Movement-Based Human-Machine System Using Arduino and Neural Network

Disabled people with quadriplegia caused by a cervical spinal cord injury, cerebral palsy, or stroke can greatly benefit from hands-free control methods to reduce their level of dependence on daily activities. In hands-free control solutions, the head movement-based control has proven to be the most efficient, natural, and comfortable way to convey desired commands to activate home appliances and access assistive devices. This study aims to develop a hands-free human-machine system (HMS) for quadriplegic people. The system is developed using an ADXL335 accelerometer and an Arduino Uno board, which is reasonably compatible with deploying a feedforward neural network (FFNN) for effectively detecting the intentional head movement.