AppWatchSecurity: Improving a Video Surveillance System by Integrating Smartwatch-Based Arousal Detection

Abstract

This paper proposes a prototype system that collaborates smartwatches with traditional video surveillance security. By combining concepts of user-centered design, ubiquitous wearable, psychophysiology and Internet of Things (IoTs), we present the upgraded video surveillance system where heart rate-based anomalies can automatically trigger the alarm. As a first prototype, the system was limited to library-like experimental setups and the anomaly was defined by arousal heart rate—unusually high heart beats. Using a smartwatch and a simple three-question questionnaire, we were able to collect referential arousal heart rate data from 25 healthy subjects together with their individual rating scores regarding three habit factors—smoking, drinking alcohol and eating fatty foods. According to our semi-quantitative user testings in a controlled library environment, the prototype was able to wirelessly connect and synchronize all devices, send the alarm, and perform real-time heart rate measurement as well as calculation. Based on confusion matrix evaluation, our anomaly detection gave promising results of 95 % accuracy and 90 % precision. However, major revision was required for the anomaly detection to cover unobserved factors, and there were serious usability problems regarding the smartwatch to be fixed.