We present a novel hierarchical model for human activity recognition. In contrast with approaches that successively recognize actions and activities, our approach jointly models actions and activities in a unified framework, and their labels are simultaneously predicted. The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs. Although loops are present in the model, the model has an overall linear-chain structure, where the exact inference is tractable. Therefore, the model is very efficient in both inference and learning. The parameters of the graphical model are learned with a structured support vector machine. A data-driven approach is used to initialize the latent variables; therefore, no manual labeling for the latent states is required. The experimental results from using two benchmark datasets show that our model outperforms the state-of-the-art approach, and our model is computationally more efficient.
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We will demonstrate a prototype exergame aimed at the serious domain of elderly fitness. The exergame incorporates straightforward means to gesture recognition, and utilises a Kinect camera to obtain 2.5D sensory data of the human user.
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Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.
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This paper reports on the first stage of a research project1) that aims to incorporate objective measures of physical activity into health and lifestyle surveys. Physical activity is typically measured with questionnaires that are known to have measurement issues, and specifically, overestimate the amount of physical activity of the population. In a lab setting, 40 participants wore four different sensors on five different body parts, while performing various activities (sitting, standing, stepping with two intensities, bicycling with two intensities, walking stairs and jumping). During the first four activities, energy expenditure was measured by monitoring heart rate and the gas volume of in‐ and expired O2 and CO2. Participants subsequently wore two sensor systems (the ActivPAL on the thigh and the UKK on the waist) for a week. They also kept a diary keeping track of their physical activities, work and travel hours. Machine learning algorithms were trained with different methods to determine which sensor and which method was best able to differentiate the various activities and the intensity with which they were performed. It was found that the ActivPAL had the highest overall accuracy, possibly because the data generated on the upper tigh seems to be best distinguishing between different types of activities and therefore led to the highest accuracy. Accuracy could be slightly increased by including measures of heartrate. For recognizing intensity, three different measures were compared: allocation of MET values to activities (used by ActivPAL), median absolute deviation, and heart rate. It turns out that each method has merits and disadvantages, but median absolute deviation seems to be the most promishing metric. The search for the best method of gauging intensity is still ongoing. Subsequently, the algorithms developed for the lab data were used to determine physical activity in the week people wore the devices during their everyday activities. It quickly turned out that the models are far from ready to be used on free living data. Two approaches are suggested to remedy this: additional research with meticulously labelled free living data, e.g., by combining a Time Use Survey with accelerometer measurements. The second is to focus on better determining intensity of movement, e.g., with the help of unsupervised pattern recognition techniques. Accuracy was but one of the requirements for choosing a sensor system for subsequent research and ultimate implementation of sensor measurement in health surveys. Sensor position on the body, wearability, costs, usability, flexibility of analysis, response, and adherence to protocol equally determine the choice for a sensor. Also from these additional points of view, the activPAL is our sensor of choice.
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Over the past few years a growing number of artists have critiqued the ubiquity of identity recognition technologies. Specifically, the use of these technologies by state security programs, tech-giants and multinational corporations has met with opposition and controversy. A popular form of resistance to recognition technology is sought in strategies of masking and camouflage. Zach Blas, Leo Selvaggio, Sterling Crispin and Adam Harvey are among a group of internationally acclaimed artists who have developed subversive anti-facial recognition masks that disrupt identification technologies. This paper examines the ontological underpinnings of these popular and widely exhibited mask projects. Over and against a binary understanding and criticism of identity recognition technology, I propose to take a relational turn to reimagine these technologies not as an object for our eyes, but as a relationship between living organisms and things. A relational perspective cuts through dualist and anthropocentric conceptions of recognition technology opening pathways to intersectional forms of resistance and critique. Moreover, if human-machine relationships are to be understood as coming into being in mutual dependency, if the boundaries between online and offline are always already blurred, if the human and the machine live intertwined lives and it is no longer clear where the one stops and the other starts, we need to revise our understanding of the self. A relational understanding of recognition technology moves away from a notion of the self as an isolated and demarcated entity in favour of an understanding of the self as relationally connected, embedded and interdependent. This could alter the way we relate to machines and multiplies the lines of flight we can take out of a culture of calculated settings.
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Digital surveillance technologies using artificial intelligence (AI) tools such as computer vision and facial recognition are becoming cheaper and easier to integrate into governance practices worldwide. Morocco serves as an example of how such technologies are becoming key tools of governance in authoritarian contexts. Based on qualitative fieldwork including semi-structured interviews, observation, and extensive desk reviews, this chapter focusses on the role played by AI-enhanced technology in urban surveillance and the control of migration between the Moroccan–Spanish borders. Two cross-cutting issues emerge: first, while international donors provide funding for urban and border surveillance projects, their role in enforcing transparency mechanisms in their implementation remains limited; second, Morocco’s existing legal framework hinders any kind of public oversight. Video surveillance is treated as the sole prerogative of the security apparatus, and so far public actors have avoided to engage directly with the topic. The lack of institutional oversight and public debate on the matter raise serious concerns on the extent to which the deployment of such technologies affects citizens’ rights. AI-enhanced surveillance is thus an intrinsically transnational challenge in which private interests of economic gain and public interests of national security collide with citizens’ human rights across the Global North/Global South divide.
