Human Activity recognition using machine learning

Abstract

Human activity recognition is a broad field of study concerned with identifying the specific movement or action of a person based on sensor data. It can improve people's well-being by automatically assessing and summarizing their daily activities. In this study an automatic detection of nine different human activities from body sensor data was developed. The human activity data was collected from 30 between the age of 12 and 55 to provide data. from those 17 participants were male and 13 participants were female. Participants completed nine activities (sitting, standing, walking, running, upstairs, downstairs, sit-up, jumping and cycling) while wearing an I-phone eight plus in the pocket. Records from 18 individuals used for training, 6 participants data was used for validation and the remaining 6 participants records were used for testing. The human activity data is captured by via phone's integrated acceleration and gyroscope sensors. Hence a total of nine-dimensional data (triaxial accelerometer data (linear acceleration and body acceleration) and triaxial gyroscope data) was acquired using these two sensors. In all case the sample rate used to capture the data was 50Hz. In this study both classical machine learning and deep learning methods were exported. In classical techniques four machine learning classifiers (SVM, KNN, logistic regression and decision tree) have been used to categorize the data. For this to work 18 set of features were carefully computed. Three deep learning models (CNN, LSTM and hybrid CNN-LSTM) have been also trained in the accurate detection of human activity classification.