• 센서학회지
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• 제22권1호
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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.

• 한국전문물리치료학회지
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• 제23권3호
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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.

• 한국정보통신학회논문지
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• 제14권8호
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• pp.1901-1906
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• 2010

걸음걸이 패턴 분류는 많은 응용분야가 있을 뿐만 아니라 매우 중요한 연구 분야이다. 따라서 본 연구에서는 허리에 부착된 가속도 모듈로부터 획득된 신호를 이용하여 천천히 걷기(slow walking, S.W), 일반 걷기(normal walking, N.W), 빠르게 걷기(fast walking, F.W) 등의 보행 패턴을 분류하고자한다. 11명의 성인으로부터 블루트스 모듈을 이용하여 100Hz로 샘플링된 3축 가속도 신호를 획득하였다. 획득된 데이터는 웨이브렛 변환을 이용하여 분석하였다. 걸음걸이 패턴은 두가지의 파라미터들을 이용하여 분석되어지는데, 하나는 운동에 해당하는 웨이브렛 계수의 에너지(power)와 전 후방향의 전체 에너지사이의 비율(RPA)이고, 다른 파라미터는 전 후 방향과 상 하 수직 방향 사이에서 웨이브렛 계수의 제곱근 평균 비율(RAV)이다. 천천히 걷기는 다른 걷기와 비교했을 때 작은 RPA값을 가지게 되어 분류가 용이하며, 천천히 걷기는 RAV를 이용하여 일반 걷기와 구별되어 질 수 있었다. 따라서 본연구는 건강한 성인에게서 3축 가속도 신호를 획득한 후 웨이브렛 파라미터를 이용하여 걷기 패턴을 잘 구별할 수 있는 연구임을 확인 하였다.