scholarly journals Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

2020 ◽  
Vol 10 (23) ◽  
pp. 8451
Author(s):  
Andrius Apsega ◽  
Liudvikas Petrauskas ◽  
Vidmantas Alekna ◽  
Kristina Daunoraviciene ◽  
Viktorija Sevcenko ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Sensitivity And Specificity ◽  
Gait Speed ◽  
Stance Phase ◽  
Wearable Sensors ◽  
Stride Time ◽  
Double Support ◽  
Phase Time ◽  
Gait Assessment ◽  
Gait Parameters

Background and objectives: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail). Materials and methods: 133 participants (≥60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters were assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19 s), stance phase time (0.68 s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity. Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.

scholarly journals Wearable Sensors Technology as a Tool for Discriminating Frailty Levels During Instrumented Gait Analysis

2020 ◽  
Author(s):  
Andrius Apsega ◽  
Liudvikas Petrauskas ◽  
Vidmantas Alekna ◽  
Kristina Daunoraviciene ◽  
Viktorija Sevcenko ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Gait Speed ◽  
Stance Phase ◽  
Wearable Sensors ◽  
Logistic Models ◽  
Stride Time ◽  
Double Support ◽  
Phase Time ◽  
Gait Assessment ◽  
Gait Parameters

Abstract Background: One of the greatest challenges facing the healthcare of the aging population is frailty. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening. The purpose of this study was to examine the ability of a wearable sensor-based assessment of gait to discriminate between frailty levels (robust, prefrail, and frail).Methods: 133 participants (≥ 60 years) were recruited and frailty was assessed using the Fried criteria. Gait was assessed using wireless inertial sensors attached by straps on the thighs, shins, and feet. Between-group differences in frailty were assessed using analysis of variance. Associations between frailty and gait parameters was assessed using multinomial logistic models with frailty as the dependent variable. We used receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index.Results: Frailty was identified in 37 (28%) participants, prefrailty in 66 (50%), and no Fried criteria were found in 30 (23%) participants. Gait speed, stance phase time, swing phase time, stride time, double support time, and cadence were able to discriminate frailty from robust, and prefrail from robust. Stride time (AUC = 0.915), stance phase (AUC = 0.923), and cadence (AUC = 0.930) were the most sensitive parameters to separate frail or prefrail from robust. Other gait parameters, such as double support, had poor sensitivity. We determined the value of stride time (1.19s), stance phase time (0.68s), and cadence (101 steps/min) to identify individuals with prefrailty or frailty with sufficient sensitivity and specificity.Conclusions: The results of our study show that gait analysis using wearable sensors could discriminate between frailty levels. We were able to identify several gait indicators apart from gait speed that distinguish frail or prefrail from robust with sufficient sensitivity and specificity. If improved and adapted for everyday use, gait assessment technologies could contribute to frailty screening and monitoring.

scholarly journals The parameters of gait analysis related to ambulatory and balance functions in hemiplegic stroke patients: a gait analysis study

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Min Cheol Chang ◽  
Byung Joo Lee ◽  
Na-Young Joo ◽  
Donghwi Park
Keyword(s):  
Gait Analysis ◽  
Lower Limb ◽  
Gait Speed ◽  
Stance Phase ◽  
Pressure Sensors ◽  
Stroke Patients ◽  
Unaffected Side ◽  
Rehabilitative Treatment ◽  
Phase Time ◽  
Balance Functions

Abstract Background Ambulatory and balance functions are important for maintaining general health in humans. Gait analysis allows clinicians and researchers to identify the parameters to be focused on when assessing balance and ambulatory functions. In this study, we performed gait analysis with pressure sensors to identify the gait-analysis parameters related to balance and ambulatory functions in hemiplegic stroke patients. Methods We retrospectively reviewed the medical records of 102 patients with hemiplegic stroke who underwent gait analysis. Correlations between various temporospatial parameters in the gait analysis and the motor and balance functions assessed using functional ambulation category, modified Barthel index, and Berg balance scale were analyzed. Results Gait speed/height and the lower-limb stance-phase time/height were the only temporal and spatial parameters, respectively, that showed a statistical correlation with motor and balance functions. Conclusions Measurements of walking speed and stance-phase time of the unaffected lower limb can allow clinicians to easily assess the ambulatory and balance functions of hemiplegic stroke patients. Rehabilitative treatment focusing on increasing gait speed and shortening the stance-phase time of the unaffected side may improve the ambulatory and balance functions in these patients.

