Assistive devices in recent years have transitioned from a passive mode of operation to the integration of smart solutions that enable humans to interact with active and robotic platforms. The main problems in the evolution of this kind of device are accessibility in terms of price and the functional limitations of the smart integrated solutions. This project proposes an armrest prototype for integration into smart walkers or wheelchairs that can detect the user’s intentions at a low development cost. The smart principle of operation is based on Hall-effect sensors, strategically positioned to measure the Center of Pressure (CoP) of the user’s forearm and to classify motor intention using machine learning algorithms such as Random Forest and Leave-One-Subject-Out (LOSO). The detection and correct classification of the user’s intention is a tool that can be integrated as a control system for both motorized and passive assistive devices.
Cinardi et al. (Mon,) studied this question.