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
  2. Department of Computer Science, Rama Devi Women’s University
    Bhubaneswar, India
    {panigrahichhabi, patibibudhendu}
  3. Department of Computer Science & Engineering, S’O’A Deemed to be University
    Bhubaneswar, India
  4. Department of Computer Science & Engineering, Gandhi Engineering College
    Bhubaneswar, India
  5. Department of Computer Science and Information Engineering, Providence University
    Taichung 43301
  6. iCETS, Infosys
    Bhubaneswar, India


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)

Publication information

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

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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),