrently, there are several studies showing that wearable inertial sensors are highly sensitive in the detection of gait disturbances in people with multiple sclerosis (PwMS), showing excellent reliability within one or 7–14 days. However, it is not known how stable these gait parameters remain over a longer period of time. This is surprising, because many treatments last longer than two weeks. Thus, the purpose of the current study was to examine gait parameters obtained by means of wearable inertial sensors during a 6-min walk and to reassess these parameters after a period of one year.MethodsFifty PwMS (without a relapse or a recent change in the Expanded Disability Status Scale (EDSS) or treatment) and 20 healthy participants were examined at two assessment points (interval between assessments: 14.4 ± 6.6 months). At each assessment point, all participants had to complete a 6-min walking test, an observer-rater test (Berg Balance Scale, BBS) and a Timed-up and Go Test (TUG). To measure mean gait parameters (i.e. walking speed, stride length, stride time, the duration of the stance and swing phase and minimum toe-to-floor distance), as well as the intraindividual standard deviation of each mean gait parameter, wearable inertial sensors were utilized.ResultsWe found that even after one year all mean gait parameters showed excellent Intraclass Correlation Coefficients (ICC between 0.75 and 0.95) in PwMS. Looking at MS subgroups, the ICCs were slightly higher in MS subgroup 2 (EDSS 2.0–5.0) than those in MS subgroup 1 (EDSS 0.0–1.5) and healthy controls. Compared to the mean gait parameters, parameters of gait variability showed only good-to-fair ICC values in PwMS. Concerning BBS and TUG, the ICC values after one year were close to the ICC values of the measured mean gait parameters.ConclusionsDue to the excellent stability of mean gait parameters after one year, these sensor-based gait parameters can be identified as clinically relevant markers to evaluate treatment effects over a longer (several months) period of time in MS.

Arm swing deviations in patients with Parkinson’s disease at different gait velocities

Asymmetry of arm swing (AS) has been described as a characteristic of normal physiological gait. In patients with Parkinson’s disease (PWPD), a one-sided reduction of AS can occur already as a prodromal symptom. There is limited evidence regarding AS in PWPD, but a growing interest in AS as a focus of exercise therapy. The differences of AS between 32 healthy subjects (HS) and 36 mildly-to-moderately impaired PWPD were assessed in overground walking at various gait speeds. Assessments were carried out with a sensor-based gait measurement system over a 40 m walk in very slow, slow, preferred, fast, and very fast gait speed. Longitudinal and AS kinematics were compared with ANOVA function and regression analysis. PWPD exhibited a one-sided reduction of AS compared to HS at normal, fast, and very fast walking. AS coordination, representing the timing of reciprocity of right and left AS, was reduced in PWPD in very slow and normal walking. With respect to leg movements, PWPD exhibited an increase in stride time variability in very slow gait. There were no group differences for cadence, stride length, and gait velocity. This study informs about the kinematics of AS at various gait velocities ranging from very slow to very fast in mildly-to-moderately impaired PWPD. Reduced one-sided AS can be considered as a very early sign of parkinsonian gait disturbance that precedes alterations of locomotive leg movements and improves at faster gait speeds.

Wearable sensor-based gait analysis to discriminate early Parkinson’s disease from essential tremor

BackgroundDifferentiating early-stage Parkinson's disease (PD) from essential tremor (ET) is challenging since they have some overlapping clinical features. Since early-stage PD may present with slight gait impairment and ET generally does not, gait analysis could be used to differentiate PD from ET using machine learning.ObjectiveTo differentiate early-stage PD from ET via machine learning using gait and postural transition parameters calculated using the raw kinematic signal captured from inertial measurement unit (IMU) sensors.MethodsGait and postural transition parameters were collected from 84 early-stage PD and 80 ET subjects during the Time Up and Go (TUG) test. We randomly split our data into training and test data. Within the training data, we separated the TUG test into four components: standing, straight walk, turning, and sitting to build weighted average ensemble classification models. The four components’ weight indices were trained using logistic regression. Several ensemble models’ leave-one-out cross-validation (LOOCV) performances were compared. Independent test data were used to evaluate the model with the best LOOCV performance.ResultsThe best weighted average ensemble classification model LOOCV results included an accuracy of 84%, Kappa of 0.68, sensitivity of 85.9%, specificity of 82.1%, and AUC of 0.912. Thirty-three gait and postural transition parameters, such as Arm–Symbolic Symmetry Index and 180° Turn–Max Angular Velocity, were included in Feature Group III. The independent test data achieved a 75.8% accuracy.ConclusionsOur findings suggest that gait and postural transition parameters obtained from wearable sensors combined with machine learning had the potential to distinguish between early-stage PD and ET.

Reliability and Validity of a Wearable Sensing System and Online Gait Analysis Report in Persons after Stroke.

