A comparative analysis of classification techniques for human activity recognition using wearable sensors and smart-phones

Abstract

INTRODUCTION: In these days, the usage of smart-phones and wearable sensors have increased at an exceptional rate. These smart devices are equipped with different sensors such as gyroscope, accelerometer and GPS. By using these sensors to analyze the activity of the end-user, behavioural characteristics of the user can be captured. OBJECTIVES: Although smart-phone and wearable devices provide a platform for conducting social, psychological and physical studies, they still have several limitations and challenges. METHODS: This paper provides a comparative analysis of different classical Machine Learning and Deep Learning algorithms and discusses their accuracy and efficiency for human activity recognition (HAR). RESULTS and CONCLUSION: The paper has primarily used the data captured using wireless sensor devices placed on different parts of a human body, and then compared the results for different classifiers. The conclusion shows that Deep learning schemes are extremely accurate and efficient in comparison with classical machine learning techniques.

Keywords

• human activity recognition
• multi-class classification
• smart-phone
• wearable sensors
• classifiers
• sensor devices
• business intelligence