S. Dalla Gasperina
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3 records found
1
Objective: Hybrid systems that integrate Functional Electrical Stimulation (FES) and robotic assistance have been proposed in neurorehabilitation to enhance therapeutic benefits. This study focuses on designing a cooperative controller capable of distributing the required torque for movement between robotic actuation and FES, thereby eliminating the need for time-consuming calibration procedures. Methods: The control schema comprises three main blocks: a motion generation block that defines the desired trajectory, a motor control block including both a weight compensation feedforward and a feedback impedance controller, and an FES control block, based on trial-by-trial Iterative Learning Control (ILC), that adjusts the stimulation intensity according to a predefined stimulation waveform. The feedforward motor assistance can be dynamically regulated using an allocation factor. Experiments involving 12 healthy volunteers were conducted using a one-degree-of-freedom elbow testbed. Results: The experimental results showcased the successful integration of Functional Electrical Stimulation (FES) with robotic actuation, ensuring precise trajectory tracking with a Root Mean Square Error (RMSE) below 7°. Notably, allocating more torque to FES led to a 51% reduction in motor torque. In conditions where FES operated alone, there was poorer tracking performance with an RMSE of 24° and an early onset of muscle fatigue, as evidenced by a reduced number of achieved repetitions. Furthermore, the hybrid approach enabled 100 fatigue-free elbow flexion repetitions, underscoring the effectiveness of cooperative FES-motor control in extending the benefits of FES-induced exercises. Significance: This study proposes a flexible approach which can be extended to a multi-degree-of-freedom hybrid system. Furthermore, it underscores the significance of employing a straightforward and adaptable methodology with a rapid calibration procedure, making it easily transferable to clinical applications.
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Objective: Hybrid systems that integrate Functional Electrical Stimulation (FES) and robotic assistance have been proposed in neurorehabilitation to enhance therapeutic benefits. This study focuses on designing a cooperative controller capable of distributing the required torque for movement between robotic actuation and FES, thereby eliminating the need for time-consuming calibration procedures. Methods: The control schema comprises three main blocks: a motion generation block that defines the desired trajectory, a motor control block including both a weight compensation feedforward and a feedback impedance controller, and an FES control block, based on trial-by-trial Iterative Learning Control (ILC), that adjusts the stimulation intensity according to a predefined stimulation waveform. The feedforward motor assistance can be dynamically regulated using an allocation factor. Experiments involving 12 healthy volunteers were conducted using a one-degree-of-freedom elbow testbed. Results: The experimental results showcased the successful integration of Functional Electrical Stimulation (FES) with robotic actuation, ensuring precise trajectory tracking with a Root Mean Square Error (RMSE) below 7°. Notably, allocating more torque to FES led to a 51% reduction in motor torque. In conditions where FES operated alone, there was poorer tracking performance with an RMSE of 24° and an early onset of muscle fatigue, as evidenced by a reduced number of achieved repetitions. Furthermore, the hybrid approach enabled 100 fatigue-free elbow flexion repetitions, underscoring the effectiveness of cooperative FES-motor control in extending the benefits of FES-induced exercises. Significance: This study proposes a flexible approach which can be extended to a multi-degree-of-freedom hybrid system. Furthermore, it underscores the significance of employing a straightforward and adaptable methodology with a rapid calibration procedure, making it easily transferable to clinical applications.
High transparency is a fundamental requirement for upper-limb exoskeletons to promote active patient participation. Although various control strategies have been suggested to improve the transparency of these robots, there are still some limitations, such as the need for precise dynamic models and potential safety issues when external forces are applied to the robot. This study presents a novel hybrid controller designed to tackle these limitations by combining a traditional zero-torque controller with an interaction torque observer that compensates for residual undesired disturbances. The transparency of the proposed controller was evaluated using both quantitative-e.g., residual joint torques and movement smoothness-and qualitative measures-e.g., comfort, agency, and perceived resistance-in a pilot study with six healthy participants. The performance of the new controller was compared to that of two conventional controllers: a zero-torque closed-loop controller and a velocity-based disturbance observer. Our preliminary results show that the proposed hybrid controller may be a good alternative to state-of-the-art controllers as it allows participants to perform precise and smooth movements with low interaction joint torques. Importantly, participants rated the new controller higher in comfort and agency, and lower in perceived resistance. This study highlights the importance of incorporating both quantitative and qualitative assessments in evaluating control strategies developed to enhance the transparency of rehabilitation robots.
