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A Parallel Signal Detector Approach for Detection of Human Activities Using Multiple Seismic Sensors

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Emerging Electronics and Automation (E2A 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1088))

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Abstract

Intrusion detection systems play an important role in the protection of vital installations and automated monitoring of unmanned areas. Seismic sensors such as geophones are used for sensing the ground vibrations caused due to human activities. These sensors are being employed for the covert detection of seismic signatures. This study investigates an in-depth analysis of different parameters of a signal detection algorithm STA/LTA for efficient detection of ground vibrations caused due human activities. High false alarm rates frequently limit the effectiveness of unattended ground sensor (UGS) systems, presumably as a result of flaws in the underlying algorithms and constraints in onboard computation. A real-time energy-based parallel recursive STA/LTA algorithm using a 32-bit analog-to-digital converter (ADC) with an ARM microprocessor is developed. The real-time detected event data is utilized for the optimization of the STA window length, and threshold values. Machine learning models such as support vector machines (SVM) with different kernels, k-nearest neighbor (kNN), and random forest are trained to classify the human activities. A STA window length of 300 samples with triggering thresholds (Ton = 2.7 and Toff = 1.8) achieves highest detection F1 score of ~ 0.74 with 81.7% accuracy of random forest classifier.

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Acknowledgements

The authors acknowledge NITTTR Chandigarh and Centre of Excellence for Intelligent Sensors and Systems (iSenS), CSIR-CSIO, Chandigarh for providing the resources for carrying out this research activity. The research work has been partially supported from CSIR-CSIO’s In-house project Grant OLP246. The authors also acknowledge the contribution of research fellows and project associates of iSenS for their support during experiments.

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Authors and Affiliations

  1. National Institute of Technical Teachers Training and Research, Chandigarh, 160019, India

    Rahul Walia & Piush Verma

  2. CSIR—Central Scientific Instruments Organisation, Chandigarh, 160030, India

    Manjeet Singh & Ripul Ghosh

  3. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Manjeet Singh & Ripul Ghosh

Authors
  1. Rahul Walia
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  2. Manjeet Singh
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  3. Piush Verma
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  4. Ripul Ghosh
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Corresponding author

Correspondence to Ripul Ghosh .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland

    Moncef Gabbouj

  2. Division of Green Electronics, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Shyam Sudhir Pandey

  3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Hari Krishna Garg

  4. Department of EIE, National Institute of Technology (NIT) Silchar, Silchar, Assam, India

    Ranjay Hazra

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Walia, R., Singh, M., Verma, P., Ghosh, R. (2024). A Parallel Signal Detector Approach for Detection of Human Activities Using Multiple Seismic Sensors. In: Gabbouj, M., Pandey, S.S., Garg, H.K., Hazra, R. (eds) Emerging Electronics and Automation. E2A 2022. Lecture Notes in Electrical Engineering, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-99-6855-8_43

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