Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation

Journals

1. He F, Liu Y, Zhan W, Xu Q, Chen X. Manual Operation Evaluation Based on Vectorized Spatio-Temporal Graph Convolutional for Virtual Reality Training in Smart Grid. Energies 2022;15(6):2071 View
2. Li Y, Yang L, He Z, Liu Y, Wang H, Zhang W, Teng L, Chen D, Song G. Low‐Cost Data Glove Based on Deep‐Learning‐Enhanced Flexible Multiwalled Carbon Nanotube Sensors for Real‐Time Gesture Recognition. Advanced Intelligent Systems 2022;4(11) View
3. Qiu J, Li Y, Liu H, Lin S, Pang L, Sun G, Song Y. Research on motion recognition based on multi-dimensional sensing data and deep learning algorithms. Mathematical Biosciences and Engineering 2023;20(8):14578 View
4. Chen J, Wang J, Yuan Q, Yang Z. CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise. IEEE Journal of Translational Engineering in Health and Medicine 2023;11:351 View
5. Willingham T, Stowell J, Collier G, Backus D. Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities. International Journal of Environmental Research and Public Health 2024;21(1):79 View
6. Sassi M, Carnevale A, Mancuso M, Schena E, Pecchia L, Giuseppe Longo U. Classification of Shoulder Rehabilitation Exercises by Using Wearable Systems and Machine Learning Algorithms. IEEE Sensors Journal 2024;24(14):22934 View
7. Sassi M, Carnevale A, Mancuso M, Schena E, Pecchia L, Longo U. Machine‐learning models for shoulder rehabilitation exercises classification using a wearable system. Knee Surgery, Sports Traumatology, Arthroscopy 2024 View