Digital Biomarkers of Cognitive Frailty: The Value of Detailed Gait Assessment Beyond Gait Speed

Gerontology ◽  
2021 ◽  
pp. 1-10
Author(s):  
He Zhou ◽  
Catherine Park ◽  
Mohammad Shahbazi ◽  
Michele K. York ◽  
Mark E. Kunik ◽  
...  
Keyword(s):  
Older Adults ◽  
Steady State ◽  
Dual Task ◽  
Gait Speed ◽  
Stride Length ◽  
Cognitive Frailty ◽  
Single Task ◽  
Double Support ◽  
Gait Parameters ◽  
Digital Biomarkers

<b><i>Background:</i></b> Cognitive frailty (CF), defined as the simultaneous presence of cognitive impairment and physical frailty, is a clinical symptom in early-stage dementia with promise in assessing the risk of dementia. The purpose of this study was to use wearables to determine the most sensitive digital gait biomarkers to identify CF. <b><i>Methods:</i></b> Of 121 older adults (age = 78.9 ± 8.2 years, body mass index = 26.6 ± 5.5 kg/m<sup>2</sup>) who were evaluated with a comprehensive neurological exam and the Fried frailty criteria, 41 participants (34%) were identified with CF and 80 participants (66%) were identified without CF. Gait performance of participants was assessed under single task (walking without cognitive distraction) and dual task (walking while counting backward from a random number) using a validated wearable platform. Participants walked at habitual speed over a distance of 10 m. A validated algorithm was used to determine steady-state walking. Gait parameters of interest include steady-state gait speed, stride length, gait cycle time, double support, and gait unsteadiness. In addition, speed and stride length were normalized by height. <b><i>Results:</i></b> Our results suggest that compared to the group without CF, the CF group had deteriorated gait performances in both single-task and dual-task walking (Cohen’s effect size <i>d</i> = 0.42–0.97, <i>p</i> &#x3c; 0.050). The largest effect size was observed in normalized dual-task gait speed (<i>d</i> = 0.97, <i>p</i> &#x3c; 0.001). The use of dual-task gait speed improved the area under the curve (AUC) to distinguish CF cases to 0.76 from 0.73 observed for the single-task gait speed. Adding both single-task and dual-task gait speeds did not noticeably change AUC. However, when additional gait parameters such as gait unsteadiness, stride length, and double support were included in the model, AUC was improved to 0.87. <b><i>Conclusions:</i></b> This study suggests that gait performances measured by wearable sensors are potential digital biomarkers of CF among older adults. Dual-task gait and other detailed gait metrics provide value for identifying CF above gait speed alone. Future studies need to examine the potential benefits of gait performances for early diagnosis of CF and/or tracking its severity over time.

The Feasibility and Effectiveness of Wearable Sensor Technology in the Management of Elderly Diabetics with Foot Ulcer Remission: A Proof-Of-Concept Pilot Study with Six Cases

Gerontology ◽  
2021 ◽  
pp. 1-10
Author(s):  
Chenzhen Du ◽  
Hongyan Wang ◽  
Heming Chen ◽  
Xiaoyun Fan ◽  
Dongliang Liu ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Stride Length ◽  
Wearable Sensors ◽  
Foot Ulcer ◽  
Wearable Devices ◽  
Foot Ulcers ◽  
Diabetic Patients ◽  
Wearable Sensor ◽  
Double Support ◽  
Gait Parameters

