Comparison of machine learning techniques for the identification of human activities from inertial sensors available in a mobile device after the application of data imputation techniques. (August 2021)

• Record Type: Journal Article
• Authors: Pires, Ivan Miguel; Hussain, Faisal; Marques, Gonçalo; Garcia, Nuno M.
• Is Part Of: Computers in biology and medicine. Volume 135(2021)
• Publication Date: 2021-08
• Subjects: Human activities recognition -- Machine learning -- Mobile sensors -- Identification of human daily living activities; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104638
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Human activity recognition (HAR) is a significant research area due to its wide range of applications in intelligent health systems, security, and entertainment games. Over the past few years, many studies have recognized human daily living activities using different machine learning approaches. However, the performance of a machine learning algorithm varies based on the sensing device type, the number of sensors in that device, and the position of the underlying sensing device. Moreover, the incomplete activities ( i.e ., data captures) in a dataset also play a crucial role in the performance of machine learning algorithms. Therefore, we perform a comparative analysis of eight commonly used machine learning algorithms in different sensor combinations in this work. We used a publicly available mobile sensors dataset and applied the k-Nearest Neighbors (KNN) data imputation technique for extrapolating the missing samples. Afterward, we performed a couple of experiments to figure out which algorithm performs best at which sensors' data combination. The experimental analysis reveals that the AdaBoost algorithm outperformed all machine learning algorithms for recognizing five different human daily living activities with both single and multi-sensor combinations. Furthermore, the experimental results show that AdaBoost is capable to correctly identify all the activities presented in the dataset with 100% classification accuracy. Highlights: This paper proposes the implementation of different machine learning methods the recognition of Activities of Daily Living. This paper fuses the accelerometer, magnetometer and gyroscope sensors' data after the imputation of the sensors' data. The comparison of three different implemented method was performed. The use of machine learning methods for the recognition of Activities of Daily Living achieved consistent results.