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Human activity recognition (HAR) by wearable sensor devices embedded in theInternet of things (IOT) can play a significant role in remote healthmonitoring and emergency notification, to provide healthcare of higherstandards. The purpose of this study is to investigate a human activityrecognition method of accrued decision accuracy and speed of execution to beapplicable in healthcare. This method classifies wearable sensor accelerationtime series data of human movement using efficient classifier combination offeature engineering-based and feature learning-based data representation.Leave-one-subject-out cross-validation of the method with data acquired from 44subjects wearing a single waist-worn accelerometer on a smart textile, andengaged in a variety of 10 activities, yields an average recognition rate of90%, performing significantly better than individual classifiers. The methodeasily accommodates functional and computational parallelization to bringexecution time significantly down.

Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers