Modeling Activity Recognition Using
نویسندگان
چکیده
Wearable technology presents a uniquely convenient and portable way to record physiological data from users, which could be used to monitor health or recreational activities. With increasing amounts of such data, it would be useful to automatically categorize a user’s activity based on this data. Our paper utilizes machine learning to classify user activity, and we compare the strengths and weaknesses of supervised and unsupervised learning approaches using LDA, SVM and Random Forest classifiers, and K-means clustering classifiers, respectively. We then discuss which of these algorithms show the best performance for general activity recognition.
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