Thermal sensor array (TSA) offers privacy-preserving, low-cost, and non-invasive features, which makes it suitable for various indoor applications such as anomaly detection, health monitoring, home security, and monitoring energy efficiency applications. Previous approaches to human-centred applications using the TSA usually relied on the use of a fixed sensor location to make the human-sensor distance and the human presence shape fixed. However, placing this sensor in different locations and new indoor environments can pose a significant challenge. In this paper, a novel framework based on a deep convolutional encoder-decoder network is proposed to address this challenge in real-life deployment. The framework presents a semantic segmentation of the human presence and estimates the occupancy in indoor-environment. It is also capable to segment the human presence and counts the number of people from different sensor locations, indoor environments, and human to sensor distance. Furthermore, the impact of the distance on the human presence using the TSA is investigated. The framework is evaluated to estimate the occupancy in different sensor locations, the number of occupants, environments, and human distance with classification and regression machine learning approaches. This paper shows that the classification approach using the adaptive boosting algorithm is an accurate approach which has achieves an accuracy of 98.43% and 100% from vertical and overhead sensor locations respectively.

No abstract

Thermal imaging has recently come to light to measure high human body temperature (fever) in responses to the global public health issues. This is normally achieved by very expensive high-resolution thermal cameras. Lately, there has been a new commercial low-resolution Thermal Sensor Array (TSA) that have gained growing interest in indoor human monitoring applications due to their low-cost and human privacy-preserving claims. However, there has not been sufficient independent empirical calibration of low-resolution TSA and high-resolution images for human-centred applications. This letter provides empirical calibration of low-and high-resolution thermal imaging techniques in terms of their visible outputs, accuracy in temperature values and stability. Besides, this letter assesses the claimed privacy-preserving feature of TSA by experimentally validating the possibility of revoking the human identity from the TSA's output. Thus, this letter aims to understand better the advantages, limitations, and future trends of using TSA in human monitoring applications.

Human distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of $$\pm 0.2$$ ± 0.2  m in continuous-based estimation and $$96.8\%$$ 96.8 % achieved-accuracy in discrete distance estimation.

To support the independent living of older adults in their own homes, it is essential to identify their abnormal behaviors before triggering an automated alert system. Existing normal vision sensing approaches to detect human falls in the activities of daily living (ADL) experienced acceptability issues due to outstanding privacy concerns when they are deployed in personal environments. Besides, false alerts (false-positive) fall detection has not been addressed thoroughly in systems that report abnormal human behaviors as emergency alerts to the information support. This article proposes a novel human-in-the-loop fall detection approach in the ADLs using a low-resolution thermal sensor array. The motivation for enabling a human interactive model, fall detection confirmation, is to influence resource efficiency by reducing false-positive alerts while keeping the false-negative fall predictions as low as possible. The proposed approach is based on the motion sequence classification of human movements using a recurrent neural network. The proposed approach is evaluated with comprehensive experiments using different learning techniques, users, and domestic environment conditions. This article shows a performance accuracy of 99.7% to detect human falls from various typical ADLs.