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 .
An infrared sensor array is a device composed of a small number of discrete infrared sensors. It represents the spatial distribution of temperature as a low-resolution image. Unlike colour cameras, infrared sensor arrays only capture the shape of the human body, therefore making individual identification harder. Additionally, the low spatial resolution also makes identification of individuals difficult. As this is more comfortable for users, it becomes more acceptable for installation in residential environments. Such infrared sensor arrays can be applied in many scenarios. A 4x4 sensor array has been used to recognise hand motion directions @cite_5 , although the extremely low resolution of this sensor renders it unsuitable for more complex visual tasks. A 8x8 pixel sensor array has been successfully used to detect, count and track people indoors @cite_6 . Human movements has also be inferred by using the subject's location and moving trajectory using a 16x16 sensor array @cite_10 . Most recently, a multi-sensor system has been designed for human movement detection and activity recognition @cite_1 , which our method will be compared against.
@cite_1 @cite_5 @cite_10 @cite_6
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We propose a human body tracking method using a far-infrared sensor array. A far-infrared sensor array captures the spatial distribution of temperature as a low-resolution image. Since it is difficult to identify a person from the low-resolution thermal image, we can avoid privacy issues. Therefore, it is expected to be applied for the analysis of human behaviors in various places. However, it is difficult to accurately track humans because of the lack of information sufficient to describe the feature of the target human body in the low-resolution thermal image. In order to solve this problem, we propose a thermo-spatial sensitive histogram suitable to represent the target in the low-resolution thermal image. Unlike the conventional histograms, in case of the thermo-spatial sensitive histogram, a voting value is weighted depending on the distance to the target’s position and the difference from the target’s temperature. This histogram allows the accurate tracking by representing the target with multiple histograms and reducing the influence of the background pixels. Based on this histogram, the proposed method tracks humans robustly to occlusions, pose variations, and background clutters. We demonstrate the effectiveness of the method through an experiment using various image sequences. Low Resolution Thermal Array Sensors are widely used in several applications in indoor environments. In particular, one of these cheap, small and unobtrusive sensors provides a low-resolution thermal image of the environment and, unlike cameras; it is capable to detect human heat emission even in dark rooms. The obtained thermal data can be used to monitor older seniors while they are performing daily activities at home, to detect critical situations such as falls. Most of the studies in activity recognition using Thermal Array Sensors require human detection techniques to recognize humans passing in the sensor field of view. This paper aims to improve the accuracy of the algorithms used so far by considering the temperature environment variation. This method leverages an adaptive background estimation and a noise removal technique based on Kalman Filter. In order to properly validate the system, a novel installation of a single sensor has been implemented in a smart environment: the obtained results show an improvement in human detection accuracy with respect to the state of the art, especially in case of disturbed environments. Indoor tracking has all-pervasive applications beyond mere surveillance, for example in education, health monitoring, marketing, energy management and so on. Image and video based tracking systems are intrusive. Thermal array sensors on the other hand can provide coarse-grained tracking while preserving privacy of the subjects. The goal of the project is to facilitate motion detection and group proxemics modeling using an 8 x 8 infrared sensor array. Each of the 8 x 8 pixels is a temperature reading in Fahrenheit. We refer to each 8 x 8 matrix as a scene. We collected approximately 902 scenes with different configurations of human groups and different walking directions. We infer direction of motion of a subject across a set of scenes as left-to-right, right-to-left, up-to-down and down-to-up using cross-correlation analysis. We used features from connected component analysis of each background subtracted scene and performed Support Vector Machine classification to estimate number of instances of human subjects in the scene. In this paper we present our work towards a hand gesture recognition system realised with a passive thermal infrared sensor array. In contrast with the majority of recent research activities into gesture recognition, which focus on the complex analysis of video sequences, our approach shows that the functionality of a simple pyroelectric movement sensor can be expanded to detect differing hand gestures at short range. We show that blob detection from a hand waving over a 16 element passive infrared sensor array provides sufficient information to discriminate four directions of hand stroke. This sensor system is unique and lends itself to low cost, low profile and low power applications. Keywords-touchless input device; dynamic hand gesture; infrared motion sensor; infrared sensor array; pyroelectricity;