Abstract

The study of human activity recognition (HAR) holds significant importance within wearable technology and ubiquitous computing, driven by the increasing ubiquity of inertial measurement unit (IMU) sensors embedded in devices like smartphones, smartwatches, and fitness trackers. The effective classification and recognition of human actions are crucial for various applications, including health monitoring, fitness tracking, and personalized user experiences. This study comprehensively examines the advancements in HAR by applying machine learning (ML) methodologies to data collected from IMU sensors. We explore seven powerful ML algorithms that have been pivotal in transforming raw sensor data into actionable insights for activity classification. These algorithms include decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), artificial neural networks (ANN), convolutional neural networks (CNN), and long short-term memory networks (LSTM). Each algorithm is assessed based on its ability to accurately process and classify various human activities, highlighting their strengths and limitations in different scenarios. Moreover, the study delves into the critical role of evaluation metrics and the confusion matrix in validating the performance of these ML models. Metrics such as accuracy, precision, recall, F1 score, and specificity are examined to provide a holistic view of the model's efficacy. The confusion matrix is emphasized as a tool for understanding the true positive, false positive, true negative, and false negative rates, offering insights into the practical performance of the models in realworld applications. Through this detailed investigation, we aim to shed light on the current state of HAR and the potential future directions for research and development in this dynamic field.