Abstract
Elderly activity detection is one of the major issues in modern civilizations. Due to the increase in workload, people are not able to watch over their elderly ones and leave them into old homes for care and cure. In some countries, most elderly people like to live independently instead of living in old homes, which may leave them alone and helpless in any emergency. This study proposes a Machine Learning (ML) based activity detection system which helps to classify the activities of elderly people includes falling down, lying down, sitting down, walking, lying, sitting, standing up, being on all fours, sitting on the ground, standing up from sitting, and standing up from sitting on the ground. The dataset contains the records of five elderly people's activities are used in this research. The data is collected via a wearable sensor accelerometer, which is in the form of time-series data, associated with the timestamp in each reading. To choose the best classifier for the system, we have applied five ML classifiers including Random Forest, k-Nearest Neighbor (k-NN), BernoulliNB, Decision Tree and XGBClassifier. The k-NN classifier achieved a performance accuracy of 99.50%. The proposed elderly fall detection method performs very accurate at identifying activities such as standing up from the lying posture in order to determine whether a fall was happened by a protracted lay, which cause small to serious health issues.
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Ali, M., Mushtaq, M.F., Shahroz, M., Majeed, R., Samad, A., Akram, U. (2022). Elderly Fall Activity Detection Using Supervised Machine Learning Models. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_33
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