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Involving and engaging people with intellectual disabilities on issues relating to their mental wellbeing is essential if relevant tools and solutions are to be developed. This research explores how inclusive and participatory co-design techniques and principles can be used to engage people with intellectual disabilities in designing innovations in mental wellbeing tangible technologies. In particular, individuals with intellectual disabilities participated in a co-design process via a series of workshops and focus groups to design tangible interfaces for mental wellbeing as their wellbeing challenges are often diagnostically overshadowed. The workshops helped participants explore new technologies, including sensors and feedback mechanisms that can help monitor and potentially improve mental wellbeing. The adopted co-design approach resulted in a range of effective and suitable interfaces being developed for varying ages. [ABSTRACT FROM AUTHOR]

In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches. Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of two popular types of deep neural networks running on five custom built devices and a comparative mobile app (68.5–89% accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This exploratory work illustrates several key features that inform the design of data collection tools that can help researchers select and apply appropriate labelling techniques to their work. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist in building adaptive, high-performance edge solutions. [ABSTRACT FROM AUTHOR]

Highlights • First hybrid Deep Learning approach applied to environmental and on-body sensor data. • Deep-learning is effective in human emotion classification. • Multi-model data fusion approach performs better than single modality classification. • Hybrid approach with combined CNN and LSTM exceed the performance of CNN alone. Abstract The detection and monitoring of emotions are important in various applications, e.g., to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g., EEG and GSR), environmental data (e.g., audio and weather), videos (e.g., for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%) [ABSTRACT FROM AUTHOR]

In recent years, mobile phone technology has taken tremendous leaps and bounds to enable all types of sensing applications and interaction methods, including mobile journaling and self-reporting to add metadata and to label sensor data streams. Mobile self-report techniques are used to record user ratings of their experiences during structured studies, instead of traditional paper-based surveys. These techniques can be timely and convenient when data are collected "in the wild". This paper proposes three new viable methods for mobile self-reporting projects and in real-life settings such as recording weather information or urban noise mapping. These techniques are Volume Buttons control, NFC-on-Body, and NFC-on-Wall. This work also provides an experimental and comparative analysis of various self-report techniques regarding user preferences and submission rates based on a series of user experiments. The statistical analysis of our data showed that pressing screen buttons and screen touch allowed for higher labelling rates, while Volume Buttons proved to be more valuable when users engaged in other activities, e.g. while walking. Similarly, based on participants' preferences, we found that NFC labelling was also an easy and intuitive technique when used in the context of self-reporting and place-tagging. Our hope is that by reviewing current self-reporting interfaces and user requirements, we will be able to enable new forms of self-reporting technologies that were not possible before. [ABSTRACT FROM AUTHOR]

Over the past few years, there has been a noticeable advancement in environmental models and information fusion systems taking advantage of the recent developments in sensor and mobile technologies. However, little attention has been paid so far to quantifying the relationship between environment changes and their impact on our bodies in real-life settings. In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion. We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: (1) model the short term impact of the ambient environment on human body, (2) predict emotions based on-body sensors and environmental data. To achieve this, we have conducted a real-world study ‘in the wild’ with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models. Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA ). Air pressure demonstrated the greatest contribution towards the detected changes in body temperature and motion. Also, significant correlation was found between air pressure and heart rate . Finally, decision fusion of the classification results from different modalities is performed. To the best of our knowledge this work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting. [ABSTRACT FROM AUTHOR]

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture. [ABSTRACT FROM AUTHOR]

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely 'sensing,' 'analysis,' and 'application.' Sensing investigates the different sensing modalities that are used in existing real-time affective applications, analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing. [ABSTRACT FROM AUTHOR]

In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest. [ABSTRACT FROM AUTHOR]

Mobile sensing and mapping applications are becoming more prevalent because sensing hardware is becoming more portable and more affordable. However, most of the hardware uses small numbers of fixed sensors that report and share multiple sets of environmental data which raises privacy concerns. Instead, these systems can be decentralized and managed by individuals in their public and private spaces. This paper describes a robust system called MobGeoSens which enables individuals to monitor their local environment (e.g. pollution and temperature) and their private spaces (e.g. activities and health) by using mobile phones in their day to day life. The MobGeoSen is a combination of software components that facilitates the phone’s internal sensing devices (e.g. microphone and camera) and external wireless sensors (e.g. data loggers and GPS receivers) for data collection. It also adds a new dimension of spatial localization to the data collection process and provides the user with both textual and spatial cartographic displays. While collecting the data, individuals can interactively add annotations and photos which are automatically added and integrated in the visualization file/log. This makes it easy to visualize the data, photos and annotations on a spatial and temporal visualization tool. In addition, the paper will present ways in which mobile phones can be used as noise sensors using an on-device microphone. Finally, we present our experiences with school children using the above mentioned system to measure their exposure to environmental pollution. [ABSTRACT FROM AUTHOR]

