Multitask LSTM Model for Human Activity Recognition and Intensity Estimation Using Wearable Sensor Data

Abstract

Human activity recognition (HAR) and measuring the intensity of activity are increasingly important for healthcare applications, such as fitness tracking and patient monitoring. However, these two tasks have been performed separately, leading to delays and expensive implementations. In this article, we propose a holistic approach to achieve both HAR and intensity estimation simultaneously. We introduce a new data set with both activity types and activity intensities, and a multitask long short-term memory (LSTM) model to accurately classify the activity types and estimate the intensity of each activity. In addition, we evaluate our proposed neural network with other publicly available data sets and show that including activity intensities in the data set help multitask models perform comparably to two separate single-task models.