• Journal of Sensor Science and Technology
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• v.22 no.1
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• pp.54-64
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• 2013

In this study, we developed a falling recognition system to transmit SMS data through CDMA communication using a three axises acceleration sensor and a two axises gyro sensor. 5 healthy men were selected into a control group, and the fall recognition system using the three axises acceleration sensor and the two axises gyro sensor was devised to conduct an experiment. The system was attached to the upper of their sternum. According to the experiment protocol, the experiment was carried out 3 times repeatedly divided into 3 specific protocols: falling during gait, falling in stopped state, and falling in everyday life. Data obtained in the falling recognition system and LabVIEW 8.5 were used to decide if falling corresponds to that regulated in an analysis program applying an algorithm proposed in this study. In addition, results from falling recognition were transmitted to designated cellular phone in a SMS (Shot Message Service) form. These research results show that an erroneous detection rate of falling reached 19% in applying an acceleration signal only; 6% in applying an angular velocity; and 2% in applying a proposed algorithm. Such finding suggests that an erroneous detection rate of falling is improved when the proposed algorithm is applied incorporated with acceleration and angular velocity. In this study therefore, we proposed that a falling recognition system implemented in this study can make a contribution to the recognition of falling of the aged or the disabled.

• Physical Therapy Korea
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• v.23 no.3
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• pp.1-10
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• 2016

Background: Gait problems appear in most stroke patients. Commonly, stroke patients show the typical abnormal gait patterns, such as circumduction, genu recurvatum, and spastic paretic stiff-legged gait. An inclined treadmill gait exercise is good for gait problems of stroke patients. In addition, the backward walking training has been recommended in order to improve the component of the movement for the forward walking. Objects: The purpose of this study to investigated the effects of backward walking with inclined treadmill training on the gait in chronic stroke patients. Methods: A total of 30 volunteers were randomly allocated to two groups that walked on an inclined treadmill: the experimental group ($n_1=15$), which walked backward, and the control group ($n_2=15$), which walked forward. To measure the improvement of the patients' gait, a Figure of Eight Walking Test (F8W), Four Square Step Test (FSST), and Functional Gait Assessment (FGA) were performed. We also measured spatio-temporal gait variables, including gait speed, cadence, stride length, and single limb support using a three-axial wireless accelerometer. The measurements were taken before and after the experiment. The Wilcoxon signed-rank test was used to compare both groups before and after the interventions. The Mann-Whitney U test was used for the comparisons after the interventions. The statistical significance was set at ${\alpha}=.05$. Results: Before and after experiment, all dependent variables were significantly different between the two groups (p<.05). As compared to the control group, the experimental group showed more significant improvements in F8W, FSST, speed, cadence, stride length, and single limb support (p<.05); however, FGA in this group was not significantly different from the control (p>.05). Conclusion: Our results suggest that backward walking on an inclined treadmill is more effective for improving the gait of stroke patients than forward walking.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1901-1906
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• 2010

This classification of walking patterns is important and many kinds of applications. Therefore, we attempted to classify walking on level ground from slow walking to fast walking using a waist acceleration signal. A tri-axial accelerometer was fixed to the subject's waist and the three acceleration signals were recorded by bluetooth module at a sampling rate of 100 Hz eleven healthy. The data were analyzed using discrete wavelet transform. Walking patterns were classified using two parameters; One was the ratio between the power of wavelet coefficients which were corresponded to locomotion and total power in the anteroposterior direction (RPA). The other was the ratio between root mean square of wavelet coefficients at the anteroposterior direction and that at the vertical direction(RAV). Slow walking could be distinguished by the smallest value in RPA from other walking pattern. Fast walking could be discriminated from level walking using RAV. It was possible to classify the walking pattern using acceleration signal in healthy people.