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Smartphone-Based Human Activity Pattern Identification Using Unsupervised Learning

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Proceedings of International Conference on Data Science and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 287))

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Abstract

To avail smart healthcare facilities, it is essential to accumulate meaningful information on a patient. Study of human activity patterns is one of the magnificent areas to accumulate such meaningful information. Supervised learning methods are widely used to classify various human activity patterns. In supervised learning, the generation of training dataset is laborious and repetitive work. In this paper, to avoid this labor-intensive work, we investigate a new approach using unsupervised learning method for smartphone sensor-based human activity pattern identification (HAPI). In this work, hierarchical clustering method is used to cluster similar pattern activities using in-built accelerometer and gyroscope sensors of smartphones. Experimental results using our self-collected dataset show that hierarchical clustering achieves more than 90% accuracy to identify human activity patterns without generating the training dataset manually.

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Authors and Affiliations

  1. Banasthali Vidyapith, Vanasthali, 304022, Rajasthan, India

    Dipanwita Thakur

  2. Maulana Abul Kalam Azad University of Technology, Kolkata, West Bangal, India

    Suparna Biswas

Authors
  1. Dipanwita Thakur
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  2. Suparna Biswas
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Corresponding author

Correspondence to Dipanwita Thakur .

Editor information

Editors and Affiliations

  1. Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sarbani Roy

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Chandreyee Chowdhury

  4. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Amir H. Gandomi

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Thakur, D., Biswas, S. (2022). Smartphone-Based Human Activity Pattern Identification Using Unsupervised Learning. In: Saraswat, M., Roy, S., Chowdhury, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 287. Springer, Singapore. https://doi.org/10.1007/978-981-16-5348-3_43

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