Title: LSTM-based statistical framework for human activity recognition using mobile sensor data

Authors: Y. Vijaya Durga; S. Venkatramaphanikumar; K.V. Krishna Kishore

Addresses: Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India ' Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India ' Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India

Abstract: Fall detection plays an essential part in the healthcare monitoring system and also helps the elderly and disabled people. In this paper, authors have proposed a methodology for an efficient detection of automatic fall, which can perceive every possible fall event by using human activity recognition (HAR). Eigen features and some other time related features are computed to the data, collected from the sensors associated with Android mobile devices. These features will be analysed to classify physical activities such as fall, walking, sitting, upstairs, downstairs, jogging, etc. In this work, long short-term memory (LSTM) Neural Network is used to classify human activities. Based on this, alerts will be generated in case of fall detection; otherwise, data will be archived for the future references. Performance of the proposed framework is evaluated on two benchmark datasets (WISDOM and UCI) and one real time tracked dataset. The accuracy of the proposed framework on tracked dataset is 91.48% and outperforms other classifiers.

Keywords: fall detection; smartphone sensors; activities of daily living; ADL; human activity recognition; HAR; long short-term memory classification.

DOI: 10.1504/IJAIP.2024.141527

International Journal of Advanced Intelligence Paradigms, 2024 Vol.29 No.1, pp.86 - 99

Received: 04 Sep 2018
Accepted: 30 Nov 2018
Published online: 23 Sep 2024 *

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