Leveraging multi-view learning for human anomaly detection in industrial internet of things

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

One of the most prominent human anomalous behaviour is fall. Fall detection for elderly care is important in the smart home care system. Fall detection is one of the smart home applications. In home-based assisted living, the detection of human anomalous behaviour is crucial. Home-based assisted living is becoming prevalent in elderly care. Aging brings several health issues and prevents the elderly to live an independent life. Therefore, it is important to detect human anomalous behaviour to ensure the safety of older adults. In this paper, we propose a multi-view learning approach to detect human anomalous behaviour. Our multi-view algorithms are running on edge devices. Multi-view learning is basically a method of learning from multiple views. It involves data fusion or integration from learned multiple feature sets. We evaluate our approaches for fall detection on the publicly available dataset "MobiFall-Dataset-v2.0". We construct 2 distinct views of accelerometer and gyroscope data and apply co-training methods to train the classifiers. We perform experiments on the extracted feature sets of two different views and combined them using 3 different methods. We introduce 3 feature fusion methods to leverage the inadequacy of single-view learning. Multi-view approach used in this work has an improvement over single view learning and produces better accuracy in classification of falls.

Original languageEnglish
Title of host publicationProceedings - IEEE Congress on Cybermatics
Subtitle of host publication2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020
Place of PublicationLos Alamitos, California
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages533-537
Number of pages5
ISBN (Electronic)9781728176475
DOIs
Publication statusPublished - 2020
Event2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020 - Rhodes Island, Greece
Duration: 2 Nov 20206 Nov 2020

Publication series

NameProceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartData 2020

Conference

Conference2020 IEEE Congress on Cybermatics: 13th IEEE International Conferences on Internet of Things, iThings 2020, 16th IEEE International Conference on Green Computing and Communications, GreenCom 2020, 13th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2020 and 6th IEEE International Conference on Smart Data, SmartData 2020
CountryGreece
CityRhodes Island
Period2/11/206/11/20

Keywords

  • Activity Recognition
  • Fall Detection
  • Multi-view Learning
  • Sensor Fusion

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