• 재활복지공학회논문지
• /
• 제7권2호
• /
• pp.69-74
• /
• 2013

본 연구에서는 손목 움직임의 추정을 위한 근전도 신호 기반 동작 분류 알고리즘을 제안한다. 근전도의 특징점을 추출하기 위하여 절대차분표준편차(DASDV)과 제곱평균제곱근(RMS)을 사용하며, 측정 된 근전도 신호를 이용하여 동작 마다 30개의 특징점(RMS, DASDV)을 추출한다. 근전도 신호를 특정한 패턴으로 나타내어 적용시키기 위하여 평균값을 기준으로 집단을 두 부분으로 나누고, 패턴분류 방법인 k-NN으로 패턴을 학습시킨 후, 집단을 나누지 않은 방법을 사용한 기존의 연구와 비교하여 제안한 알고리즘의 성능을 검증한다. 실험결과 제안한 알고리즘은 92.59%의 인식률을 보였으며, 이전 연구 결과보다 0.84% 포인트의 성능 개선을 보였다.

• 재활복지공학회논문지
• /
• 제6권1호
• /
• pp.67-73
• /
• 2012

본 논문에서는 표면 근전도 신호를 사용하여 손목 움직임의 동작을 분류하기 위해 인공 신경 회로망(ANN : Artificial Neural Network)기반의 동작 분류 알고리즘을 제안한다. 손목 움직임에 무리가 없는 20~30대 성인 26명을 대상으로 척측 수근 굴근과 척측 수근 신근에 부착한 2채널의 전극으로부터 표면 근전도 신호를 취득하고, 취득한 근전도로부터 손목의 굴곡, 신전, 내전, 외전, 휴식 다섯 동작을 인식한다. 빠른 처리 속도를 위해 획득한 신호로부터 시간 영역에서의 특징점을 추출하고 ANN을 이용한 동작 분류에 사용된다. 특징점으로 DAMV, DASDV, MAV, RMS를 사용하였으며, ANN 기반의 동작 분류의 인식율은 DAMV는 98.03%, DASDV는 97.97%, MAV는 96.95%, 그리고 RMS는 96.82%의 정확도를 나타낸다.

• 전기학회논문지
• /
• 제61권5호
• /
• pp.757-762
• /
• 2012

In this paper, the multi feature extraction algorithm for estimation of wrist movements based on Electromyogram(EMG) is proposed. For the extraction of precise features from the EMG signals, the difference absolute mean value(DAMV), the mean absolute value(MAV), the root mean square(RMS) and the difference absolute standard deviation value(DASDV) to consider amplitude characteristic of EMG signals are used. We figure out a more accurate feature-set by combination of two features out of these, because of multi feature extraction algorithm is more precise than single feature method. Also, for the motion classification based on EMG, the linear discriminant analysis(LDA), the quadratic discriminant analysis(QDA) and k-nearest neighbor(k-NN) are used. We implemented a test targeting twenty adult male to identify the accuracy of EMG pattern classification of wrist movements such as up, down, right, left and rest. As a result of our study, the LDA, QDA and k-NN classification method using feature-set with MAV and DASDV showed respectively 87.59%, 89.06%, 91.75% accuracy.

• 한국전문물리치료학회지
• /
• 제28권2호
• /
• pp.123-131
• /
• 2021

Background: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture). Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects: The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features. Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee ("knee together") or the mat ("knee on mat"), or neither the other knee nor the mat was contacted by the impacting-side knee ("free knee"). Falls involved "backward initial fall direction" or "free knee" were defined as "injurious falls" as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls. Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion: Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.