Exploratory Analysis of Smartphone Sensor Data for Human Activity Recognition

Abstract

Precise recognition of human activities in any smart environment such as smart homes or smart healthcare centers is vital for child care, elder care, disabled patient monitoring, self-management systems, safety, tracking healthcare functionality, etc. Automatic human activity recognition (HAR) based on smartphone sensor data is becoming widespread day by day. However, it is challenging to understand human activities using sensor data and machine learning and so the recognition accuracy of many state-of-the-art methods is relatively low. It requires high computational overhead to improve recognition accuracy. The goal of this paper is to use exploratory data analysis (EDA) to deal with this strain and after analyzing, visualizations and dimensionality reductions are obtained which assists in deciding the data mining techniques. The HAR method based on smartphone accelerometer and gyroscope sensors’ data, EDA, and prediction models proposed in this paper is a high-precision method, and its highest accuracy is 97.12% for the HAR smartphone dataset. Heterogeneous models-based two ensembles: stacking and voting are used in this study to identify human activities of daily living (ADL). Three estimators are used: Linear Discriminant Analysis, Linear Support Vector Machines, and Logistic Regression for both stacked and voting generalization. The experimental results show that the generalization algorithms provide an automatic and precise HAR system and can serve as a decision-making tool to identify ADL in any smart environment.

Keywords

• Activities of daily living
• exploratory data analysis
• hard voting
• heterogeneous model
• smartphone sensor
• stacked generalization