Anti-cheating: Detecting self-inflicted and impersonator cheaters for remote health monitoring systems with wearable sensors

Nabil Alshurafa*, Jo Ann Eastwood, Mohammad Pourhomayoun, Suneil Nyamathi, Lily Bao, Bobak Mortazavi, Majid Sarrafzadeh

*Corresponding author for this work

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

19 Scopus citations

Abstract

In remote health monitoring of patient's physical activity, ensuring correctness and authenticity of the received data is essential. Although many activity monitoring systems, devices and techniques have been developed, preventing patient cheating of an activity monitor has been a primarily unaddressed challenge across the board. Patients can manually shake an activity monitor device (sensor) with their hand and watch their physical activity points or rewards increase, we define this as 'self-inflicted' cheating. A second type of cheating, 'impersonator' cheating, is when subjects hand the activity sensor over to a friend or second party to wear and perform physical activity on their behalf. In this paper, we propose two novel methods based on classification algorithms to address the cheating problems. The first classification framework improves the correctness of our data by detecting self-inflicted cheatings. The second technique is an advanced classification scheme that extracts and learns unique patient-specific activity patterns from prior data collected on a patient to distinguish the true subject from an impersonator. We tested our proposed techniques on Wanda, a remote health monitoring system used in our Women's Heart Health study of 90 African American women at risk of cardiovascular disease. We were able to distinguish cheating from other physical activities such as walking and running, as well as other common activities of daily living such as driving and playing video games. The self-inflicted cheating classifier achieved an accuracy of above 90% and an AUC of 99%. The impersonator cheater framework results in an average accuracy of above 90% and an average AUC of 94%. Our results provide insight into the randomness of cheating activities, successfully detects cheaters, and attempts to build more context-aware remote activity monitors that more accurately capture patient activity.

Original languageEnglish (US)
Title of host publicationProceedings - 11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014
PublisherIEEE Computer Society
Pages92-97
Number of pages6
ISBN (Print)9781479949328
DOIs
StatePublished - Jan 1 2014
Event11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014 - Zurich, Switzerland
Duration: Jun 16 2014Jun 19 2014

Other

Other11th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2014
CountrySwitzerland
CityZurich
Period6/16/146/19/14

Keywords

  • Activity Recognition
  • Cheating Detection
  • Feature Selection
  • Remote Health Monitoring System
  • Wearable Body Sensors

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Human-Computer Interaction
  • Hardware and Architecture

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