MyoBuddy

Abstract

Muscular dystrophy is a group of genetic diseases that cause the loss of muscles and hence weakening the muscle strength. A typical treatment for muscular dystrophy patients is routinely performing weight exercise to slow down the loss in muscles. Thus, we propose a system MyoBuddy to help both physical therapists and patients to keep track of the weights in workout activities based on electromyography (EMG) sensors embedded in Myo armband. In our study, we collect 102 sessions of EMG data from barbell bicep curl exercise with a range of weights from 20 to 70 lbs with a 10-pound increment. Both Support Vector Machine and Random Forest algorithms are explored to classify which weight of barbells are lifted. At the end, we achieve 77.1% classification accuracy on average. † Equal contribution authors 1 The source of the statistics: https://www.cdc.gov/ ncbddd/musculardystrophy/research.html

A Framework and Call to Action for the Future Development of EMG-Based Input in HCI

“…With the release of an inexpensive commercially available sEMG device in 2014 [130], EMG became a viable option for broad use by HCI researchers. Since then, EMG has been leveraged for a wide range of general-purpose applications, such as: controlling drones [147], enhanced piano control [88], sign language recognition [116], RC car control [92], spelling systems [158], interactions with games [173], and augmenting gym experiences [72], to name a few. However, while EMG-based control has become accessible to the HCI community and is continuing to grow in popularity, it has yet to make significant strides toward being used in real-world generalpurpose applications.…”