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Activity Recognition for Elderly Care Using Genetic Search

Ankita Biswal¹, Chhabi Rani Panigrahi², Anukampa Behera³, Sarmistha Nanda⁴, Tien-Hsiung Weng⁵, Bibudhendu Pati² and Chandan Malu⁶

1. Dept. of Computer Science & Engineering, CUTM
   Bhubaneswar, India
   ankitabiswal94371@gmail.com
2. Department of Computer Science, Rama Devi Women’s University
   Bhubaneswar, India
   {panigrahichhabi, patibibudhendu}@gmail.com
3. Department of Computer Science & Engineering, S’O’A Deemed to be University
   Bhubaneswar, India
   anukampa1@gmail.com
4. Department of Computer Science & Engineering, Gandhi Engineering College
   Bhubaneswar, India
   sarmisthananda@gmail.com
5. Department of Computer Science and Information Engineering, Providence University
   Taichung 43301
   thweng@gm.pu.edu.tw
6. iCETS, Infosys
   Bhubaneswar, India
   chandanmalu@gmail.com

Abstract

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.

Key words

Activity Recognition; HAR; Genetic Search Algorithm; HAPT; SMO; Edge Computing; Cloud Computing

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS230622003B

Publication information

Volume 21, Issue 1 (January 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Biswal, A., Panigrahi, C. R., Behera, A., Nanda, S., Weng, T., Pati, B., Malu, C.: Activity Recognition for Elderly Care Using Genetic Search. Computer Science and Information Systems, Vol. 21, No. 1, 95–116. (2024), https://doi.org/10.2298/CSIS230622003B