Classifying and Analyzing Physical Activities through Heart Rate Variability and Other Physical Metrics Using Holter Monitor Data
Abstract
This case study focuses on labeled data derived from a Holter monitor, a device that continuously measures and records heart activity, along with several other physical metrics. The primary objectives are twofold: first, to identify and explain similarities among the different types of physical activities based on the given metrics; second, to develop a predictive model that can accurately classify the type of activity given the recorded data. The research involves a detailed data preparation, including cleaning and extracting features, visualizing data by applying t-SNE and UMAP methods, and employing ensemble approach to build a model showing high precision, recall, and F1-scores in classifying various activities. This comprehensive approach demonstrates the efficacy of machine learning techniques that can be applied in health informatics and human activity recognition.
References
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