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Graphology-based handwriting analysis to identify human behavior, irrespective of applications, is interesting. Unlike existing methods that use characters, words and sentences for behavioural analysis with human intervention, we propose an automatic method by analysing a few handwritten English lowercase characters from a to z to identify person behaviours. The proposed method extracts structural features, such as loops, slants, cursive, straight lines, stroke thickness, contour shapes, aspect ratio and other geometrical properties, from different zones of isolated character images to derive the hypothesis based on a dictionary of Graphological rules. The derived hypothesis has the ability to categorise the personal, positive, and negative social aspects of an individual. To evaluate the proposed method, an automatic system is developed which accepts characters from a to z written by different individuals across different genders and age groups. This automatic privacy projected system is available on the website (http://subha.pythonanywhere.com). For quantitative evaluation of the proposed method, several people are requested to use the system to check their characteristics with the system automatic response based on his/her handwriting by choosing to agree or disagree options. The automatic system receives 5300 responses from the users, for which, the proposed method achieves 86.70% accuracy.

Driver behaviour is an important factor in road safety. Computer vision techniques have been widely used to monitor the driver behaviour. The violation of privacy and the possibility of spoofing are two continuing challenges in camera‐based systems. To address these challenges, we propose an efficient approach to monitor and detect driver behaviour based on movement characteristics of the vehicle rather than the visual features of the driver. The main goal of this paper is to classify the driver behaviour into five classes: safe, distracted, aggressive, drunk, and drowsy driving. A lightweight 1D Convolutional Neural Network with high efficiency and low computational complexity is suggested to classify the driver behaviour. Experimental results confirm that our method could successfully classify behaviours of a driver with accuracy of 99.999%.

Non‐suicide self‐injury (NSSI) can be dangerous and difficult for guardians or caregivers to detect in time. NSSI refers to when people hurt themselves even though they have no wish to cause critical or long‐lasting hurt. To timely identify and effectively prevent NSSI in order to reduce the suicide rates of patients with a potential suicide risk, the detection of NSSI based on the spatiotemporal features of indoor activities is proposed. Firstly, an NSSI behaviour dataset is provided, and it includes four categories that can be used for scientific research on NSSI evaluation. Secondly, an NSSI detection algorithm based on the spatiotemporal features of indoor activities (NssiDetection) is proposed. NssiDetection calculates the human bounding box by using an object detection model and employs a behaviour detection model to extract the temporal and spatial features of NSSI behaviour. Thirdly, the optimal combination schemes of NssiDetection is investigated by checking its performance with different behaviour detection methods and training strategies. Lastly, a case study is performed by implementing an NSSI behaviour detection prototype system. The prototype system has a recognition accuracy of 84.18% for NSSI actions with new backgrounds, persons, or camera angles. The detection of NSSI based on the spatiotemporal features of indoor activities (NssiDetection) is proposed. NssiDetection is characterised by the use of temporal and spatial features to detect the NSSI behaviour. We investigate the optimal combination schemes of NssiDetection by checking its performance with different behaviour detection methods and training strategies. We perform a case study by implementing an NSSI behaviour detection prototype system, which shows a recognition accuracy of 84.18% for NSSI action with new backgrounds, persons or camera angles.

It is a basic task to measure the similarity between two uncertain concepts in many real-life artificial intelligence applications, such as image retrieval, collaborative filtering, public opinion guidance, and so on. As an important cognitive computing model, cloud model has been used in many fields of artificial intelligence. It can realise the bidirectional cognitive transformation between qualitative concept and quantitative data based on the theory of probability and fuzzy set. The similarity measure of two uncertain concepts is a fundamental issue in cloud model theory. Popular similarity measure methods of cloud model are surveyed in this study. Their limitations are analysed in detail. Some related future research topics are proposed.

