기계학습 모델을 이용한 노인보행과비노인보행의 구별 방법에 관한 연구
A Study on the Method of Differentiating Between Elderly Walking and Non-Senior Walking Using Machine Learning Models
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초록
보행 분석은 인간의 걸음걸이를 분석하여 보행과 관련된 여러 다양한 정보를 얻기 위한 연구 분야 중 하나로써 의료 분야뿐만 아니라 기계공학,전자공학 및 컴퓨터공학 등 다양한 학문 분야에서 오랫동안 연구되고 있다. 보행 분석을 통해 걸음걸이에 문제가 있는지를 파악하려는 노력이꾸준히 이어져 왔다. 본 논문에서는 이러한 보행 이상을 알아보기 위한 전 단계로써 보행 데이터를 활용하여 동일 실험 참가자에 대해 노인 체험복착용 전후의 걸음걸이를 기계학습 모델에 적용하여 학습시킴으로써 노인 체험복 착용 여부를 구별할 수 있는지를 연구하였다. 총 45명의 실험참가들을 대상으로 노인 체험복 착용 전과 후 각각의 보행 데이터를 수집하였고, 총 6개의 기계학습 모델을 이용하여 보행 데이터를 학습시켰다. 신경망 모델을 활용하여 노인 체험복 착용 여부를 판별한 결과 약 99%의 높은 정확도를 보였다. 본 연구에서 시사하는 것은 기계학습을 활용하여보행의 이상 유무를 판단할 수 있는 가능성을 모색했다는 데 있다.
Abstract
Gait analysis is one of the research fields for obtaining various information related to gait by analyzing human ambulation. It hasbeen studied for a long time not only in the medical field but also in various academic areas such as mechanical engineering, electronicengineering, and computer engineering. Efforts have been made to determine whether there is a problem with gait through gait analysis. In this paper, as a pre-step to find out gait abnormalities, it is investigated whether it is possible to differentiate whether experimentparticipants wear elderly simulation suit or not by applying gait data to machine learning models for the same person. For a total of45 participants, each gait data was collected before and after wearing the simulation suit, and a total of six machine learning modelswere used to learn the collected data. As a result of using an artificial neural network model to distinguish whether or not the participantswear the suit, it showed 99% accuracy. What this study suggests is that we explored the possibility of judging the presence or absenceof abnormality in gait by using machine learning.
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