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to the detection of fall-risk situations during assisted gait with the team’s commercial exoskeleton, including sensor data processing and algorithm parameter tuning. ii) Developing and testing software modules
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algorithms (e.g., filtering, normalization, and event detection) to characterize human–robot interaction patterns, with a particular focus on detecting movement intention and potential risk situations. iv
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algorithms for classifying modes of locomotion and assessing fall risk; iv) integration and control of balance recovery through slip detection tools in a commercial exoskeleton, using ROS2, DMPs, CPGs, and
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; Develop algorithms that adjust the type of pedagogical scaffolding. The goal is always to guide the student without giving the answer, but the way of guiding will be the focus of the adaptation. Fine-tuning