ArticleSensors (Basel, Switzerland)2022
A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Deep Learning-Based Temporal Gait Analysis Using a Smartphone IMU in Older Adults with and Without Non-Specific Low Back Pain.Bioengineering (Basel, Switzerland) · 2026Article
- Whole body analysis of functional communities and topological features of gait with different speeds in Parkinson's disease.Journal of neurology · 2026Article
- Explainable and Subject-Independent VOSensors (Basel, Switzerland) · 2026Article
- Scoping review of AI-driven wearable technologies for rehabilitation and functional assessment in Parkinson's disease: a protocol.BMJ open · 2026Article
- Validation of Inertial Sensor-Based Step Detection Algorithms for Edge Device Deployment.Sensors (Basel, Switzerland) · 2026Article
- Accurate quantification of steps from multiple smartphone positions.Scientific reports · 2026Article
- Deep learning-based gait phase detection using shank-mounted IMU data: Classification approach.PloS one · 2026Article
- Gait Event Detection From Thigh Segment Kinematics in Children With Crouch Gait.IEEE journal of translational engineering in health and medicine · 2026Article
- Gait Event Detection and Gait Parameter Estimation from a Single Waist-Worn IMU Sensor.Sensors (Basel, Switzerland) · 2025Article
- Augmenting a ResNet + BiLSTM Deep Learning Model with Clinical Mobility Data Helps Outperform a Heuristic Frequency-Based Model for Walking Bout Segmentation.Sensors (Basel, Switzerland) · 2025Article
- Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones.Sensors (Basel, Switzerland) · 2025Article
- Determination of Spatiotemporal Gait Parameters Using a Smartphone's IMU in the Pocket: Threshold-Based and Deep Learning Approaches.Sensors (Basel, Switzerland) · 2025Article
- Retrospective Frailty Assessment in Older Adults Using Inertial Measurement Unit-Based Deep Learning on Gait Spectrograms.Sensors (Basel, Switzerland) · 2025Article
- Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors.Bioengineering (Basel, Switzerland) · 2025Article
- Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor.Sensors (Basel, Switzerland) · 2025Article
- Validity of Wearable Inertial Sensors for Gait Analysis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Enhancing Intelligent Shoes with Gait Analysis: A Review on the Spatiotemporal Estimation Techniques.Sensors (Basel, Switzerland) · 2024Review
- Mobile Spatiotemporal Gait Segmentation Using an Ear-Worn Motion Sensor and Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.The international journal of behavioral nutrition and physical activity · 2024Article
Corrections and comments
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (-0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.
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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.