Abstract:This paper proposes a new robotic rehabilitation training platform that is motivated by the requirement for adjusting the training strategy and intensity in a patient-specific manner. The platform is implemented for tele-rehabilitation and is comprised of a haptic device operated by therapists, a lightweight exoskeleton worn by patients and a visually shared model. Through the visually shared model, the motion of the therapist and patient are measured and mapped to the motion of the corresponding object. Thus,… Show more
“…Additionally, the study underscores the integration of Cartesian and electromyography (EMG) data as feedback, facilitating the inference of limb posture from EMG signals and recalibration during new exercises. In the existing literature various approaches within closely related fields of study, including works by [36][37][38][39][40][41], partially intersect with the themes presented in this work. However, the authors remain uncertain as to whether fully analogous studies have been previously published.…”
This study presents a novel kinematic tracking model, designed for a networked exoskeleton system that is asynchronously taught by a remote therapist. On the server side, the therapist’s rehabilitation exercises are quantitatively assessed using a monocular passive vision system. The resultant analytical metrics are then transmitted asynchronously over the network to patients equipped with exoskeletons. On the client side, the exoskeleton utilizes these analytical metrics as reference paths for exercises, complemented by electromyography (EMG) feedback. This work introduces a calibration approach aimed at estimating angular positions by utilizing EMG observations. The calibration model establishes real-time correlations between polynomial reference positions. This calibration mechanism is integrated into simulations of both upper and lower limb exoskeletons. We further explore redundant kinematics, incorporating an EMG observer for linear, time-variant rehabilitation tracking control. Our methodology is validated using vision-based metric data and experimental EMG measurements for various exercises, including shoulder flexion, elbow flexion, and rowing-like movements. This work also includes computer simulations for tracking the control of rehabilitation exercises, demonstrating the adaptability of the system in reliably, robustly, and effectively following desired trajectories.
“…Additionally, the study underscores the integration of Cartesian and electromyography (EMG) data as feedback, facilitating the inference of limb posture from EMG signals and recalibration during new exercises. In the existing literature various approaches within closely related fields of study, including works by [36][37][38][39][40][41], partially intersect with the themes presented in this work. However, the authors remain uncertain as to whether fully analogous studies have been previously published.…”
This study presents a novel kinematic tracking model, designed for a networked exoskeleton system that is asynchronously taught by a remote therapist. On the server side, the therapist’s rehabilitation exercises are quantitatively assessed using a monocular passive vision system. The resultant analytical metrics are then transmitted asynchronously over the network to patients equipped with exoskeletons. On the client side, the exoskeleton utilizes these analytical metrics as reference paths for exercises, complemented by electromyography (EMG) feedback. This work introduces a calibration approach aimed at estimating angular positions by utilizing EMG observations. The calibration model establishes real-time correlations between polynomial reference positions. This calibration mechanism is integrated into simulations of both upper and lower limb exoskeletons. We further explore redundant kinematics, incorporating an EMG observer for linear, time-variant rehabilitation tracking control. Our methodology is validated using vision-based metric data and experimental EMG measurements for various exercises, including shoulder flexion, elbow flexion, and rowing-like movements. This work also includes computer simulations for tracking the control of rehabilitation exercises, demonstrating the adaptability of the system in reliably, robustly, and effectively following desired trajectories.
“…However, despite the system mobility, a significant encumbrance of their bases makes them difficult to transport and use at home. Lighter design concepts presented in [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ] offer solutions that can be easily stored in a relatively small space but still require a support frame for the rehabilitation exercises.…”
An experimental characterization is presented for an improved version of a wearable assistive device for elbow motion. The design is revised with respect to requirements for elbow motion assistance, looking at applications both in rehabilitation therapies and exercising of elderly people. A laboratory prototype is built with lightweight, portable, easy-to-use features that are verified with test results, whose discussion is also provided as a characterization of operating performance.
“…Up to now, literature has focused on comparing rehabilitation outcomes of robotic vs. in-person therapies, but there is not any knowledge of what happens when the therapist interacts physically with the patient through the robot. A few works have already presented this teleoperated modality [4][5][6], but their research was more focused on the design and control of the robotic system rather than evaluating the advantages and disadvantages of this modality compared to traditional robotic or in-person therapies. Therefore, this work addresses the objective and subjective performance implications of this novel interaction method.…”
The present study aims to evaluate the advantages of a master-slave robotic rehabilitation therapy in which the patient is assisted in real-time by a therapist. We have also explored if this type of strategy is applicable in a tele-rehabilitation environment. A pilot study has been carried out involving 10 patients who have performed a point-to-point rehabilitation exercise supported by three assistance modalities: fixed assistance (without therapist interaction), local therapist assistance, and remote therapist assistance in a simulated tele-rehabiliation scenario. The rehabilitation exercise will be performed using an upper-limb rehabilitation robotic device that assists the patients through force fields. The results suggest that the assistance provided by the therapist is better adapted to patient needs than fixed assistance mode. Therefore, it maximizes the patient’s level of effort, which is an important aspect to improve the rehabilitation outcomes. We have also seen that in a tele-rehabilitation environment it is more difficult to assess when to assist the patient than locally. However, the assistance suits patients better than the fixed assistance mode.
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