W8-Scope: Fine-grained, practical monitoring of weight stack-based exercises

• Main Authors: RADHAKRISHNAN, Meeralakshmi, MISRA, Archan, BALAN, Rajesh Krishna
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Computer Sciences; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5102; https://ink.library.smu.edu.sg/context/sis_research/article/6105/viewcontent/2.W8_Scope_Fine_GrainedPracticalMonitoringWeightStack_BasedExercise_Percom2020_.pdf
• Institution: Singapore Management University

Fine-grained, unobtrusive monitoring of gym exercises can help users track their own exercise routines and also provide corrective feedback. We propose W8-Scope, a system that uses a simple magnetic-cum-accelerometer sensor, mounted on the weight stack of gym exercise machines, to infer various attributes of gym exercise behavior. More specifically, using multiple machine learning models, W8-Scope helps identify who is exercising, what exercise she is doing, how much weight she is lifting, and whether she is committing any common mistakes. Real world studies, conducted with 50 subjects performing 14 different exercises over 103 distinct sessions in two gyms, show that W8-Scope can achieve high accuracy–e.g., identify the weight used with an accuracy of 97.5%, detect commonplace mistakes with 96.7% accuracy and identify the user with 98.7% accuracy. Moreover, by adopting incremental learning techniques, W8-Scope can also accurately track these various facets of exercise over longitudinal periods, in spite of the inherent natural changes in a user’s exercising behavior.