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High transparency is a fundamental requirement for upper-limb exoskeletons to promote active patient participation. Although various control strategies have been suggested to improve the transparency of these robots, there are still some limitations, such as the need for precise dynamic models and potential safety issues when external forces are applied to the robot. This study presents a novel hybrid controller designed to tackle these limitations by combining a traditional zero-torque controller with an interaction torque observer that compensates for residual undesired disturbances. The transparency of the proposed controller was evaluated using both quantitative-e.g., residual joint torques and movement smoothness-and qualitative measures-e.g., comfort, agency, and perceived resistance-in a pilot study with six healthy participants. The performance of the new controller was compared to that of two conventional controllers: a zero-torque closed-loop controller and a velocity-based disturbance observer. Our preliminary results show that the proposed hybrid controller may be a good alternative to state-of-the-art controllers as it allows participants to perform precise and smooth movements with low interaction joint torques. Importantly, participants rated the new controller higher in comfort and agency, and lower in perceived resistance. This study highlights the importance of incorporating both quantitative and qualitative assessments in evaluating control strategies developed to enhance the transparency of rehabilitation robots.
Conference paper
(2023)
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Alexandre Ratschat, Tiago M. C. Lomba, Stefano D. Gasperina, Laura Marchal-Crespo
Rehabilitation robotics combined with virtual reality using head-mounted displays enable naturalistic, immersive, and motivating therapy for people after stroke. There is growing interest in employing digital twins in robotic neurore-habilitation, e.g., in telerehabilitation for virtual coaching and monitoring, as well as in immersive virtual reality applications. However, the kinematic matching of the robot's visualization with the real robot movements is hardly validated, potentially affecting the users' experience while immersed in the virtual environment due to a visual-proprioceptive mismatch. The kinematic mismatch may also limit the validity of assessment measures recorded with the digital twin. We present the development and low-cost kinematic validation of a digital twin of a seven active degrees-of-freedom exoskeleton for stroke rehabilitation. We validated the kinematic accuracy of the digital twin end-effector by performing two tasks-a planar reaching task and a 3D functional task-performed by a single healthy participant. We computed the end-effector position and rotation from the forward kinematics of the robot, the digital twin, and data recorded from the real robot using a low-cost tracking system based on HTC VIVE trackers and compared them pair-wise. We found that the digital twin closely matches the forward kinematics and tracked movement of the real robot and thus provides a reliable platform for future research on digital twins for stroke rehabilitation.
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Rehabilitation robotics combined with virtual reality using head-mounted displays enable naturalistic, immersive, and motivating therapy for people after stroke. There is growing interest in employing digital twins in robotic neurore-habilitation, e.g., in telerehabilitation for virtual coaching and monitoring, as well as in immersive virtual reality applications. However, the kinematic matching of the robot's visualization with the real robot movements is hardly validated, potentially affecting the users' experience while immersed in the virtual environment due to a visual-proprioceptive mismatch. The kinematic mismatch may also limit the validity of assessment measures recorded with the digital twin. We present the development and low-cost kinematic validation of a digital twin of a seven active degrees-of-freedom exoskeleton for stroke rehabilitation. We validated the kinematic accuracy of the digital twin end-effector by performing two tasks-a planar reaching task and a 3D functional task-performed by a single healthy participant. We computed the end-effector position and rotation from the forward kinematics of the robot, the digital twin, and data recorded from the real robot using a low-cost tracking system based on HTC VIVE trackers and compared them pair-wise. We found that the digital twin closely matches the forward kinematics and tracked movement of the real robot and thus provides a reliable platform for future research on digital twins for stroke rehabilitation.