Aims: Using specials wearable sensors, we explored changes in gait and balance parameters, over time, in elderly patients at high risk of diabetic foot, wearing different types of footwear. This assessed the relationship between gait and balance changes in elderly diabetic patients and the development of foot ulcers, in a bid to uncover potential benefits of wearable devices in the prognosis and management of the aforementioned complication. Methods: A wearable sensor-based monitoring system was used in middle-elderly patients with diabetes who recently recovered from neuropathic plantar foot ulcers. A total of 6 patients (age range: 55–80 years) were divided into 2 groups: the therapeutic footwear group (n = 3) and the regular footwear (n = 3) group. All subjects were assessed for gait and balance throughout the study period. Walking ability and gait pattern were assessed by allowing participants to walk normally for 1 min at habitual speed. The balance assessment program incorporated the “feet together” standing test and the instrumented modified Clinical Test of Sensory Integration and Balance. Biomechanical information was monitored at least 3 times. Results: We found significant differences in stride length (p < 0.0001), stride velocity (p < 0.0001), and double support (p < 0.0001) between the offloading footwear group (OG) and the regular footwear group on a group × time interaction. The balance test embracing eyes-open condition revealed a significant difference in Hip Sway (p = 0.004), COM Range ML (p = 0.008), and COM Position (p = 0.004) between the 2 groups. Longitudinally, the offloading group exhibited slight improvement in the performance of gait parameters over time. The stride length (odds ratio 3.54, 95% CI 1.34–9.34, p = 0.018) and velocity (odds ratio 3.13, 95% CI 1.19–8.19, p = 0.033) of OG patients increased, converse to the double-support period (odds ratio 6.20, 95% CI 1.97–19.55, p = 0.002), which decreased. Conclusions: Special wearable devices can accurately monitor gait and balance parameters in patients in real time. The finding reveals the feasibility and effectiveness of advanced wearable sensors in the prevention and management of diabetic foot ulcer and provides a solid background for future research. In addition, the development of foot ulcers in elderly diabetic patients may be associated with changes in gait parameters and the nature of footwear. Even so, larger follow-up studies are needed to validate our findings.

scholarly journals Assessing the concurrent validity of a gait analysis system integrated into a smart walker in older adults with gait impairments

2019 ◽  
Vol 33 (10) ◽  
pp. 1682-1687 ◽  
Author(s):  
Christian Werner ◽  
Georgia Chalvatzaki ◽  
Xanthi S Papageorgiou ◽  
Costas S Tzafestas ◽  
Jürgen M Bauer ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Gait Speed ◽  
Concurrent Validity ◽  
Stride Length ◽  
Gait Parameters ◽  
Stance Time ◽  
Absolute Agreement ◽  
Analysis System ◽  
Smart Walker ◽  
Gait Impairments

Objective: To assess the concurrent validity of a smart walker–integrated gait analysis system with the GAITRite® system for measuring spatiotemporal gait parameters in potential users of the smart walker. Design: Criterion standard validation study. Setting: Research laboratory in a geriatric hospital. Participants: Twenty-five older adults (⩾65 years) with gait impairments (habitual rollator use and/or gait speed <0.6 m/s) and no severe cognitive impairment (Mini-Mental State Examination ⩾17). Main measures: Stride, swing and stance time; stride length; and gait speed were simultaneously recorded using the smart walker–integrated gait analysis system and the GAITRite system while participants walked along a 7.8-m walkway with the smart walker. Concurrent criterion-related validity was assessed using the Bland–Altman method, percentage errors (acceptable if <30%), and intraclass correlation coefficients for consistency (ICC3,1) and absolute agreement (ICC2,1). Results: Bias for stride, swing and stance time ranged from −0.04 to 0.04 seconds, with acceptable percentage errors (8.7%–23.0%). Stride length and gait speed showed higher bias (meanbias (SD) = 0.20 (0.11) m; 0.19 (0.13) m/s) and not acceptable percentage errors (31.3%–42.3%). Limits of agreement were considerably narrower for temporal than for spatial-related gait parameters. All gait parameters showed good-to-excellent consistency (ICC3,1 = 0.72–0.97). Absolute agreement was good-to-excellent for temporal (ICC2,1 = 0.72–0.97) but only poor-to-fair for spatial-related gait parameters (ICC2,1 = 0.37–0.52). Conclusion: The smart walker–integrated gait analysis system has good concurrent validity with the GAITRite system for measuring temporal but not spatial-related gait parameters in potential end-users of the smart walker. Stride length and gait speed can be measured with good consistency, but with only limited absolute accuracy.

scholarly journals Mild Gait Impairment and Its Potential Diagnostic Value in Patients with Early-Stage Parkinson’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuang Wu ◽  
Xu Jiang ◽  
Min Zhong ◽  
Bo Shen ◽  
Jun Zhu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Wearable Sensors ◽  
Diagnostic Value ◽  
Gait Pattern ◽  
Heel Strike ◽  
Phase Time ◽  
Gait Parameters ◽  
Gait Impairments

