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.
Keywords: Machine learning, Human activity recognition, IMU sensors.