Location-based Daily Human Activity Recognition using Hybrid Deep Learning Network
Sakorn Mekruksavanich, Chanon Promsakon, Anuchit Jitpattanakul
Abstract
Human activity recognition (HAR) is an interesting and challenging subject of study. HAR provides useful information regarding human movement and activity in ordinary life. A number of HAR-based solutions such as wellness tracking and biometric identification systems have been introduced over the past decade. A number of deep learning algorithms have recently been employed to resolve the complication of handcrafted features in traditional machine learning approaches. The novel deep learning framework to solve the HAR effect on overall accuracy is proposed in this study. The framework is a location-based CNN-LSTM hybrid model. The framework is validated using evaluation measures such as accuracy and other effective measures on a public dataset of wristwatch accelerometer data named the DHA dataset. When comparing the accuracy of alternative deep learning approaches, the proposed location-based CNN-LSTM ranked highest with an accuracy of 96.75%.