Wearable robots must adapt to both users and environments to ensure reliable and seamless functionality in diverse conditions. This adaptability requires the ability to respond to individual movement characteristics and varying external factors.
By utilizing advanced AI and machine learning techniques, we process biomechanical and physiological data to refine control strategies that enhance the overall performance of the system.
Additionally, this approach enables the estimation of gait patterns beyond the raw data directly obtained from sensors, providing a more comprehensive understanding of movement across a range of scenarios.

Each person has a different pattern of body movements, so tuning control parameters for each person are needed to maximize the robot's performance. Human-in-the-loop (HIL) optimization showed the possibility of obtaining the best control parameters for each individual in a well-refined environment (e.g., laboratory). For the extension of HIL in real life, we are studying advanced HIL to optimize the control parameters of wearable robots in mobile situations using human biomechanical signals acquired in real-time.