Chung, Sang Hun Lee, Jong Min Kim, Seung-Jong Hwang, Yoha An, Jinung 2024-01-19T11:40:10Z 2024-01-19T11:40:10Z 2022-03-07 2015 2159-6255 https://pubs.kist.re.kr/handle/201004/115062 This paper presents a framework for classifying sit-to-stand and stand-to-sit from just two channel EMG signals taken from the left leg. Our proposed framework uses linear discriminant analysis (LDA) as the classifier and a multi-window feature extraction approach termed Consecutive Time-Windowed Feature Extraction (CTFE). We present the prelimnary results from 2 healthy subjects as a proof of concept. With the two tested subjects, we got predictive accuracies above 90%. The results show promise for a framework capable of recognizing the user's intention of sit-to-stand and stand-to-sit. Potential applications include rehabilitation robots for hemiparesis patients and exoskeleton control. English IEEE Intention Recognition Method for Sit-to-Stand and Stand-to-Sit from Electromyogram Signals for Overground Lower-Limb Rehabilitation Robots Conference 1 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp.418 - 421 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) 418 421 US Busan, SOUTH KOREA 2015-07-07 2015 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM) 000381493900071 2-s2.0-84951109489 Proceedings Paper