ArticleSensors (Basel, Switzerland)2022
Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Deployment-Oriented Evaluation of Lightweight IMU-Based Human Activity Recognition: On-Device Efficiency and Streaming Feasibility.Sensors (Basel, Switzerland) · 2026Article
- Sensing the Action: Rethinking Sensor Modalities and Multi-Modal Fusion in Vision-Language-Action Models for Robotic Manipulation.Sensors (Basel, Switzerland) · 2026Review
- Efficient and Dynamically Consistent Joint Torque Estimation for Wearable Neurotechnology via Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2026Article
- Derivation-Based Calibration of IMUs Using Savitzky-Golay Filters.Sensors (Basel, Switzerland) · 2026Article
- Prediction of Three-Dimensional Ground Reaction Forces in the Golf Swing Using Wearable Inertial Measurement Units and Biomimetic Deep Learning Models.Biomimetics (Basel, Switzerland) · 2026Article
- The application of robotic and artificial intelligence technologies in spinal surgery: a review focused on prospects in remote areas of China.Journal of robotic surgery · 2025Review
- Review of sEMG for Exoskeleton Robots: Motion Intention Recognition Techniques and Applications.Sensors (Basel, Switzerland) · 2025Review
- A Deep Learning-Based Framework Oriented to Pathological Gait Recognition with Inertial Sensors.Sensors (Basel, Switzerland) · 2025Article
- A Novel Active Learning Framework for Cross-Subject Human Activity Recognition from Surface Electromyography.Sensors (Basel, Switzerland) · 2024Article
- A Review of Wearable Optical Fiber Sensors for Rehabilitation Monitoring.Sensors (Basel, Switzerland) · 2024Review
- Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.Sensors (Basel, Switzerland) · 2024Article
- Action Recognition of Taekwondo Unit Actions Using Action Images Constructed with Time-Warped Motion Profiles.Sensors (Basel, Switzerland) · 2024Article
- A Novel Framework Based on Deep Learning Architecture for Continuous Human Activity Recognition with Inertial Sensors.Sensors (Basel, Switzerland) · 2024Article
- Biosignal-integrated robotic systems with emerging trends in visual interfaces: A systematic review.Biophysics reviews · 2024Review
- A Wearable Inertial Sensor Approach for Locomotion and Localization Recognition on Physical Activity.Sensors (Basel, Switzerland) · 2024Article
- Robust human locomotion and localization activity recognition over multisensory.Frontiers in physiology · 2024Article
- Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot's control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer's activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device.
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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.