Background and Purpose. Patients with early-stage Parkinson’s disease (PD) have gait impairments, and gait parameters may act as diagnostic biomarkers. We aimed to (1) comprehensively quantify gait impairments in early-stage PD and (2) evaluate the diagnostic value of gait parameters for early-stage PD. Methods. 32 patients with early-stage PD and 30 healthy control subjects (HC) were enrolled. All participants completed the instrumented stand and walk test, and gait data was collected using wearable sensors. Results. We observed increased variability of stride length (SL) ( P < 0.001 ), stance phase time (StPT) ( P = 0.004 ), and swing phase time (SwPT) ( P = 0.011 ) in PD. There were decreased heel strike (HS) ( P = 0.001 ), range of motion of knee ( P = 0.036 ), and hip joints ( P < 0.001 ) in PD. In symmetry analysis, no difference was found in any of the assessed gait parameters between HC and PD. Only total steps ( AUC = 0.763 , P < 0.001 ), SL ( AUC = 0.701 , P = 0.007 ), SL variability ( AUC = 0.769 , P < 0.001 ), StPT variability ( AUC = 0.712 , P = 0.004 ), and SwPT variability ( AUC = 0.688 , P = 0.011 ) had potential diagnostic value. When these five gait parameters were combined, the predictive power was found to increase, with the highest AUC of 0.802 ( P < 0.001 ). Conclusions. Patients with early-stage PD presented increased variability but still symmetrical gait pattern. Some specific gait parameters can be applied to diagnose early-stage PD which may increase diagnosis accuracy. Our findings are helpful to improve patient’s quality of life.

scholarly journals Instrumented gait analysis defines the walking signature of CACNA1A disorders

2021 ◽  
Author(s):  
Elisabetta Indelicato ◽  
Cecilia Raccagni ◽  
Sarah Runer ◽  
Julius Hannink ◽  
Wolfgang Nachbauer ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Wearable Sensors ◽  
Step Length ◽  
Ankle Motion ◽  
Assessment Protocol ◽  
Gait Parameters ◽  
Ambulatory Patients ◽  
Gait Disturbances ◽  
Specific Scale ◽  
Instrumented Gait Analysis

Abstract Background Gait disturbances are a frequent symptom in CACNA1A disorders. Even though, data about their severity and progression are lacking and no CACNA1A-specific scale or assessment for gait is available. Methods We applied a gait assessment protocol in 20 ambulatory patients with genetically confirmed CACNA1A disorders and 39 matched healthy controls. An instrumented gait analysis (IGA) was performed by means of wearable sensors in basal condition and after a treadmill/cycloergometer challenge in selected cases. Results CACNA1A patients displayed lower gait speed, shorter steps with increased step length variability, a reduced landing acceleration as well as a reduced range of ankle motion compared to controls. Furthermore, gait-width in patients with episodic CACNA1A disorders was narrower as compared to controls. In one patient experiencing mild episodic symptoms after the treadmill challenge, the IGA was able to detect a deterioration over all gait parameters. Conclusions In CACNA1A patients, the IGA with wearable sensors unravels specific gait signatures which are not detectable at naked eye. These features (narrow-based gait, lower landing acceleration) distinguish these patients from other ataxic disorders and may be target of focused rehabilitative interventions. IGA can potentially be applied to monitor the neurological fluctuations associated with CACNA1A disorders.

scholarly journals Synchronized Sensor Insoles for Clinical Gait Analysis in Home-Monitoring Applications

2018 ◽  
Vol 4 (1) ◽  
pp. 433-437 ◽  
Author(s):  
Nils Roth ◽  
Christine F. Martindale ◽  
Bjoern M. Eskofier ◽  
Heiko Gaßner ◽  
Zacharias Kohl ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Pressure Sensors ◽  
Home Monitoring ◽  
Fully Integrated ◽  
Double Support ◽  
Timing Offset ◽  
System A ◽  
Gait Parameters ◽  
Double Support Time ◽  
Clinical Gait Analysis

