Christopher Millar

About

Christopher Millar is a researcher at the forefront of human-robot interaction and biomedical signal processing, with a particular focus on advancing prosthetic and robotic hand control. His work centers on leveraging surface electromyography (sEMG) and deep learning to decode complex human motor intent. In his highly regarded 2021 paper, "LSTM Classification of Functional Grasps Using sEMG Data from Low-Cost Wearable Sensor," Millar demonstrated how long short-term memory networks can accurately classify everyday functional grasps from low-cost, wearable sensor data. This contribution is pivotal for translating natural human grasping patterns to anthropomorphic robotic hands, bridging the gap between affordable sensing technology and sophisticated control. While his most-cited work has garnered 4 citations, its impact lies in its practical, accessible approach to a traditionally resource-intensive problem. Millar’s research not only pushes the boundaries of neural-driven prosthetics but also emphasizes real-world applicability, making him a key figure in the development of intuitive, human-like robotic manipulation systems.

Research Focus

Key Achievements

Top Papers

1. 1
   LSTM Classification of Functional Grasps Using sEMG Data from Low-Cost Wearable Sensor

   4 citations · 2021

Key Collaborators

Contact & Links