Establishing the waist as the better location for attaching a single accelerometer to estimate center of pressure trajectories

Chen, Vincent C.F.; Chen, Shih-Wei

doi:10.1016/j.clinbiomech.2018.10.003

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…It recorded g-force measurements within ±8 g along each axis, maintaining a data sampling rate of up to 100 Hz. The phones were securely attached at the waist [ 17 ] with an athletic-style elastic belt enclosure, positioned approximately at the level of the lumbosacral junction. The raw accelerometer signal was synchronized with video recordings captured by a GoPro camera at a rate of 30 Hz.…”

Section: Materials and Methodsmentioning

confidence: 99%

Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach

Ramli,

Liu,

Berndt

et al. 2024

Sensors

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Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant’s level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body’s center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson’s r = −0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual’s stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

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Section: Materials and Methodsmentioning

confidence: 99%

Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach

Ramli,

Liu,

Berndt

et al. 2024

Sensors

View full text Add to dashboard Cite

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Benchmarking Learning Networks on Eat-Sleep Conditions

Parque

Obasekore

Oladayo

et al. 2019

2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech)

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Evaluation of Anticipatory Postural Adjustment before Quantified Weight Shifting—System Development and Reliability Test

Liaw

Chen

et al. 2021

Applied Sciences

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Anticipatory postural adjustment (APA) existed before a self-induced perturbation is an important motor control skill for balance and gait initiation, but cannot be easily monitored. During proactive balance test, a self-initiated weight shifting is produced. This might be an optimal paradigm for APA measurement. The purpose of this study was to investigate if APAs existed in the proactive balance test which consists of quantifiable weight shifting. The feature and reliability of the APAs were also evaluated. We firstly built a proactive balance test program on the commercially available Wii balance board. The program could generate adjustable target direction and distance for guiding subjects performing quantifiable weight shifting. The center of pressure (COP) was recorded and analyzed for balance-related variables (path length, path time, and direction error) and APA-related variables (APA time, APA distance, and APA correction). The results showed that APAs could be detected in every testing trial. Adequate to good reliability in both balance and APA-related variables were found. This study proved the feasibility of quantifying APA during proactive balance tests and its feasibility for clinical- and home-based measurements.

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Establishing the waist as the better location for attaching a single accelerometer to estimate center of pressure trajectories

Cited by 3 publications

References 43 publications

Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach

Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach

Benchmarking Learning Networks on Eat-Sleep Conditions

Evaluation of Anticipatory Postural Adjustment before Quantified Weight Shifting—System Development and Reliability Test

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