ArticleEntropy (Basel, Switzerland)2020
Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 55 citations in OpenAlex.
- In Vivo Assessment of Ankle Stability During Dynamic Exercises: Scoping Review.Healthcare (Basel, Switzerland) · 2025Review
- Surface Electromyography Combined with Artificial Intelligence in Predicting Neuromuscular Falls in the Elderly: A Narrative Review of Present Applications and Future Perspectives.Healthcare (Basel, Switzerland) · 2025Review
- Article
- Design and Testing of a Portable Wireless Multi-Node sEMG System for Synchronous Muscle Signal Acquisition and Gesture Recognition.Micromachines · 2025Article
- Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2025Article
- Article
- A multimodal framework based on deep belief network for human locomotion intent prediction.Biomedical engineering letters · 2024Article
- A survey on the state of the art of force myography technique (FMG): analysis and assessment.Medical & biological engineering & computing · 2024Review
- User- and Speed-Independent Slope Estimation for Lower-Extremity Wearable Robots.Annals of biomedical engineering · 2024Review
- Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform.Scientific reports · 2024Article
- The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals.Sensors (Basel, Switzerland) · 2024Article
- Multi-branch deep learning neural network prediction model for the development of angular biosensors based on sEMG.Frontiers in bioengineering and biotechnology · 2024Article
- Mechanomyography Signal Pattern Recognition of Knee and Ankle Movements Using Swarm Intelligence Algorithm-Based Feature Selection Methods.Sensors (Basel, Switzerland) · 2023Article
- Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study.Bioengineering (Basel, Switzerland) · 2023Article
- Soft pneumatic muscles for post-stroke lower limb ankle rehabilitation: leveraging the potential of soft robotics to optimize functional outcomes.Frontiers in bioengineering and biotechnology · 2023Article
- Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview.Biomedical engineering letters · 2022Review
- Electromyography Parameter Variations with Electrocardiography Noise.Sensors (Basel, Switzerland) · 2022Article
- CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning.Sensors (Basel, Switzerland) · 2022Article
- Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method.Sensors (Basel, Switzerland) · 2021Article
- Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.
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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.