Wearable Sensors for Detecting and Measuring Kinetic Characteristics

Huang, Zihao; Lian, Jiakun

doi:10.1088/1742-6596/2174/1/012007

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…The potential of IMU's to gather kinematic data is well established in the literature with a many papers documenting the accurate measurement of spatiotemporal metrics and overall agreement with that obtained from laboratorybased techniques [25][26][27][28][29][30]. Other descriptive kinematic data such as joint angles are also possible with IMU's, yet they often require multiple sensors and extensive calibration [31]. which again, is not feasible in the fastpaced clinical environment.…”

Section: Wearable Sensorsmentioning

confidence: 99%

The Use of Spatiotemporal Gait Analysis in Diagnosing Pathologies: A Review

Fernando,

M Maharaj,

Koinis

et al. 2023

CCRS

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Background: Gait, particularly walking speed (WS), has emerged as an essential indicator of health. WS is indicative of current health and predicts future health trends, especially in older adults. Notably, a 0.1 m/s WS increase corresponds to a 12% rise in survival among this demographic, establishing WS as a powerful prognostic tool. This has resulted in the designation of WS as the "6th vital sign", which is applicable to a broad spectrum of medical conditions. Additionally, computerized gait analysis reveals nuanced differences in movement patterns across age groups. This review provides a detailed insight into the multifaceted nature of gait and its health implications. Purpose: This review's primary objective is to underscore the significance of gait and WS as pivotal health markers. By framing WS as the "6th vital sign" and delving into gait's complexities using digital analysis, the review aims to elucidate how gait metrics inform health trajectories in diverse medical scenarios. Methods: Our analysis employed laboratory-based three-dimensional gait techniques. Kinematic data were obtained using infrared markers on the body and triangulated with multiple cameras. Concurrently, force plates within electronic pathways captured kinetic data, such as ground reaction forces. Data collection was guided by a pre-established checklist encompassing specific conditions (including Parkinson’s Disease, Lumbar disk herniation, Chronic Mechanical Lower back pain, Lumbar Spinal Stenosis, Depression, Hip Osteoarthritis, COPD), analysis tools (e.g., type of cameras, force plates), kinematic and kinetic parameters (e.g., support moments, momentum), and potential psychological impacts on participants (e.g., Hawthorne and “white-coat” effects). The clinical significance of our data was validated against existing research on gait pattern variations in mentioned conditions, ensuring quality through stringent research standards. Conclusion: Spatiotemporal gait analysis, especially with machine learning application, is nascent. Although there's potential in its diagnostic capability, extensive research is needed for clinical use. Our focus was primarily on Parkinson’s Disease, aiming to gauge machine learning's role in discerning pathological from normal gait using spatiotemporal metrics. Future investigations should explore this approach for different gait-related conditions.

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