MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System
This provides an affordable, non-invasive prosthetic solution for individuals with arm mobility impairments, though it appears incremental as it builds on existing EEG and DNN methods.
The authors tackled the problem of limited accessibility for non-invasive prosthetic arms by developing MindArm, a low-cost system using EEG signals and deep neural networks to control arm movements, achieving accuracies of 91% for idle, 85% for handshake, and 84% for cup pickup at a cost of $500-550.
Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices are often prohibitively expensive and costly to maintain, and (2) invasive solutions require high-risk, costly brain surgery, which can pose a health risk. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, an affordable, non-invasive neuro-driven prosthetic arm system. MindArm employs a deep neural network (DNN) to translate brain signals, captured by low-cost surface electroencephalogram (EEG) electrodes, into prosthetic arm movements. Utilizing an Open Brain Computer Interface and UDP networking for signal processing, the system seamlessly controls arm motion. In the compute module, we run a trained DNN model to interpret filtered micro-voltage brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. Experimental results from a fully functional prototype show high accuracy across three actions, with 91% for idle/stationary, 85% for handshake, and 84% for cup pickup. The system costs approximately $500-550, including $400 for the EEG headset and $100-150 for motors, 3D printing, and assembly, offering an affordable alternative for mind-controlled prosthetic devices.