The seamless operation of modern, powered exoskeletons relies heavily on the intelligent fusion of Sensors and AI to accurately predict and respond to user intent. Kinematic sensors, such as rotary encoders and Inertial Measurement Units (IMUs), track joint positions and movement velocity. Simultaneously, physiological sensors, like Electromyography (EMG) electrodes, detect residual muscle activity, providing an early signal of the user's desire to move.

Artificial intelligence, particularly deep learning models like transformer networks, processes these diverse sensory inputs—sometimes even integrating data from visual sensors or Brain-Machine Interfaces—to achieve highly adaptive control. This AI-driven approach allows the system to recognize complex transitions, such as gait initiation, stair climbing, or standing up, and immediately apply the necessary, personalized assistance torque. This automation of intention recognition is the key to creating devices that feel intuitive and natural, improving safety and reducing the cognitive load on the wearer.

Related Reports