Human Activities Recognition Using Machine Learning and Artificial Initialization

Abstract

Human Activity Recognition (HAR)is an important challenge for applications in various areas such as healthcare, smart environments, and surveillance. In this paper, we propose a machine learning and artificial intelligence-based approach for HAR using wearable sensor data. The proliferation of wearable devices has made it possible to collect a wide range of sensor data, including accelerometer and gyroscope readings, providing valuable insights into human activity. Our proposed approach uses machine learning algorithms, including support vector machines (SVMs), random forests, and artificial neural networks (ANNs), to classify human activities based on sensor data. We explore feature extraction methods that transform raw sensor readings into meaningful representations including time- and frequency-domain features. We also explore the effectiveness of feature selection methods to identify the most discriminatory features for activity recognition. We also use deeplearning techniques suchas Convolutional Neural Network (CNN)and Recurrent Neural Network (RNN) to automatically learn hierarchical representations from sensor for HAR.We are developing a deeplearning architecture tailored to sequential sensor data that captures both the spatial and temporal dependencies inherent in human activity. We evaluate the proposed approach on publicly available datasets covering a variety of human activities, including walking, running, sitting, standing, and other common daily activities. Experimental results demonstrate the effectiveness of our method in accurately recognizing human activities, outperforming baseline approaches, and achieving state-of-the-art performance on HAR tasks. We also compare and analyses various machine learning and deep learning models to review the pros and cons of HAR applications. We also discuss practical considerations such as computational complexity, scalability, and real-time performance, highlighting challenges and opportunities for future research.