The authors investigated the effect of gaze tracking of humans by an agent. Psychological studies have shown that the presence of others facilitates task with implying partnership. This effect also occurs if the presence of others is replaced with a robot. Furthermore, the previous research showed that being touched by a robot motivates a person. However, direct contact such as the touch of a robot has a risk of disturbing human work. The authors used gaze tracking for social facilitation because gaze tracking can also facilitate human efforts and implemented a social facilitation robot agent by using gaze tracking. The agent projects its face on a surface to show quickly changing expressions, which is difficult to achieve with ordinary robots. The agent can also express gaze tracking by implementing eye trackers. The authors prepared three conditions with monotonous tasks to compare the effects of gaze tracking: the agent gazes at the instructed point moves its gaze randomly and traces the gaze point of a participant. The results showed that the participants’ motivation increased if the agent is tracking gazed point by a human, even though there are no differences between the achievement scores of the task and the continuation time.

A general event data recorder is a device installed in automobiles to record information related to vehicle crashes or accidents. The data provide a better understanding of how certain crashes come about. This study made a prototype of a driving behaviour-based event data recorder (DBEDR), which provides the information of driving behaviours and a danger level. The authors approach is to recognise the seven behaviours: normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving and approaching the car in front by the hidden Markov models. All data were collected from a real vehicle and evaluated in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behaviour recognition. The danger level is inferred by fuzzy rules involved with the above behaviours. DBEDR recorded the recognised driving behaviours and the danger level, and the places were stored with the assistance of a global positioning system receiver. By integrating Google Maps, the locations, the driving behaviour occurrences, the danger level on the travel routes and the recorded images, the proposed DBEDR could be more useful compared with the traditional EDRs.

Naturalistic driving is an experimentation model that allows us to recognise the driving modes observing the driver's behaviour at the wheel of a set of people in natural conditions during long periods of observation. This research methodology aims at increasing the representativeness of the data collected in opposition to data stemming from highly controlled laboratory experiments. However, naturalistic driving research designs produce large volumes of data that are difficult to handle. Thus, it is very important to work with suitable methods for representing and interpreting data, allowing us to observe the variability of the results. The aim of this study is to implement a new methodology adapted to the particularities of the naturalistic method that allows us to retrieve the positioning information through a georeferencing process of the available data. This method is the first step (preprocessing) to achieve a more clear and intuitive representation (cartographic representation) using Geographic Information Systems (GIS).

The authors present a new approach to the modelling of human driving behaviour, which describes driving behaviour as the result of an optimisation process within the formal framework of hybrid automata. In contrast to most approaches, the aim is not to construct a (cognitive) model of a human driver, but to directly model driving behaviour. The authors assume human driving to be controlled by the anticipated outcomes of possible behaviours. These positive and negative outcomes are mapped onto a single theoretical variable – the so called reinforcement value. Behaviour is assumed to be chosen in such a way that the reinforcement value is optimised in any given situation. To formalise the authors models they use hybrid automata, which allow for both continuous variables and discrete states. The models are evaluated using simulations of the optimised driving behaviours. A car entering a freeway served as the scenario to demonstrate our approach. First results yield plausible predictions for car trajectories and the chronological sequence of speed, depending on the surrounding traffic, indicating the feasibility of the approach.

The everyday life of a prison is not only shaped by the objective of resocialisation but also, as a rule, by aspects of security, which have the highest priority. A particular challenge is the prompt detection of potentially dangerous behavior. In the face of scarce human resources and psychologically limited attention, the use of video live streams is not an optimal solution. An alternative are 3D sensors, which enable the automatic and robust detection of such behavior in detention rooms and other areas such as hospital wards or workshops, while respecting the privacy of the monitored people. In this paper we present our research on this matter, based on realistic data that was acquired in an Austrian prison over 3.5 months. We discuss the recording setup and resulting dataset, and present algorithms for detecting selected behaviors. The experimental results show that these behaviors can be detected reliably, demonstrating that automatic behavior analysis in 3D data is a promising means for supporting the security personnel.