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Abstract: In this article I will reconstruct the Dutch debate on citizenship against the developing thinking about social citizenship in the context of globalization. I will show that the confusion around citizenship in the Netherlands can be seen as a result from a split between an unanimous negative valuation of Dutch citizen participation and the practice in which the participation of Dutch population is high. Recent theory on citizenship and participation localizes citizen participation, through membership, in the heart of citizenship practices (B.S. Turner). This can be understood as that all kinds of practices and activities can be viewed as citizenship practices and citizenship activities. From this perspective it becomes clear that in the Netherlands, due to the nation-state conceptualisation of citizenship social participation is not acknowledged as part of citizenship. In reverse, the case study learns about the theory of citizenship in practices that that theory functions as a framework that visualises different - sometimes conflicting - notions and practices of citizenship and thereby recognises all these as part of citizenship.
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Objective: To evaluate the preliminary effectiveness of a goal-directed movement intervention using a movement sensor on physical activity of hospitalized patients. Design: Prospective, pre-post study. Setting: A university medical center. Participants: Patients admitted to the pulmonology and nephrology/gastro-enterology wards. Intervention: The movement intervention consisted of (1) self-monitoring of patients' physical activity, (2) setting daily movement goals and (3) posters with exercises and walking routes. Physical activity was measured with a movement sensor (PAM AM400) which measures active minutes per day. Main measures: Primary outcome was the mean difference in active minutes per day pre- and post-implementation. Secondary outcomes were length of stay, discharge destination, immobility-related complications, physical functioning, perceived difficulty to move, 30-day readmission, 30-day mortality and the adoption of the intervention. Results: A total of 61 patients was included pre-implementation, and a total of 56 patients was included post-implementation. Pre-implementation, patients were active 38 ± 21 minutes (mean ± SD) per day, and post-implementation 50 ± 31 minutes per day (Δ12, P = 0.031). Perceived difficulty to move decreased from 3.4 to 1.7 (0-10) (Δ1.7, P = 0.008). No significant differences were found in other secondary outcomes. Conclusions: The goal-directed movement intervention seems to increase physical activity levels during hospitalization. Therefore, this intervention might be useful for other hospitals to stimulate inpatient physical activity.
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In case of a major cyber incident, organizations usually rely on external providers of Cyber Incident Response (CIR) services. CIR consultants operate in a dynamic and constantly changing environment in which they must actively engage in information management and problem solving while adapting to complex circumstances. In this challenging environment CIR consultants need to make critical decisions about what to advise clients that are impacted by a major cyber incident. Despite its relevance, CIR decision making is an understudied topic. The objective of this preliminary investigation is therefore to understand what decision-making strategies experienced CIR consultants use during challenging incidents and to offer suggestions for training and decision-aiding. A general understanding of operational decision making under pressure, uncertainty, and high stakes was established by reviewing the body of knowledge known as Naturalistic Decision Making (NDM). The general conclusion of NDM research is that experts usually make adequate decisions based on (fast) recognition of the situation and applying the most obvious (default) response pattern that has worked in similar situations in the past. In exceptional situations, however, this way of recognition-primed decision-making results in suboptimal decisions as experts are likely to miss conflicting cues once the situation is quickly recognized under pressure. Understanding the default response pattern and the rare occasions in which this response pattern could be ineffective is therefore key for improving and aiding cyber incident response decision making. Therefore, we interviewed six experienced CIR consultants and used the critical decision method (CDM) to learn how they made decisions under challenging conditions. The main conclusion is that the default response pattern for CIR consultants during cyber breaches is to reduce uncertainty as much as possible by gathering and investigating data and thus delay decision making about eradication until the investigation is completed. According to the respondents, this strategy usually works well and provides the most assurance that the threat actor can be completely removed from the network. However, the majority of respondents could recall at least one case in which this strategy (in hindsight) resulted in unnecessary theft of data or damage. Interestingly, this finding is strikingly different from other operational decision-making domains such as the military, police and fire service in which there is a general tendency to act rapidly instead of searching for more information. The main advice is that training and decision aiding of (novice) cyber incident responders should be aimed at the following: (a) make cyber incident responders aware of how recognition-primed decision making works; (b) discuss the default response strategy that typically works well in several scenarios; (c) explain the exception and how the exception can be recognized; (d) provide alternative response strategies that work better in exceptional situations.
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The enhancement of GPS technology enables the use of GPS devices not only as navigation and orientation tools, but also as instruments used to capture travelled routes: as sensors that measure activity on a city scale or the regional scale. TU Delft developed a process and database architecture for collecting data on pedestrian movement in three European city centres, Norwich, Rouen and Koblenz, and in another experiment for collecting activity data of 13 families in Almere (The Netherlands) for one week. The question posed in this paper is: what is the value of GPS as ‘sensor technology’ measuring activities of people? The conclusion is that GPS offers a widely useable instrument to collect invaluable spatial-temporal data on different scales and in different settings adding new layers of knowledge to urban studies, but the use of GPS-technology and deployment of GPS-devices still offers significant challenges for future research.
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