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Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

Author

Listed:
  • Ahmad Jalal

    (Department of Computer Science, Air University, Islamabad 44000, Pakistan)

  • Mouazma Batool

    (Department of Computer Science, Air University, Islamabad 44000, Pakistan)

  • Kibum Kim

    (Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Korea)

Abstract

Human behavior modeling (HBM) is a challenging classification task for researchers seeking to develop sustainable systems that precisely monitor and record human life-logs. In recent years, several models have been proposed; however, HBM remains an inspiring problem that is only partly solved. This paper proposes a novel framework of human behavior modeling based on wearable inertial sensors; the system framework is composed of data acquisition, feature extraction, optimization and classification stages. First, inertial data is filtered via three different filters, i.e., Chebyshev, Elliptic and Bessel filters. Next, six different features from time and frequency domains are extracted to determine the maximum optimal values. Then, the Probability Based Incremental Learning (PBIL) optimizer and the K-Ary tree hashing classifier are applied to model different human activities. The proposed model is evaluated on two benchmark datasets, namely DALIAC and PAMPA2, and one self-annotated dataset, namely, IM-LifeLog, respectively. For evaluation, we used a leave-one-out cross validation scheme. The experimental results show that our model outperformed existing state-of-the-art methods with accuracy rates of 94.23%, 94.07% and 96.40% over DALIAC, PAMPA2 and IM-LifeLog datasets, respectively. The proposed system can be used in healthcare, physical activity detection, surveillance systems and medical fitness fields.

Suggested Citation

  • Ahmad Jalal & Mouazma Batool & Kibum Kim, 2020. "Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10324-:d:460031
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    References listed on IDEAS

    as
    1. Negar Golestani & Mahta Moghaddam, 2020. "Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    2. Asif Iqbal & Farman Ullah & Hafeez Anwar & Ata Ur Rehman & Kiran Shah & Ayesha Baig & Sajid Ali & Sangjo Yoo & Kyung Sup Kwak, 2020. "Wearable Internet-of-Things platform for human activity recognition and health care," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.
    3. Muhammad Arif & Ahmed Kattan, 2015. "Physical Activities Monitoring Using Wearable Acceleration Sensors Attached to the Body," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-16, July.
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    Citations

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    Cited by:

    1. Nida Khalid & Munkhjargal Gochoo & Ahmad Jalal & Kibum Kim, 2021. "Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System," Sustainability, MDPI, vol. 13(2), pages 1-30, January.
    2. Mahwish Pervaiz & Yazeed Yasin Ghadi & Munkhjargal Gochoo & Ahmad Jalal & Shaharyar Kamal & Dong-Seong Kim, 2021. "A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    3. Madiha Javeed & Munkhjargal Gochoo & Ahmad Jalal & Kibum Kim, 2021. "HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
    4. Hira Ansar & Ahmad Jalal & Munkhjargal Gochoo & Kibum Kim, 2021. "Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities," Sustainability, MDPI, vol. 13(5), pages 1-26, March.

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