ArticleSensors (Basel, Switzerland)2019
Optimizing the Scale of a Wavelet-Based Method for the Detection of Gait Events from a Waist-Mounted Accelerometer under Different Walking Speeds.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine Learning-Based Prognostic Prediction for Knee Osteoarthritis After High Tibial Osteotomy Using Wavelet-Derived Gait Features.Journal of functional morphology and kinesiology · 2026Article
- Lumbar Acceleration Gait Estimation: "Step-by-Step" Algorithm Updates and Improvements.Journal of medical Internet research · 2025Article
- Tug-vimu: a GoPro-based mobility assessment bridging the gap between technology and clinical practice.Journal of neuroengineering and rehabilitation · 2025Article
- Hierarchical clustering approach to movement smoothness and temporal adaptation in rhythmic step aerobics training in middle aged women.Scientific reports · 2025Article
- SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing.JMIR mHealth and uHealth · 2022Article
- Acceleration Gait Measures as Proxies for Motor Skill of Walking: A Narrative Review.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2021Review
- Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform.Sensors (Basel, Switzerland) · 2020Article
- Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals.Biomedical engineering online · 2020Article
- Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse's Limb.Sensors (Basel, Switzerland) · 2020Article
- Non-Linear Template-Based Approach for the Study of Locomotion.Sensors (Basel, Switzerland) · 2020Article
- Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
3 authors.
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Abstract
The accurate and reliable extraction of specific gait events from a single inertial sensor at waist level has been shown to be challenging. Among several techniques, a wavelet-based method for initial contact (IC) and final contact (FC) estimation was shown to be the most accurate in healthy subjects. In this study, we evaluated the sensitivity of events detection to the wavelet scale of the algorithm, when walking at different speeds, in order to optimize its selection. A single inertial sensor recorded the lumbar vertical acceleration of 20 subjects walking at three different self-selected speeds (slow, normal, and fast) in a motion analysis lab. The scale of the wavelet method was varied. ICs were generally accurately detected in a wide range of wavelet scales under all the walking speeds. FCs detection proved highly sensitive to scale choice. Different gait speeds required the selection of a different scale for accurate detection and timing, with the optimal scale being strongly correlated with subjects' step frequency. The best speed-dependent scales of the algorithm led to highly accurate timing in the detection of IC (RMSE < 22 ms) and FC (RMSE < 25 ms) across all speeds. Our results pave the way for the optimal adaptive selection of scales in future applications using this algorithm.
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