DocumentCode :
1985939
Title :
Non-contact versus contact-based sensing methodologies for in-home upper arm robotic rehabilitation
Author :
Howard, Alex ; Brooks, David ; Brown, Eitan ; Gebregiorgis, Adey ; Yu-Ping Chen
Author_Institution :
Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2013
fDate :
24-26 June 2013
Firstpage :
1
Lastpage :
6
Abstract :
In recent years, robot-assisted rehabilitation has gained momentum as a viable means for improving outcomes for therapeutic interventions. Such therapy experiences allow controlled and repeatable trials and quantitative evaluation of mobility metrics. Typically though these robotic devices have been focused on rehabilitation within a clinical setting. In these traditional robot-assisted rehabilitation studies, participants are required to perform goal-directed movements with the robot during a therapy session. This requires physical contact between the participant and the robot to enable precise control of the task, as well as a means to collect relevant performance data. On the other hand, non-contact means of robot interaction can provide a safe methodology for extracting the control data needed for in-home rehabilitation. As such, in this paper we discuss a contact and non-contact based method for upper-arm rehabilitation exercises that enables quantification of upper-arm movements. We evaluate our methodology on upper-arm abduction/adduction movements and discuss the advantages and limitations of each approach as applied to an in-home rehabilitation scenario.
Keywords :
biomedical measurement; electromyography; feature extraction; hidden Markov models; human-robot interaction; image motion analysis; image sequences; medical image processing; medical robotics; robot vision; EMG sensors; arm movement data extraction; control data extraction; goal-directed movements; hidden Markov model algorithm; in-home upper arm robotic rehabilitation; mobility metric quantitative evaluation; noncontact versus contact-based sensing methodology; robot interaction; robot-assisted rehabilitation; robotic devices; therapeutic interventions; therapy experiences; upper-arm adduction movements; video sequence; vision-based assessment; Data mining; Elbow; Electromyography; Hidden Markov models; Joints; Robots; Shoulder; EMG measurements; robotic rehabilitation; therapeutic robotics; vision-based assessment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on
Conference_Location :
Seattle, WA
ISSN :
1945-7898
Print_ISBN :
978-1-4673-6022-7
Type :
conf
DOI :
10.1109/ICORR.2013.6650487
Filename :
6650487
Link To Document :
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