scholarly journals Robot-Assisted Gait Self-Training: Assessing the Level Achieved

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6213
Author(s):  
Andrea Scheidig ◽  
Benjamin Schütz ◽  
Thanh Quang Trinh ◽  
Alexander Vorndran ◽  
Anke Mayfarth ◽  
...  
Keyword(s):  
Corrective Feedback ◽  
Gait Pattern ◽  
Point Of View ◽  
Clinical Environment ◽  
Robot Assisted ◽  
Immediate Feedback ◽  
Assistive Robot ◽  
Gait Feature ◽  
Training Motivation ◽  
Socially Assistive Robot

This paper presents the technological status of robot-assisted gait self-training under real clinical environment conditions. A successful rehabilitation after surgery in hip endoprosthetics comprises self-training of the lessons taught by physiotherapists. While doing this, immediate feedback to the patient about deviations from the expected physiological gait pattern during training is important. Hence, the Socially Assistive Robot (SAR) developed for this type of training employs task-specific, user-centered navigation and autonomous, real-time gait feature classification techniques to enrich the self-training through companionship and timely corrective feedback. The evaluation of the system took place during user tests in a hospital from the point of view of technical benchmarking, considering the therapists’ and patients’ point of view with regard to training motivation and from the point of view of initial findings on medical efficacy as a prerequisite from an economic perspective. In this paper, the following research questions were primarily considered: Does the level of technology achieved enable autonomous use in everyday clinical practice? Has the gait pattern of patients who used additional robot-assisted gait self-training for several days been changed or improved compared to patients without this training? How does the use of a SAR-based self-training robot affect the motivation of the patients?

The influence of a socially assistive robot on mood, anxiety, and arousal in children.

2018 ◽  
Vol 49 (1) ◽  
pp. 48-56 ◽  
Author(s):  
Molly K. Crossman ◽  
Alan E. Kazdin ◽  
Elizabeth R. Kitt
Keyword(s):  
Assistive Robot ◽  
Socially Assistive Robot

scholarly journals The design of Lil’Flo, a socially assistive robot for upper extremity motor assessment and rehabilitation in the community via telepresence

2021 ◽  
Vol 8 ◽  
pp. 205566832110018
Author(s):  
Michael J Sobrepera ◽  
Vera G Lee ◽  
Michelle J Johnson
Keyword(s):  
Rural Areas ◽  
Motor Rehabilitation ◽  
Rehabilitation Care ◽  
Rehabilitation Professionals ◽  
Novel Approach ◽  
Assistive Robot ◽  
Care Facilities ◽  
The Rich ◽  
Socially Assistive Robot ◽  
Assistive Robotic

Introduction We present Lil’Flo, a socially assistive robotic telerehabilitation system for deployment in the community. As shortages in rehabilitation professionals increase, especially in rural areas, there is a growing need to deliver care in the communities where patients live, work, learn, and play. Traditional telepresence, while useful, fails to deliver the rich interactions and data needed for motor rehabilitation and assessment. Methods We designed Lil’Flo, targeted towards pediatric patients with cerebral palsy and brachial plexus injuries using results from prior usability studies. The system combines traditional telepresence and computer vision with a humanoid, who can play games with patients and guide them in a present and engaging way under the supervision of a remote clinician. We surveyed 13 rehabilitation clinicians in a virtual usability test to evaluate the system. Results The system is more portable, extensible, and cheaper than our prior iteration, with an expressive humanoid. The virtual usability testing shows that clinicians believe Lil’Flo could be deployed in rural and elder care facilities and is more capable of remote stretching, strength building, and motor assessments than traditional video only telepresence. Conclusions Lil’Flo represents a novel approach to delivering rehabilitation care in the community while maintaining the clinician-patient connection.

Design of a 6-DOF Robotic Gait Training System With Closed-Chain Foot Initiated Kinematics Control

2015 ◽  
Author(s):  
Wei Liu ◽  
John Kovaleski ◽  
Marcus Hollis
Keyword(s):  
Three Dimensional ◽  
Computer Software ◽  
Gait Training ◽  
Gait Pattern ◽  
Training System ◽  
Normal Walking ◽  
Robot Assisted ◽  
Walking Gait ◽  
The Right ◽  
Joint Motions

Robotic assisted rehabilitation, taking advantage of neuroplasticity, has been shown to be helpful in regaining some degree of gait performance. Robot-applied movement along with voluntary efferent motor commands coordinated with the robot allows optimization of motion training. We present the design and characteristics of a novel foot-based 6-degree-of-freedom (DOF) robot-assisted gait training system where the limb trajectory mirrored the normal walking gait. The goal of this study was to compare robot-assisted gait to normal walking gait, where the limb moved independently without robotics. Motion analysis was used to record the three-dimensional kinematics of the right lower extremity. Walking motion data were determined and transferred to the robotic motion application software for inclusion in the robotic trials where the robot computer software was programmed to produce a gait pattern in the foot equivalent to the gait pattern recorded from the normal walking gait trial. Results demonstrated that ankle; knee and hip joint motions produced by the robot are consistent with the joint motions in walking gait. We believe that this control algorithm provides a rationale for use in future rehabilitation, targeting robot-assisted training in people with neuromuscular disabilities such as stroke.

Designing an Expressive Head for a Help Requesting Socially Assistive Robot

2020 ◽  
pp. 88-102
Author(s):  
Tim van der Grinten ◽  
Steffen Müller ◽  
Martin Westhoven ◽  
Sascha Wischniewski ◽  
Andrea Scheidig ◽  
...  
Keyword(s):  
Assistive Robot ◽  
Socially Assistive Robot

Month-Long, In-Home Socially Assistive Robot for Children with Diverse Needs

2020 ◽  
pp. 608-618 ◽  
Author(s):  
Caitlyn Clabaugh ◽  
Shomik Jain ◽  
Balasubramanian Thiagarajan ◽  
Zhonghao Shi ◽  
Leena Mathur ◽  
...  
Keyword(s):  
Assistive Robot ◽  
Socially Assistive Robot

scholarly journals Robotics enabled learning patterns: A Study

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Patrick Dough
Keyword(s):  
Tel Aviv ◽  
Learning Patterns ◽  
Assistive Robot ◽  
Personal Robots ◽  
Individual Consideration ◽  
Extensive Class ◽  
Socially Assistive Robot

Folks need the best for their kids' training and regularly grumble about extensive class sizes and the absence of individual consideration. Goren Gordon, a manmade brainpower analyst from Tel Aviv University who runs the Curiosity Lab there, is the same. He and his wife invest as much energy as they can with their kids, however there are still times when their children are separated from everyone else or unsupervised. At those times, they'd like their kids to have a friend to learn and play with, Gordon says. That is the situation, regardless of the possibility that that buddy is a robot. Working in the Personal Robots Group at MIT, drove by Cynthia Breazeal, Gordon was a piece of a group that built up a socially assistive robot called Tega that is intended to serve as a one-on-one associate learner in or outside of the classroom.

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