Fall Detection and Daily Living Activity Recognition using Machine Learning
Ranjeeth Kumar. C1, SathyaPraba. D2, Shanmugapriya.S3, Sounderya. R4
1Ranjeeth Kumar. C*, Assistant Professor (Sr. Gr), Department of Information Technology, Sri Ramakrishna Engineering College, Chennai, India.
2Sathya Praba. D, Student, Department of Information Technology, Sri Ramakrishna Engineering College, Chennai, India.
3Shanmugapriya.S, Student, Department of Information Technology, Sri Ramakrishna Engineering College, Chennai, India.
4Sounderya. R, Student, Department of Information Technology, Sri Ramakrishna Engineering College, Chennai, India.
Manuscript received on August 07, 2020. | Revised Manuscript received on August 15, 2020. | Manuscript published on August 30, 2020. | PP: 328-332 | Volume-9 Issue-6, August 2020. | Retrieval Number: F1308089620/2020©BEIESP | DOI: 10.35940/ijeat.F1308.089620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Elder people are increasing all over the world as a result certain fall occur in their daily life. This fall lead to several severe problems. The fall may often causes injuries and in many cases it result in death of the individual. The problem should be addressed to reduce the fall. By using some Machine Learning(ML) algorithm the fall and daily living activities are recognized. The acceleration and angular velocity data obtained from the dataset are used to detect the fall and daily living activity. Body movement of the person are collected and stored in the dataset. Acceleration and angular velocity data are used to extract the time and frequency domain feature and provide them to classification algorithm. Here, Logistic regression algorithm is used for detecting the fall and living activity. It is very effective algorithm and does not require too many computational resources. It is easy to regularize and provide well calibrated predicted probabilities as output. The sensitivity, accuracy and specificity of fall detection and activity recognition is obtained as a result. The performance evaluation is made with three classification algorithm. The three classification algorithm are Artificial neural network (ANN), K-nearest neighbours (KNN), Quadratic support vector machine (QSVM). Logistic regression provides highest accuracy compared with other three algorithm.
Keywords: Fall detection, activity recognition, logistic regression, dataset values.