ArticleSensors (Basel, Switzerland)2018
A Wearable Gait Phase Detection System Based on Force Myography Techniques.
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 22 papers.
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Who cites it
22 citing papers in PubMed.
- Article
- Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis.Sensors (Basel, Switzerland) · 2024Article
- A survey on the state of the art of force myography technique (FMG): analysis and assessment.Medical & biological engineering & computing · 2024Review
- Human Gait Activity Recognition Machine Learning Methods.Sensors (Basel, Switzerland) · 2023Article
- Techniques for the detection and management of freezing of gait in Parkinson's disease - A systematic review and future perspectives.MethodsX · 2023Article
- A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human-Machine Interactivities and Biomedical Applications.Biosensors · 2022Review
- Building Effective Machine Learning Models for Ankle Joint Power Estimation During Walking Using FMG Sensors.Frontiers in neurorobotics · 2022Article
- Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait.Frontiers in neuroscience · 2022Review
- Gait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion.Applied bionics and biomechanics · 2022Review
- Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition.Sensors (Basel, Switzerland) · 2021Article
- Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration.Sensors (Basel, Switzerland) · 2021Article
- A Survey of Human Gait-Based Artificial Intelligence Applications.Frontiers in robotics and AI · 2021Review
- Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units.Biosensors · 2020Article
- Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor.Sensors (Basel, Switzerland) · 2020Article
- A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses.Sensors (Basel, Switzerland) · 2020Review
- Wrist-worn wearables based on force myography: on the significance of user anthropometry.Biomedical engineering online · 2020Article
- Perspectives on the prospective development of stroke-specific lower extremity wearable monitoring technology: a qualitative focus group study with physical therapists and individuals with stroke.Journal of neuroengineering and rehabilitation · 2020Article
- Design of a Purely Mechanical Sensor-Controller Integrated System for Walking Assistance on an Ankle-Foot Exoskeleton.Sensors (Basel, Switzerland) · 2019Article
- Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals.Sensors (Basel, Switzerland) · 2019Article
- Estimation of Ankle Joint Power during Walking Using Two Inertial Sensors.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: Quantitative evaluation of gait parameters can provide useful information for constructing individuals’ gait profile, diagnosing gait abnormalities, and better planning of rehabilitation schemes to restore normal gait pattern. Objective determination of gait phases in a gait cycle is a key requirement in gait analysis applications; (2) Methods: In this study, the feasibility of using a force myography-based technique for a wearable gait phase detection system is explored. In this regard, a force myography band is developed and tested with nine participants walking on a treadmill. The collected force myography data are first examined sample-by-sample and classified into four phases using Linear Discriminant Analysis. The gait phase events are then detected from these classified samples using a set of supervisory rules; (3) Results: The results show that the force myography band can correctly detect more than 99.9% of gait phases with zero insertions and only four deletions over 12,965 gait phase segments. The average temporal error of gait phase detection is 55.2 ms, which translates into 2.1% error with respect to the corresponding labelled stride duration; (4) Conclusions: This proof-of-concept study demonstrates the feasibility of force myography techniques as viable solutions in developing wearable gait phase detection systems.
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Registered trials
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.