Background Subclinical (ie, threshold) social anxiety can greatly affect young people’s lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment, which would be greatly beneficial for persons with social anxiety, society, and health care services. Nevertheless, indicators such as skin temperature measured by wrist-worn sensors have not been used in prior work on physiological social anxiety detection. Objective This study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including heart rate, skin temperature, and electrodermal activity (EDA). Methods Young adults (N=12) with self-reported subclinical social anxiety (measured using the widely used self-reported version of the Liebowitz Social Anxiety Scale) participated in an impromptu speech task. Physiological data were collected using an E4 Empatica wearable device. Using the preprocessed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours (KNN) were used to develop models for 3 different contexts. Models were trained to differentiate (1) between baseline and socially anxious states, (2) among baseline, anticipation anxiety, and reactive anxiety states, and (3) social anxiety among individuals with social anxiety of differing severity. The predictive capability of the singular modalities was also explored in each of the 3 supervised learning experiments. The generalizability of the developed models was evaluated using 10-fold cross-validation as a performance index. Results With modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when differentiating between baseline and socially anxious states. Models trained to differentiate among baseline, anticipation anxiety, and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Furthermore, the models developed to differentiate between social anxiety experienced by individuals with anxiety of differing severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating anxiety among individuals with social anxiety of differing severity. Conclusions The results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors.
Abstract. Soon, pervasive computers will enormously outnumber humans. Devices requiring sufficient energy to operate maintenance-free for periods of years and beyond render today's technologies insufficient. With the gap between energy requirements of embedded systems and achievable levels of harvested power reducing, viable hybrid energy and power management subsystems have emerged that combine harvesting with finite, rechargeable energy buffers. Coupled with advances in wireless power transfer and energy storage, we propose that an energy design space is emerging. There are, as yet, no tools or systematic methods for design space exploration or engineering in this context. It is important to develop such a methodology, and critical to link it with methodologies for system design and verification. We discuss the key factors such an energy design methodology should incorporate, including size, weight, energy and power densities; efficiencies of harvesters and buffers; time between charges, (dis)charge speeds, and charge cycles; and availability and predictability of harvestable energy.Introduction. As Weiser's vision of ubiquitous computing continues to become reality, significant technical challenges remain. Chief among them is achieving autonomous and long-term operation without the use of wires or 'tethered' interfaces. From a communications perspective, solutions have emerged quickly: cellular, Wi-Fi, Bluetooth, RFID, IEEE 802.15.4, and LoRa, to name a few. However, there is growing interest in providing sustainable energy for pervasive computers using wireless interfaces -to increase autonomy, reduce clutter, reduce maintenance requirements and expand application potential. Given increasing acceptance in consumer electronics of the dedicated transmitterreceiver model, whereby power is intentionally transferred from a source to one or more receivers (e.g. Qi), it is worth investigating the model's applicability in complementary pervasive computing scenarios, such as applications using embedded sensors and actuators. This raises interesting design questions in terms of: the range of transmission of energy; the physical size of transmitters and receivers; how to efficiently manage on-board conversion, storage and management; and how to tackle heterogeneous mobility patterns where receivers may be in contact with sources for limited periods of time -due to being mobile devices themselves, or because they use mobile energy sources. There are also cases where both source and receiver(s) may be static, or both mobile (Fig. 1). The energy design space is growing, and wireless energy transfer is a promising candidate where recharging is necessary and feasible.
Many Industrial Internet of Things applications require autonomous operation and incorporate devices in inaccessible locations. Recent advances in wireless power transfer (WPT) and autonomous vehicle technologies, in combination, have the potential to solve a number of residual problems concerning the maintenance of, and data collection from embedded devices. Equipping inexpensive unmanned aerial vehicles (UAV) and embedded devices with subsystems to facilitate WPT allows a UAV to become a viable mobile power delivery vehicle (PDV) and data collection agent. A key challenge is therefore to ensure that a PDV can optimally schedule power delivery across the network, such that it is as reliable and resource efficient as possible. To achieve this and out-perform naive on-demand recharging strategies, we propose a two-stage wireless power network (WPN) approach in which a large network of devices may be grouped into small clusters, where packets of energy inductively delivered to each cluster by the PDV are acoustically distributed to devices within the cluster. We describe a novel dynamic recharge scheduling algorithm that combines genetic weighted clustering with nearest neighbour search to jointly minimize PDV travel distance and WPT losses. The efficacy and performance of the algorithm are evaluated in simulation using experimentally derived traces, and the algorithm is shown to achieve ∼90% throughput for large, dense networks.
Abstract-A variety of wireless networks, including applications of Wireless Sensor Networks, Internet of Things, Cyberphysical Systems, etc., increasingly pervade our homes, retail, transportation systems and manufacturing processes. Traditional approaches communicate data from all sensors to a central system, and users (humans or machines) query this central point for results, typically via the web. As the number of deployed sensors, thus generated data streams, is increasing exponentially, this traditional approach may no longer be sustainable, or desirable in some application contexts. Therefore, new approaches are required to allow users to directly interact with the network, for example requesting data directly from sensor nodes. This is difficult, as it requires every node to be capable of point-to-point routing, in addition to identifying a subset of nodes that can fulfil a user's query. This paper presents DRAGON, a platform that allows any node in the network to identify all nodes that satisfy user queries, i.e. request data from nodes, and relay the result to the user. The DRAGON platform achieves this in a fully distributed way. No central orchestration is required, network overheads are low, and latency is improved over existing comparable methods. DRAGON is evaluated on networks of various topologies and different network densities. It is compared to the state-of-the-art algorithms based on summary trees, like Innet and SENS-Join. DRAGON is shown to outperform these approaches up to 88% in terms of network traffic required, also a proxy for energy efficiency, and 84% in terms of processing delay. Note to Practitioners:Abstract-This work is motivated by the continuing deluge of constrained, wirelessly connected sensing and control devices. Networks of communicable sensors and actuators are finding increased applicability across a range of industries and application scenarios. They are often thought of as a subset of the Internet of Things. However, due to the inherent difficulty in building theses systems, technically and in terms of balancing the trade-offs between (economic) cost and performance (energy, latency, reliability, determinism), uptake has been slow. The community is relatively small, and therefore has not overcome all of the problems that present themselves considering required functionality of industrial applications. There is a need to find find new ways to interact with these devices, particularly those with heterogeneous attributes. There is also clear motivation to progress from traditional system architectures, whereby all data sensed are transmitted to centralised storage and management platform, to decentralised means of interrogation and control. This work proposes a solution to this problem, describing and evaluating a novel framework to query constrained networked devices based on two key improvements over the current art. The first is construction and management of a dynamic routing mechanism that facilitates the second; a method to store static attributes in a distributed m...
This paper presents the design and characterisation of a new low-power hardware platform to integrate unmanned aerial vehicle and wireless sensor technologies. In combination, these technologies can overcome data collection and maintenance problems of in situ monitoring in remote and extreme environments. Precision localisation in support of maximum efficiency mid-range inductive power transfer when recharging devices and increased throughput between drone and device are needed for data intensive monitoring applications, and to balance proximity time for devices powered by supercapacitors that recharge in seconds. The platform described in this paper incorporates ultra-wideband technology to achieve highperformance ranging and high data throughput. It enables the development of a new localisation system that is experimentally shown to improve accuracy by around two orders of magnitude to 10 cm with respect to GNSS and achieves almost 6 Mbps throughput in both lab and field conditions. These results are supported by extensive modelling and analysis. The platform is designed for application flexibility, and therefore includes a wide range of sensors and expansion possibilities, with source code for two applications made immediately available as part of a open source project to support research and development in this new area.
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