scholarly journals 'Body-in-the-Loop' Optimization of Assistive Robotic Devices: A Validation Study

2016 ◽  
Author(s):  
Jeffrey R Koller ◽  
Deanna H Gates ◽  
Daniel P Ferris ◽  
C David Remy
Keyword(s):  
Validation Study ◽  
Loop Optimization ◽  
Robotic Devices ◽  
Assistive Robotic

Virtual Interaction Between Patients and Occupational Therapists Using an Assistive Robotic Device With Cyber-Physical System

2018 ◽  
Author(s):  
Marvin H. Cheng ◽  
Po-Lin Huang ◽  
Hao-Chuan Chu ◽  
Li-Han Peng ◽  
Ezzat Bakhoum
Keyword(s):  
Physical System ◽  
Occupational Therapists ◽  
Robotic System ◽  
Cyber Physical System ◽  
Robotic Device ◽  
Stroke Patients ◽  
Virtual Interaction ◽  
Weight Design ◽  
Robotic Devices ◽  
Assistive Robotic

In this paper, we propose to design, develop, and study a cyber-physical system that enables patients and therapists to virtually interact for rehabilitation activities with assistive robotic devices. The targeted users of this system are post-stroke patients. On the patient’s side, an assistive robotic device can generate the force that the therapist applies to the patient. On the therapist’s side, another robotic device can reproduce the responsive force generated by the patient. With this system, the interaction can be virtually established. In addition, by integrating real human trajectories, the proposed assistive robotic system can help patients to perform rehabilitation activities in their own pace. Such an assistive robotic system and virtual interacting scheme can minimize both patient’s and therapist’s traveling time. The assistive functions of this light weight design can also help patients to in their ADLs.

Motion Estimation and Path Planning for Assistive Robotic Devices

2019 ◽  
Author(s):  
Marvin H. Cheng ◽  
Po-Lin Huang ◽  
Hao-Chuan Chu ◽  
E. A. McKenzie
Keyword(s):  
Neural Networks ◽  
Path Planning ◽  
Motion Estimation ◽  
Convolutional Neural Networks ◽  
Daily Activities ◽  
Healthcare Applications ◽  
Initial State ◽  
Predicting Model ◽  
Robotic Devices ◽  
Assistive Robotic

Abstract Assistive robotic devices have recently become a popular tool in various healthcare applications. To better assist users in their daily activities with robotic devices, adequate moving paths of joints need to be adopted based on user’s motions. In this paper, a motion predicting model was proposed. With the model developed using convolutional neural networks (CNNs), the corresponding type of motions can be determined efficiently in the initial state. A deriving procedure of common trajectories of desired motions has also been proposed using the approach of temporal alignment. These derived common trajectories are stored as a library. After the type of a specific motion being identified, paths are then synthesized to drive robotic devices with these derived common trajectories.

scholarly journals A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5238 ◽  
Author(s):  
Nili E. Krausz ◽  
Levi J. Hargrove
Keyword(s):  
Control Systems ◽  
Physical Contact ◽  
Assistive Device ◽  
Forward Prediction ◽  
Endpoint Control ◽  
Hand Grasp ◽  
Contextual Awareness ◽  
Geometric Segmentation ◽  
Robotic Devices ◽  
Assistive Robotic

Teleception is defined as sensing that occurs remotely, with no physical contact with the object being sensed. To emulate innate control systems of the human body, a control system for a semi- or fully autonomous assistive device not only requires feedforward models of desired movement, but also the environmental or contextual awareness that could be provided by teleception. Several recent publications present teleception modalities integrated into control systems and provide preliminary results, for example, for performing hand grasp prediction or endpoint control of an arm assistive device; and gait segmentation, forward prediction of desired locomotion mode, and activity-specific control of a prosthetic leg or exoskeleton. Collectively, several different approaches to incorporating teleception have been used, including sensor fusion, geometric segmentation, and machine learning. In this paper, we summarize the recent and ongoing published work in this promising new area of research.

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