Percutaneous patent foramen ovale closure for cryptogenic stroke: learning from clinical trial and error

Mojadidi, Mohammad K.; Mahmoud, Ahmed N.; Elgendy, Islam Y.

doi:10.21037/jtd.2017.09.153

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Furthermore, rehab training using gait assistance could help in providing intensive therapeutic exercises while also allowing for a quantitative assessment of the recovery ( Mirelman et al, 2019 ). However, the trial-and-error learning hypothesis in motor control research suggests that position-control-based movement might decrease motor learning for some tasks, and the human-robot interaction control is the main pain point of training aiding ( Reisman et al, 2010 ; Mojadidi et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Multimodal fusion and human-robot interaction control of an intelligent robot

Gong,

Chen,

Wang

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: Small-scaled robotic walkers play an increasingly important role in Activity of Daily Living (ADL) assistance in the face of ever-increasing rehab requirements and existing equipment drawbacks. This paper proposes a Rehabilitation Robotic Walker (RRW) for walking assistance and body weight support (BWS) during gait rehabilitation.Methods: The walker provides the patients with weight offloading and guiding force to mimic a series of the physiotherapist’s (PT’s) movements, and creates a natural, comfortable, and safe environment. This system consists of an omnidirectional mobile platform, a BWS mechanism, and a pelvic brace to smooth the motions of the pelvis. To recognize the human intentions, four force sensors, two joysticks, and one depth-sensing camera were used to monitor the human-machine information, and a multimodal fusion algorithm for intention recognition was proposed to improve the accuracy. Then the system obtained the heading angle E, the pelvic pose F, and the motion vector H via the camera, the force sensors, and the joysticks respectively, classified the intentions with feature extraction and information fusion, and finally outputted the motor speed control through the robot’s kinematics.Results: To validate the validity of the algorithm above, a preliminary test with three volunteers was conducted to study the motion control. The results showed that the average error of the integral square error (ISE) was 2.90 and the minimum error was 1.96.Discussion: The results demonstrated the efficiency of the proposed method, and that the system is capable of providing walking assistance.

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Section: Introductionmentioning

confidence: 99%