How Visual Communication Led to a New EMG-Driven Robotic Prosthesis Controller

• Activity Number: 111 - Let the Data Tell the Story: Principles of Visual Communication for Effective Collaboration
• Type: Invited
• Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Committee on Applied Statisticians
• Author(s): Jon Stallrich*
• Companies: North Carolina State
• Keywords: Relaxed Lasso; Adaptive Lasso; Group Lasso; Latent variables

Abstract

Robotic hand prostheses require a controller to decode muscle con- traction information, such as electromyogram (EMG) signals, into the user’s desired hand movement. State-of-the-art decoders demand extensive training, require data from a large number of EMG sensors, and are prone to poor predictions. Biomechanical models of a single movement degree-of-freedom tell us that relatively few muscles, and hence fewer EMG sensors, are needed to predict movement. This presentation tells the story of how effective collaboration and data visualization led to a novel decoder based on a dynamic, functional linear model having the recent past EMG signals as functional covariates. The model is estimated with a multi-stage, adaptive estimation procedure we call Sequential Adaptive Functional Estimation (SAFE). Starting with 16 potential EMG sensors, our method correctly identifies the few EMG signals that are known to be important for an able-bodied subject. Furthermore, the estimated effects are interpretable and can significantly improve understanding and development of robotic hand prostheses.