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 .
A large amount of research is underway in the development of a smart sensing system to detect falls in home environments. However, the use of thermal infrared arrays for fall detection has to date not been widely investigated. Although a real-time system to recognise fall and non-fall events has been presented in @cite_13 , their study overlooks the complexity of non-fall actions, where some actions, such as sitting down and inactivity, can be confused with falling @cite_3 . Taking this into consideration, various non-fall activities are specifically incorporated in our dataset, including those most likely to be confused with falling.
@cite_13 @cite_3
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This paper presents new approach for unobtrusive indoor fall detection by an IR thermal array sensor. Unlike existing methods that run fall detection at server and require high communication and processing rates, we perform fall detection within the sensor node by a computationally inexpensive algorithm that signals the server only when a fall occurs. Experiments with prototype design show that such formulation provides robust and real-time fall detection even in a noisy environment. As we grow old, our desire for independence does not diminish; yet our health increasingly needs to be monitored. Injuries such as falling can be a serious problem for the elderly. If a person falls and is not able to get assistance within an hour, casualties arising from that fall can result in fatalities as early as 6 months later [1]. It would seem then that a choice between safety and independence must be made. Fortunately, as health care technology advances, simple devices can be made to detect or even predict falls in the elderly, which could easily save lives without too much intrusion on their independence. Much research has been done on the topic of fall detection and fall prediction. Some have attempted to detect falls using a variety of sensors such as: cameras, accelerometers, gyroscopes, microphones, or a combination of the like. This paper is aimed at reporting which existing methods have been found effective by others, as well as documenting the findings of our own experiments. The combination of which will assist in the progression towards a safe, unobtrusive monitoring system for independent seniors.