ArticleSensors (Basel, Switzerland)2018
An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- eHealth tools to assess the neurological function for research, in absence of the neurologist: a systematic review, part II (hardware).Journal of neurology · 2025Pooled it
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- Development of an IMU-Based Post-Stroke Gait Data Acquisition and Analysis System for the Gait Assessment and Intervention Tool.Sensors (Basel, Switzerland) · 2025Article
- A multi-sensor approach to improve interpretability of the 6-min walk test as an outcome in muscular dystrophies: an observational study.Brain communications · 2025Article
- Quantitative analysis of gait parameters in Parkinson's disease and the clinical significance.Frontiers in neurology · 2025Article
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- A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors.Sensors (Basel, Switzerland) · 2024Article
- Machine learning approach to classifying declines of physical function and muscle strength associated with cognitive function in older women: gait characteristics based on three speeds.Frontiers in public health · 2024Article
- Comparing Inertial Measurement Units to Markerless Video Analysis for Movement Symmetry in Quarter Horses.Sensors (Basel, Switzerland) · 2023Article
- Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit.Sensors (Basel, Switzerland) · 2023Article
- An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Multidimensional Biomechanics-Based Score to Assess Disease Progression in Duchenne Muscular Dystrophy.Sensors (Basel, Switzerland) · 2023Article
- Comparing Loose Clothing-Mounted Sensors with Body-Mounted Sensors in the Analysis of Walking.Sensors (Basel, Switzerland) · 2022Article
- Method for Estimating Temporal Gait Parameters Concerning Bilateral Lower Limbs of Healthy Subjects Using a Single In-Shoe Motion Sensor through a Gait Event Detection Approach.Sensors (Basel, Switzerland) · 2022Article
- Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review.Frontiers in rehabilitation sciences · 2022Article
- Validation of a smartphone application and wearable sensor for measurements of wrist motions.The Journal of hand surgery, European volume · 2021Article
- Gait and Axial Spondyloarthritis: Comparative Gait Analysis Study Using Foot-Worn Inertial Sensors.JMIR mHealth and uHealth · 2021Article
- Joint Constraints Based Dynamic Calibration of IMU Position on Lower Limbs in IMU-MoCap.Sensors (Basel, Switzerland) · 2021Article
- Comparison of different symmetry indices for the quantification of dynamic joint angles.BMC sports science, medicine & rehabilitation · 2021Article
- The effects of a secondary task on gait in axial spondyloarthritis.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.