Bring Gait Lab to Everyday Life: Gait Analysis in Terms of Activities of Daily Living

Chen, Diliang; Cai, Yi; Qian, Xiaoye; Ansari, Rahila; Xu, Wenyao; Chu, Kuang‐Han; Huang, Ming-Chun

doi:10.1109/jiot.2019.2954387

Cited by 53 publications

(29 citation statements)

References 48 publications

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“…This study complements previous studies in several ways: a) Rich testing data: The system was tested on a large group of participants with varying age and degree of gait impairment during steady and unsteady walking. Overall, the system's performance in identifying steps and estimating temporal gait parameters was comparable to the state-of-theart systems, which were often tested on a smaller and more homogenous group of participants [10], [17], [34].…”

Section: Resultsmentioning

confidence: 90%

Automatic gait analysis during steady and unsteady walking using a smartphone

Sher¹,

Langford²,

Dogger³

et al. 2021

Preprint

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Section: Resultsmentioning

confidence: 90%

Automatic gait analysis during steady and unsteady walking using a smartphone

Sher¹,

Langford²,

Dogger³

et al. 2021

Preprint

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show abstract

“…to describe spatiotemporal features of gaits accurately. Diliang Chen et al calculated 26 gait parameters referring to basic gait parameters, gait variability, gait symmetry, and turning gait parameters for behavior recognition (sitting, standing, walking, running, up/downstairs) to evaluate the performance of activities of daily living (Chen et al, 2020).…”

Section: Spatiotemporal Gait Variablesmentioning

confidence: 99%

“…We can capture more spontaneous sports information using wearable devices for gait analysis in intelligent healthcare. In previous studies, the motion data captured by the smartphone were transformed to describe users' daily exercise, to calculate the risk of fall, and to predict sports injuries using intelligent algorithms for improving individuals' health management; smart insole was applied to measure step frequency, plantar pressure, and gait events for daily health monitoring; textile sensor arrays recognized motion behaviors in real-time (Chen et al, 2020).…”

Section: Health Care-related Applications Daily Health Monitoringmentioning

confidence: 99%

Wearable Devices for Gait Analysis in Intelligent Healthcare

Liu

Zhao

Zheng

et al. 2021

Front. Comput. Sci.

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In this study, we review the role of wearable devices in tracking our daily locomotion. We discuss types of wearable devices that can be used, methods for gait analyses, and multiple healthcare-related applications aided by artificial intelligence. Impaired walking and locomotion are common resulting from injuries, degenerative pathologies, musculoskeletal disorders, and various neurological damages. Daily tracking and gait analysis are convenient and efficient approaches for monitoring human walking, where concreate and rich data can be obtained for examining our posture control mechanism during body movement and providing enhanced clinical pieces of evidence for diagnoses and treatments. Many sensors in wearable devices can help to record data of walking and running; spatiotemporal and kinematic variables can be further calculated in gait analysis. We report our previous works in gait analysis, discussing applications of wearable devices for detecting foot and ankle lesions, supporting surgeons in early diagnosis, and helping physicians with rehabilitation.

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“…More versatile, low-cost systems are represented by wearable sensors such as inertial motion units (IMUs) [ 6 ] or instrumented clothing [ 7 ]. Wearable sensors allow motion analysis in various environments.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters

Guaitolini

Petros

Prado

et al. 2021

Sensors

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Ageing, disease, and injuries result in movement defects that affect daily life. Gait analysis is a vital tool for understanding and evaluating these movement dysfunctions. In recent years, the use of virtual reality (VR) to observe motion and offer augmented clinical care has increased. Although VR-based methodologies have shown benefits in improving gait functions, their validity against more traditional methods (e.g., cameras or instrumented walkways) is yet to be established. In this work, we propose a procedure aimed at testing the accuracy and viability of a VIVE Virtual Reality system for gait analysis. Seven young healthy subjects were asked to walk along an instrumented walkway while wearing VR trackers. Heel strike (HS) and toe off (TO) events were assessed using the VIVE system and the instrumented walkway, along with stride length (SL), stride time (ST), stride width (SW), stride velocity (SV), and stance/swing percentage (STC, SWC%). Results from the VR were compared with the instrumented walkway in terms of detection offset for time events and root mean square error (RMSE) for gait features. An absolute offset between VR- and walkway-based data of (15.3 ± 12.8) ms for HS, (17.6 ± 14.8) ms for TOs and an RMSE of 2.6 cm for SW, 2.0 cm for SL, 17.4 ms for ST, 2.2 m/s for SV, and 2.1% for stance and swing percentage were obtained. Our findings show VR-based systems can accurately monitor gait while also offering new perspectives for VR augmented analysis.

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Bring Gait Lab to Everyday Life: Gait Analysis in Terms of Activities of Daily Living

Cited by 53 publications

References 48 publications

Automatic gait analysis during steady and unsteady walking using a smartphone

Automatic gait analysis during steady and unsteady walking using a smartphone

Wearable Devices for Gait Analysis in Intelligent Healthcare

Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters

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