Adaptive Rehabilitation Robot Stiffness Control

The stiffness coefficient is changed online during the exercise session based on the patient's performance. The controlling unit adjusts the stiffness coefficient dynamically - increasing it when the patient deviates from the reference trajectory and decreasing it when the patient follows the trajectory well. This dynamic adjustment allows the robotic device to adapt the level of assistance to the patient's real-time performance, resolving the contradiction between providing consistent guidance and adapting to patient needs.

The system continuously monitors the patient's position relative to the reference trajectory and uses this feedback to adjust the stiffness coefficient. The controlling unit receives position information from sensors and modifies the guidance force parameters accordingly. This closed-loop feedback mechanism enables the system to maintain reliable guidance while adapting to the patient's performance level, directly addressing the technical contradiction.

The guidance force is made dynamic by adjusting the stiffness coefficient based on patient performance. When the patient performs well (stays close to the reference trajectory), the stiffness coefficient decreases, reducing the guidance force and challenging the patient to maintain performance with less assistance. When the patient struggles (deviates from trajectory), the stiffness coefficient increases, providing stronger guidance force. This dynamic force adjustment resolves the contradiction between providing strong guidance and challenging the patient.

The system changes the stiffness coefficient parameter online during the exercise based on measured performance metrics. By modifying this key parameter, the robotic device can vary the guidance force level to match the patient's capability, ensuring neither excessive force (which would prevent challenge) nor insufficient force (which would provide inadequate guidance). This parameter change approach directly addresses the contradiction between force magnitude and patient challenge.

The robotic device performs the functions of the therapist by automatically adjusting the stiffness coefficient based on patient performance without requiring continuous therapist intervention. The system monitors patient position, calculates deviation from the reference trajectory, and autonomously modifies guidance parameters. This self-service capability allows the system to provide personalized adaptive therapy while enabling high-intensity, high-repetition training that would be difficult to deliver manually, resolving the contradiction between personalization and training intensity.

The patent replaces the manual mechanical interaction of therapist-patient with an automated robotic system. The robotic device uses sensors to detect patient position and a controlling unit to compute and apply appropriate guidance forces. This substitution of mechanical therapy with an automated control system enables both personalized adaptive guidance and high-intensity repetitive training, directly addressing the limitations of manual one-to-one interaction.

The system implements a feedback loop where the controlling unit continuously monitors patient position through sensors, compares it to the reference trajectory, and adjusts the stiffness coefficient accordingly. This feedback mechanism provides performance-based adaptation but requires increased control system complexity to process position data and modify guidance parameters in real-time, directly reflecting the trade-off between adaptability and system complexity.

The online adjustment of the stiffness coefficient parameter based on patient performance creates adaptive therapy but necessitates a more complex controlling unit capable of real-time parameter modification. The system must continuously calculate deviation metrics and translate them into appropriate parameter changes, increasing the computational and control complexity while achieving performance-based adaptation.