The Machine Learning Models for Activity Recognition Applications with Wearable Sensors

The growing number of fitness trackers and dedicated smartphone applications indicate the importance of human activity recognition tasks in everyday life. Wearable devices tend to incorporate a broad spectrum of various sensors, such as an accelerometer, a gyroscope, a heart rate sensor, etc. It is essential to understand what type of data and algorithm contribute to a better overall activity recognition rate, especially for real-time activity recognition based on real-time data streams. In this paper, we focus on comparing and contrasting different machine learning techniques and gauge their performance in terms of human activity recognition accuracy. We investigate and analyze sensor mounting position, sensor types, and data type influence on activity recognition accuracy. Also, we render and analyze the benefits of using a RNN classifier over a more static classifier such as J48 and Naive Bayes algorithms in our empirical study.