Human activity classification from wearable devices with cameras

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

1 Scopus citations

Abstract

There have been many approaches for human activity classification relying on accelerometer sensors, or cameras installed in the environment. There is relatively less work using egocentric videos. Accelerometer-only systems, although computationally efficient, are limited in the variety and complexity of the activities that they can detect. For instance, we can detect a sitting event by using accelerometer data, but cannot determine whether the user has sat on a chair or sofa, or what type of environment the user is in. In order to detect activities with more details and context, we present a robust and autonomous method using both accelerometer and ego-vision data obtained from a smart phone. A multi-class Support Vector Machine (SVM) is used to classify activities by using accelerometer data and optical flow vectors. Objects in the scene are detected from camera data by using an Aggregate Channel Features based detector. Another multi-class SVM is used to detect approaching different objects. Then, a Hidden Markov Model (HMM) is employed to detect more complex activities. Experiments have been conducted with subjects performing activities of sitting on chairs, sitting on sofas, and walking through doorways. The proposed method achieves overall precision and recall rates of 95% and 89%, respectively.

Original languageEnglish (US)
Title of host publicationConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages183-187
Number of pages5
Volume2017-October
ISBN (Electronic)9781538618233
DOIs
StatePublished - Apr 10 2018
Event51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States
Duration: Oct 29 2017Nov 1 2017

Other

Other51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
CountryUnited States
CityPacific Grove
Period10/29/1711/1/17

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ASJC Scopus subject areas

  • Control and Optimization
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Biomedical Engineering
  • Instrumentation

Cite this

Lu, Y., & Velipasalar, S. (2018). Human activity classification from wearable devices with cameras. In M. B. Matthews (Ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 (Vol. 2017-October, pp. 183-187). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.2017.8335163