TY - GEN
T1 - Treadmill motor current value based walk phase estimation
AU - Ohki, Eiichi
AU - Nakashima, Yasutaka
AU - Ando, Takeshi
AU - Fujie, Masakatsu G.
PY - 2009
Y1 - 2009
N2 - We have developed a gait rehabilitation robot for hemiplegic patients using the treadmill. A walk phase, which includes time balance of stance and swing legs, is one of the most basic indexes to evaluate patients' gait. In addition, the walking phase is one of the indexes to control our robotic rehabilitation system. However, conventional methods to measure the walk phase require another system such as the foot switch and force plate. In this paper, an original algorithm to estimate the walk phase of a person on a treadmill using only the current value of DC motor to control the treadmill velocity is proposed. This algorithm was verified by experiments on five healthy subjects, and the walk phase of four subjects could be estimated in 0.2 (s) errors. However, the algorithm had erroneously identified a period of time in the stance phase as swing phase time when little body weight loaded on the subject's leg. Because a period of time with little body weight to affected leg is often observed in a hemiplegic walk, the proposed algorithm might fail to properly estimate the walk phase of hemiplegic patients. However, this algorithm could be used to estimate the time when body weight is loaded on patient legs, and thus could be used as a new quantitative evaluation index.
AB - We have developed a gait rehabilitation robot for hemiplegic patients using the treadmill. A walk phase, which includes time balance of stance and swing legs, is one of the most basic indexes to evaluate patients' gait. In addition, the walking phase is one of the indexes to control our robotic rehabilitation system. However, conventional methods to measure the walk phase require another system such as the foot switch and force plate. In this paper, an original algorithm to estimate the walk phase of a person on a treadmill using only the current value of DC motor to control the treadmill velocity is proposed. This algorithm was verified by experiments on five healthy subjects, and the walk phase of four subjects could be estimated in 0.2 (s) errors. However, the algorithm had erroneously identified a period of time in the stance phase as swing phase time when little body weight loaded on the subject's leg. Because a period of time with little body weight to affected leg is often observed in a hemiplegic walk, the proposed algorithm might fail to properly estimate the walk phase of hemiplegic patients. However, this algorithm could be used to estimate the time when body weight is loaded on patient legs, and thus could be used as a new quantitative evaluation index.
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U2 - 10.1109/IEMBS.2009.5332924
DO - 10.1109/IEMBS.2009.5332924
M3 - Conference contribution
C2 - 19963952
AN - SCOPUS:77950993828
SN - 9781424432967
T3 - Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
SP - 7131
EP - 7134
BT - Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - IEEE Computer Society
T2 - 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009
Y2 - 2 September 2009 through 6 September 2009
ER -