Affiliations: [a] Segmentation and Targeting, Commercial Analytics, IQVIA, USA
| [b]
International Institute of Health Management and Research, New Delhi, India
Correspondence:
[*]
Corresponding author. Harshit Jain, Segmentation and Targeting, Commercial Analytics, IQVIA, USA. E-mail: [email protected].
Abstract: User activity classification is one of the most popular research topic in the domain of health care and social care, since this automated technology can provide monitoring and understanding of activities of patients. Smartphone inbuilt sensors based User Activity Classifier (UAC) recognizes user activities using features extracted from sensors like accelerometer and gyroscope in build in smartphones. In this research paper, we are proposing a new user activity classifier system using Layer Recurrent Neural Network (LRNN) which is Artificial Neural Network (ANN). We utilize synthesized data, containing features of user activity classification system, extracted from the raw data recorded in smartphones. With these derived features, we train and test Layer Recurrent Neural Network classifier for user activity classifier. In order to evaluate this system, we have compared the performance of this Layer Recurrent Neural Network based user activity classifier against the convention Multilayer Perceptron (MLP) and Naive Bayes based user activity classifier. Test results show that the proposed Layer Recurrent Neural Network -based user activity classifier is able to recognize user activities reliably and outperforms the Multilayer Perceptron based user activity classifier. We have achieved the classification accuracy of 98.56% for the activities. The results are much more accurate than Multilayer Perceptron based classifier and Naive Bayes classifier.
