DEEP LEARNING FOR RECOGNIZING DAILY HUMAN ACTIVITIES USING SMART HOME SENSORS

Abstract

One of the vital purposes of health-related studies is to enhance people’s living conditions and well-being. Solutions for smart homes could offer occupants preventive care based on the identification of regular activities. Recent advancements and developments in sensor technology have raised the demand for intelligent household products and services. The rising volume of data necessitates the development of the deep learning domain for the automated identification of human motions. Moreover, networks with long short-term memory have been used to represent spatio-temporal sequences recorded by smart home sensors. This study proposed ResNeXt-based models that learn to identify human behaviors in smart homes to increase detection capability. Experiment findings generated on a publicly available benchmark dataset known as CASAS data demonstrate that ResNeXt-based techniques surpass conventional DL approaches, achieving improved outcomes compared to the existing research. ResNeXt outperformed the benchmark approach by an average of 84.81%, 93.57%, and 90.38% for the CASAS Cairo, CASAS Milan, and CASAS Kyoto3 datasets, respectively.