Computer Science and Information Systems 2024 Volume 21, Issue 1, Pages: 95-116
https://doi.org/10.2298/CSIS230622003B
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Activity recognition for elderly care using genetic search
Biswal Ankita (Dept. of Computer Science & Engineering, CUTM, Bhubaneswar, India), [email protected]
Panigrahi Chhabi Rani (Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India), [email protected]
Behera Anukampa (Department of Computer Science & Engineering, S’O’A Deemed to be University, Bhubaneswar, India), [email protected]
Nanda Sarmistha (Department of Computer Science & Engineering, Gandhi Engineering College, Bhubaneswar, India), [email protected]
Weng Tien-Hsiung (Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan), [email protected]
Pati Bibudhendu (Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India), [email protected]
Malu Chandan (iCETS, Infosys, Bhubaneswar, India), [email protected]
The advent of newer and better technologies has made Human Activity Recognition (HAR) highly essential in our daily lives. HAR is a classification problem where the activity of humans is classified by analyzing the data collected from various sources like sensors, cameras etc. for a period of time. In this work, we have proposed a model for activity recognition which will provide a substructure for the assisted living environment. We used a genetic search based feature selection for the management of the voluminous data generated from various embedded sensors such as accelerometer, gyroscope, etc. We evaluated the proposed model on a sensor-based dataset - Human Activities and Postural Transitions Recognition (HAPT) which is publically available. The proposed model yields an accuracy of 97.04% and is better as compared to the other existing classification algorithms on the basis of several considered evaluation metrics. In this paper, we have also presented a cloud based edge computing architecture for the deployment of the proposed model which will ensure faster and uninterrupted assisted living environment.
Keywords: Activity Recognition, HAR, Genetic Search Algorithm, HAPT, SMO, Edge Computing, Cloud Computing
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