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
2771763340 2076068958
A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved. 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.