Home activity monitoring using low resolution infrared sensor array

Tao, Lili; Smith, Melvyn; Volonakis, Timothy; Tan, Bo; Jing, Yanguo; Chetty, Kevin; Smith, Melvyn

Abstract

Abstract. Action monitoring in a home environment provides important information for health monitoring and may serve as input into a smart home environment. Visual analysis using cameras can recognise actions in a complex scene, such as someones living room. However, although there the huge potential benefits and importance, specifically for health, cameras are not widely accepted because of privacy concerns. This paper recognises human activities using a sensor that retains privacy. The sensor is not only different by being thermal, but it is also of low resolution: 8x8 pixels. The combination of the thermal imaging, and the low spatial resolution ensures the privacy of individuals. We present an approach to recognise daily activities using this sensor based on a discrete cosine transform. We evaluate the proposed method on a state-of-the-art dataset and experimentally confirm that our approach outperforms the baseline method. We also introduce a new dataset, and evaluate the method on it. Here we show that the sensor is considered better at detecting the occurrence of falls and Activities of Daily Living. Our method achieves an overall accuracy of 87.50% across 7 activities
with a fall detection sensitivity of 100% and specificity of 99.21%.
Keywords: home monitoring, fall detection, low resolution sensor, thermal imaging, user privacy

• Presentation Conference Type: Conference Paper (published)
• Acceptance Date: Oct 13, 2018
• Online Publication Date: Nov 13, 2018
• Publication Date: 2018
• Deposit Date: Apr 30, 2021
• Series Title: Lecture Notes in Computer Science
• Keywords: home monitoring, fall detection, low resolution sensor, thermal imaging, user privacy
• Public URL: https://uwe-repository.worktribe.com/output/7319754