The restoration of gait and mobility after stroke is an important and challenging therapy goal due to the complexity of the potentially impaired functions. As a result, precise and clinically feasible assessment methods are required for personalized gait rehabilitation after stroke. The aim of this study is to investigate the reliability and validity of a sensor-based gait analysis system in stroke survivors with different severities of gait deficits. For this purpose, 28 chronic stroke survivors (9 women, ages: 62.04 ± 11.68 years) with mild to moderate walking impairments performed a set of ambulatory assessments (3× 10MWT, 1× 6MWT per session) twice while being equipped with a sensor suit. The derived gait reports provided information about speed, step length, step width, swing and stance phases, as well as joint angles of the hip, knee, and ankle, which we analyzed for test-retest reliability and hypothesis testing. Further, test-retest reliability resulted in a mean ICC of 0.78 (range: 0.46-0.88) for walking 10 m and a mean ICC of 0.90 (range: 0.63-0.99) for walking 6 min. Additionally, all gait parameters showed moderate-to-strong correlations with clinical scales reflecting lower limb function. These results support the applicability of this sensor-based gait analysis system for individuals with stroke-related walking impairments.

An observational study to assess validity and reliability of smartphone sensor-based gait and balance assessments in multiple sclerosis: Floodlight GaitLab protocol.

Gait and balance impairments are often present in people with multiple sclerosis (PwMS) and have a significant impact on quality of life and independence. Gold-standard quantitative tools for assessing gait and balance such as motion capture systems and force plates usually require complex technical setups. Wearable sensors, including those integrated into smartphones, offer a more frequent, convenient, and minimally burdensome assessment of functional disability in a home environment. We developed a novel smartphone sensor-based application (Floodlight) that is being used in multiple research and clinical contexts, but a complete validation of this technology is still lacking. This protocol describes an observational study designed to evaluate the analytical and clinical validity of Floodlight gait and balance tests. Approximately 100 PwMS and 35 healthy controls will perform multiple gait and balance tasks in both laboratory-based and real-world environments in order to explore the following properties: (a) concurrent validity of the Floodlight gait and balance tests against gold-standard assessments; (b) reliability of Floodlight digital measures derived under different controlled gait and balance conditions, and different on-body sensor locations; (c) ecological validity of the tests; and (d) construct validity compared with clinician- and patient-reported assessments. The Floodlight GaitLab study (ISRCTN15993728) represents a critical step in the technical validation of Floodlight technology to measure gait and balance in PwMS, and will also allow the development of new test designs and algorithms.

Sensor-Based Gait Analysis for Parkinson’s Disease Prediction

Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease. This type of minimal infrastructure equipment helps in analyzing the Parkinson’s gait properties without affecting the common behavioral patterns during the clinical practices. Therefore, the Accelerometer Sensor-based Parkinson’s Disease Identification System (ASPDIS) is introduced with a kernel-based support vector machine classifier model to make an early prediction of the disease. consequently, the proposed classifier can easily predict various severity levels of Parkinson’s disease from the sensor data. The performance of the proposed classifier is compared against the existing models such as random forest, decision tree, and k-nearest neighbor classifiers respectively. As per the experimental observation, the proposed classifier has more capability to differentiate Parkinson’s from non-Parkinson patients depending upon the severity levels. Also, it is found that the model has outperformed the existing classifiers concerning prediction time and accuracy respectively.

Pose-Based Gait Analysis for Diagnosis of Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder that is more common in elderly people and affects motor control, flexibility, and how easily patients adapt to their walking environments. PD is progressive in nature, and if undetected and untreated, the symptoms grow worse over time. Fortunately, PD can be detected early using gait features since the loss of motor control results in gait impairment. In general, techniques for capturing gait can be categorized as computer-vision-based or sensor-based. Sensor-based techniques are mostly used in clinical gait analysis and are regarded as the gold standard for PD detection. The main limitation of using sensor-based gait capture is the associated high cost and the technical expertise required for setup. In addition, the subjects’ consciousness of worn sensors and being actively monitored may further impact their motor function. Recent advances in computer vision have enabled the tracking of body parts in videos in a markerless motion capture scenario via human pose estimation (HPE). Although markerless motion capture has been studied in comparison with gold-standard motion-capture techniques, it is yet to be evaluated in the prediction of neurological conditions such as PD. Hence, in this study, we extract PD-discriminative gait features from raw videos of subjects and demonstrate the potential of markerless motion capture for PD prediction. First, we perform HPE on the subjects using AlphaPose. Then, we extract and analyse eight features, from which five features are systematically selected, achieving up to 93% accuracy, 96% precision, and 92% recall in arbitrary views.

In-Home Older Adults' Activity Pattern Monitoring Using Depth Sensors: A Review.

The global population is aging due to many factors, including longer life expectancy through better healthcare, changing diet, physical activity, etc. We are also witnessing various frequent epidemics as well as pandemics. The existing healthcare system has failed to deliver the care and support needed to our older adults (seniors) during these frequent outbreaks. Sophisticated sensor-based in-home care systems may offer an effective solution to this global crisis. The monitoring system is the key component of any in-home care system. The evidence indicates that they are more useful when implemented in a non-intrusive manner through different visual and audio sensors. Artificial Intelligence (AI) and Computer Vision (CV) techniques may be ideal for this purpose. Since the RGB imagery-based CV technique may compromise privacy, people often hesitate to utilize in-home care systems which use this technology. Depth, thermal, and audio-based CV techniques could be meaningful substitutes here. Due to the need to monitor larger areas, this review article presents a systematic discussion on the state-of-the-art using depth sensors as primary data-capturing techniques. We mainly focused on fall detection and other health-related physical patterns. As gait parameters may help to detect these activities, we also considered depth sensor-based gait parameters separately. The article provides discussions on the topic in relation to the terminology, reviews, a survey of popular datasets, and future scopes.