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Evaluation of Self-Reliance Support Robot Through Relative Phase
Author(s) -
Tianyi Wang,
Hieyong Jeong,
Yuko Ohno
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2747841
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Numerous studies have developed self-reliance support robots, such as those assisting the sit-to-stand (STS) movement, which requires coordination between the upper body and the lower limbs. However, few studies have quantitatively evaluated the service quality of such robots. This paper proposes a method to evaluate the service quality of STS-assistance robots through the relative phase (RP), which contains information on the coordinating relationship between the upper body and the lower limbs. STS experiments were performed under three conditions, namely unassisted STS movement and robot-supported STS movements lasting 2 and 5 s. The results showed that the quality of robot assistance during STS movement could be quantitatively evaluated through RP. Furthermore, three features—minimum RP, mean absolute RP, and deviation phase (DP)—that contained information on the users response to the robot could be extracted for data mining. Moreover, electromyography performed to verify the experimental results confirmed the relationship between coordinated performance and muscle activities during STS movements. Thus, evaluating STS movements through the RP is an effective method of evaluating the service quality of robots, and features extracted from RP theory could distinguish classes of movements with a high probability.

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