• The Journal of Korean Physical Therapy
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• v.28 no.2
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• pp.95-100
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• 2016

Purpose: The purposes of this study were to examine muscle activities of trunk and lower limb during squat and 108 bows exercises and to provide objective data for establishing a training method for improving muscle strength of trunk and lower limb. Methods: Twenty normal healthy subjects participated in this study. Each exercise was divided into five periods. Muscle activities of trunk and lower limb in each period of both 108 bows and squat exercises were measured and analyzed by independent t-test. Results: In starting, mid-flexion, mid-extension, and end period muscle activities obtained from 108 bows exercise were significantly higher than those from squat exercise. However, in the final flexion period, muscle activities of multifidus, elector spinae, rectus femoris, biceps femoris, and tibialis anterior from squat exercise were significantly higher than those from bow exercise. Conclusion: In this study, high muscle activities in most muscles of trunk and lower limb were observed from all periods of 108 bows exercise except the final flexion period. Therefore, it is likely that 108 bows exercise rather than squat exercise is more suitable for high strength exercise to improve muscle strength of trunk and lower limb and thus will be applicable for strengthening muscles of trunk and lower limb of patients.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1150-1158
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• 2010

In this paper, a new reliable portable activity monitoring device implemented with the buddhist-style bowing activity and walking step detection algorithm, is presented. In order to monitor the bowing and walking activities, miniaturized 3-axis accelerometer sensor with the sensitivity of 800 mV/g was used. After initial signal conditioning, vector magnitude of accelerometer signals was calculated. Syntactic peak detection method was used in order to feature points. All signal processing algorithms were implemented in ultra-low power microcontroller MSP430 with double precision floating point arithmetic. For evaluation, 19 young man($24.22\pm5.22$ yrs) and woman($22.28\pm2.72$ yrs) were involved. The accuracy of the proposed algorithms were 98.91 %($\pm0.011$) for walking step detection and 98.25 %($\pm0.023$) for buddhist-style bowing activity. Comparing to the commercialized pedometer accuracy, 87.1 %($\pm0.058$), the proposed walking step detection algorithms show more reliable accuracy.