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Abstract
To increase intelligence of walking-assisted devices, it is important to identify motion mode of the subject who wears a walking-assisted device. In this paper, cerebral hemoglobin information was used for recognizing motion mode. Spontaneous upstairs, downstairs, sit-down, and standup movements were performed on seven subjects. During the movement, cerebral hemoglobin information was recorded by applying near-infrared spectroscopic technology. Analyses of variance were performed to compare the rate of change of oxygenated hemoglobin and deoxygenated hemoglobin (oxyHb_rate and deoxyHb_rate) in different motion modes and in different motor-related regions. In PMCR region, oxyHb_rate and deoxyHb_rate were significantly different in the upstairs and downstairs modes (p = 0.001 and p = 0.000); however, they were not obviously different in the sit-down and standup modes (p = 0.914 and p = 0.836). In PMCL region, oxyHb_rate and deoxyHb_rate were significantly different in the downstairs mode (p = 0.008), whereas they were not distinctly different in the upstairs mode (p = 0.601). Results demonstrated that the motion trend of two lower limbs could be identified based on the statistical difference between oxyHb_rate and deoxyHb_rate in the PMCR region. As for cycle-repetitive movement of two lower limbs, upward and downward motion direction could be further recognized based on the difference between oxyHb_rate and deoxyHb_rate in the PMCL region. Since the data using for analyses was those collected before the start of movement, the results will be preferable to provide appropriate reference movements for a walking-assisted device. This is helpful to enhance intelligence of walking-assisted devices.
Keywords
Mirror-symmetric movement, cycle-repetitive movement, identification of motion mode, oxyHb_rate and deoxyHb_rate
Date received: 22 September 2014; accepted: 19 January 2015
Academic Editor: Zhuming Bi
Introduction
Recently, the number of persons with motor dysfunction in lower limbs has been increasing markedly because of natural disasters, injuries, accidents, diseases, and so on. In order to restore patients' ability of independent walking, there is an urgent demand for intelligent walking-assisted devices. This requires walking-assisted devices to provide auxiliary power in different motion modes and to determine motion mode based on patients' physiological information rather than an oral command or once selection of buttons. To control the movement of a device by using brain information has been attracting much attention in worldwide.
Commonly used brain imaging technologies...