CHING-YI TSAI

In strength training, the workload is a crucial element influencing the effectiveness of the training. Traditional methods of measuring workload, such as using instrumented dumbbells or manually counting repetitions, can make tracking user performance and progress tedious. To streamline this process, I contributed to the development of MuscleSense. This project utilizes wearable Myo sensors to gather surface electromyography (sEMG) data during strength exercises. My role was to create algorithms for smoothing, rectifying, and classifying the sEMG data, enabling the automatic and real-time recording and analysis of strength training sessions. Our findings and methodologies were published in a TEI 2020 paper.