A Reliable Kinematic Measurement of Upper Limb Exoskeleton for VR Therapy with Visual-inertial Sensors

Kwok, Thomas M.; Li, Tong; Huang, Yu

doi:10.1109/aim46323.2023.10196192

Cited by 2 publications

(5 citation statements)

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“…Regarding research significance, the proposed control framework benefits various exoskeleton designs since it independently controls the active joints' movement. For example, redundantly-actuated exoskeletons like Harmony [18], ARMin [19], CURER [34], and NESM-γ [35]; underactuated exoskeletons with passive joints like UULE [24], ASSISTON-SE [20], and NESM [21]; and wearable exoskeletons with fewer DoFs like Elbow-Wrist Exoskeleton [36] and portable shoulder exoskeleton [37]. Also, it is not required to have any specific and complicated controller for different kinds of exoskeletons.…”

Section: Discussionmentioning

confidence: 99%

“…To show the potential of the proposed control in underactuated exoskeletons, we implemented the control in the Underactuated Upper Limb Exoskeleton (UULE) [24]. This is a bilateral underactuated exoskeleton that assists chronic stroke patients in bimanual ADL training, as shown in Fig.…”

Section: A Underactuated Upper Limb Exoskeleton (Uule)mentioning

confidence: 99%

“…The encoders measured hinge joint angles (sF/E-A, eF/E-A, and wF/E-A), while visual-inertial sensors measured ball and revolute joint angles (sP/R-P, sR-P, and eR-A). More details can be found in our previous works [24].…”

Section: A Underactuated Upper Limb Exoskeleton (Uule)mentioning

confidence: 99%

“…Regarding virtual arm motion, the guiding arm was updated by Forward Kinematics (FK) in Unity3D with therapist-demonstrated joint trajectories, while the patient's virtual arm was updated with robot joint feedback (𝑞). Further, the translations of guiding and virtual arms can be achieved by the therapist's and patient's shoulder translations, estimated by the passive ball joint at sP/R-P and a long radius between sF/E-A and sP/R-P. More details on the kinematic model of virtual arms can be found in our previous work [24].…”

Section: ) Visual Guidance and Feedback In Vrmentioning

confidence: 99%

“…The marker's orientation measurement, fused 10 Hz camera feedback (720 p/120 fps) and 100 Hz IMU data, was sent to the PC/104 for joint angle calculations. More details and the experimental validation of sensors in the VR ADL task can be found in [24]. Subsequently, joint angle measurements were sent to Unity3D in another computer through User Datagram Protocol (UDP) at 70 Hz with 2.983ms of connection latency.…”

Section: Experiments a System Hardwarementioning

confidence: 99%

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Asymmetric Bimanual ADL Training With Underactuated Exoskeleton Using Independent Joint Control and Visual Guidance

Kwok,

2024

IEEE Access

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This paper addresses the challenges in existing upper limb robotic rehabilitation training for asymmetric bimanual activities of daily living (ADL), crucial for stroke patients' ADL-related functional recovery. Our proposed exoskeleton control framework introduces independent joint control and visual guidance in virtual reality (VR) to facilitate asymmetric bimanual ADL training. Unlike conventional taskspace control methods, our approach implements independent joint control into underactuated exoskeletons, offering individualized assistance tailored to patients' joint impairment conditions. The framework utilizes human-demonstrated ADL motions for joint trajectory planning, potentially teaching compensatory techniques with unique joint coordination patterns through therapist demonstrations. To address interjoint coordination challenges in underactuated exoskeletons, VR visual guidance aids patients in self-coordinating unassisted and assisted joints. The proposed framework was evaluated by human experiments with 15 healthy subjects. It demonstrated the effectiveness of the proposed visual guidance, exhibiting a motion period similar to human-demonstrated motion and statistically significant reductions in angle errors at the shoulder (sF/E-A) and wrist (wF/E-A). The robot assistance provided by the control framework was further validated through statistically significant reductions in electromyography (EMG) and angle errors at robot-assisted joints. This proof-of-concept on healthy subjects suggests the potential of our control framework to assist stroke patients in asymmetric bimanual ADL training.

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