AbstractWearable sensor systems are of increasing interest in clinical gait analysis. However, little information about gait dynamics of patients under free living conditions is available, due to the challenges of integrating such systems unobtrusively into a patient’s everyday live. To address this limitation, new, fully integrated low power sensor insoles are proposed, to target applications particularly in home-monitoring scenarios. The insoles combine inertial as well as pressure sensors and feature wireless synchronization to acquire biomechanical data of both feet with a mean timing offset of 15.0 μs. The proposed system was evaluated on 15 patients with mild to severe gait disorders against the GAITRite® system as reference. Gait events based on the insoles’ pressure sensors were manually extracted to calculate temporal gait features such as double support time and double support. Compared to the reference system a mean error of 0.06 s ±0.06 s and 3.89 % ±2.61 % was achieved, respectively. The proposed insoles proved their ability to acquire synchronized gait parameters and address the requirements for home-monitoring scenarios, pushing the boundaries of clinical gait analysis.

scholarly journals Women after Bilateral Surgical Correction of Hallux Valgus Do not Show Improvement in Spatiotemporal Gait Parameters at 18 Weeks Postoperatively

2021 ◽  
Vol 10 (4) ◽  
pp. 608
Author(s):  
Katarzyna Kaczmarczyk ◽  
Gabor J. Barton ◽  
Ida Wiszomirska ◽  
Michal Wychowanski
Keyword(s):  
Hallux Valgus ◽  
Surgical Correction ◽  
Control Group ◽  
Stride Time ◽  
Leg Length ◽  
Control Groups ◽  
Postoperative Rehabilitation ◽  
Double Support ◽  
Gait Parameters ◽  
And Control

Background: Hallux valgus (HV) is a gait-altering orthopedic deformity, somewhat more prevalent in women, which often affects both limbs. Although surgery is a commonly applied treatment, there is no consensus in the literature on how invasive HV correction affects spatiotemporal gait parameters, or how quickly improvement can be expected. We investigated gait parameters in female HV patients who underwent bilateral surgical correction of hallux valgus, both preoperatively and 18 weeks following surgery (a timeframe relevant from the perspective of physical therapy), and also in relation to a non-HV control group. Methods: A total of 23 women aged 40–70 years, with moderate to severe HV deformity in both feet, were assessed preoperatively and 18 weeks postoperatively, and an age-matched control group of 76 healthy women was also assessed. A total of 22 spatiotemporal parameters were collected during 30 s walks over an electronic walkway (Zebris Medical System). Results: Of the 22 parameters analyzed, significant differences between the preoperative experimental and control groups were found only in 4 parameters (Velocity, Right step time, Total double support and Stride time), but in 16 parameters between the postoperative experimental and control groups (the greatest impact being found for: Left and Right Step time, Stride time, Cadence, Right Foot rotation, Left Step length (% leg length) and Stride length (% leg length)). Conclusions: Women after bilateral HV correction did not exhibit improved (i.e., more normal) gait parameters at 18 weeks postoperatively; rather, they showed more gait abnormalities than preoperatively. These findings urge longer-term planning of postoperative rehabilitation, involving continual evaluation of gait improvement.

scholarly journals Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sungmoon Jeong ◽  
Hosang Yu ◽  
Jaechan Park ◽  
Kyunghun Kang
Keyword(s):  
Deep Learning ◽  
Gait Analysis ◽  
Normal Pressure Hydrocephalus ◽  
Learning Algorithms ◽  
Normal Pressure ◽  
Gait Variability ◽  
Stride Time ◽  
Analysis Method ◽  
Gait Parameters ◽  
Analysis System

AbstractA vision-based gait analysis method using monocular videos was proposed to estimate temporo-spatial gait parameters by leveraging deep learning algorithms. This study aimed to validate vision-based gait analysis using GAITRite as the reference system and analyze relationships between Frontal Assessment Battery (FAB) scores and gait variability measured by vision-based gait analysis in idiopathic normal pressure hydrocephalus (INPH) patients. Gait data from 46 patients were simultaneously collected from the vision-based system utilizing deep learning algorithms and the GAITRite system. There was a strong correlation in 11 gait parameters between our vision-based gait analysis method and the GAITRite gait analysis system. Our results also demonstrated excellent agreement between the two measurement systems for all parameters except stride time variability after the cerebrospinal fluid tap test. Our data showed that stride time and stride length variability measured by the vision-based gait analysis system were correlated with FAB scores. Vision-based gait analysis utilizing deep learning algorithms can provide comparable data to GAITRite when assessing gait dysfunction in INPH. Frontal lobe functions may be associated with gait variability measurements using vision-based gait analysis for INPH patients.

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