Automatic recognition of human emotions is not a trivial process. There are many factors affecting emotions internally and externally. Expressing emotions could also be performed in many ways such as text, speech, body gestures or even physiologically by physiological body responses. Emotion detection enables many applications such as adaptive user interfaces, interactive games, and human robot interaction and many more. The availability of advanced technologies such as mobiles, sensors, and data analytics tools led to the ability to collect data from various sources, which enabled researchers to predict human emotions accurately. Most current research uses them in the lab experiments for data collection. In this work, we use direct and real time sensor data to construct a subject-independent (generic) multi-modal emotion prediction model. This research integrates both on-body physiological markers, surrounding sensory data, and emotion measurements to achieve the following goals: (1) Collecting a multi-modal data set including environmental, body responses, and emotions. (2) Creating subject-independent Predictive models of emotional states based on fusing environmental and physiological variables. (3) Assessing ensemble learning methods and comparing their performance for creating a generic subject-independent model for emotion recognition with high accuracy and comparing the results with previous similar research. To achieve that, we conducted a real-world study "in the wild" with physiological and mobile sensors. Collecting the data-set is coming from participants walking around Minia university campus to create accurate predictive models. Various ensemble learning models (Bagging, Boosting, and Stacking) have been used, combining the following base algorithms (K Nearest Neighbor KNN, Decision Tree DT, Random Forest RF, and Support Vector Machine SVM) as base learners and DT as a meta-classifier. The results showed that, the ensemble stacking learner technique gave the best accuracy of 98.2% compared with other variants of ensemble learning methods. On the contrary, bagging and boosting methods gave (96.4%) and (96.6%) accuracy levels respectively. [ABSTRACT FROM AUTHOR]

COVID-19 has shown a relatively low case fatality rate in young healthy individuals, with the majority of this group being asymptomatic or having mild symptoms. However, the severity of the disease among the elderly as well as in individuals with underlying health conditions has caused significant mortality rates worldwide. Understanding this variance amongst different sectors of society and modelling this will enable the different levels of risk to be determined to enable strategies to be applied to different groups. Long-established compartmental epidemiological models like SIR and SEIR do not account for the variability encountered in the severity of the SARS-CoV-2 disease across different population groups. The objective of this study is to investigate how a reduction in the exposure of vulnerable individuals to COVID-19 can minimise the number of deaths caused by the disease, using the UK as a case study. To overcome the limitation of long-established compartmental epidemiological models, it is proposed that a modified model, namely SEIR-v, through which the population is separated into two groups regarding their vulnerability to SARS-CoV-2 is applied. This enables the analysis of the spread of the epidemic when different contention measures are applied to different groups in society regarding their vulnerability to the disease. A Monte Carlo simulation (100,000 runs) along the proposed SEIR-v model is used to study the number of deaths which could be avoided as a function of the decrease in the exposure of vulnerable individuals to the disease. The results indicate a large number of deaths could be avoided by a slight realistic decrease in the exposure of vulnerable groups to the disease. The mean values across the simulations indicate 3681 and 7460 lives could be saved when such exposure is reduced by 10% and 20% respectively. From the encouraging results of the modelling a number of mechanisms are proposed to limit the exposure of vulnerable individuals to the disease. One option could be the provision of a wristband to vulnerable people and those without a smartphone and contact-tracing app, filling the gap created by systems relying on smartphone apps only. By combining very dense contact tracing data from smartphone apps and wristband signals with information about infection status and symptoms, vulnerable people can be protected and kept safer. [ABSTRACT FROM AUTHOR]

A correction to the article “Emotions in context: examining pervasive affective sensing systems, applications, and analyses" that was published in the August 20, 2015 issue is presented.

This work applies a knowledge modelling approach in the design of a framework for the generation of travel demand for traffic simulation applications. The proposed framework is based on interchangeable modules that integrate the main stages of travel demand modelling supported by an engineered knowledge base. This approach is intended to promote greater behavioural diversity, incorporate more diverse contextual data, facilitate access to online datasets, and support users to undertake and validate investigations across a range of models and implementations. The framework provides the user with direct control to modify the schema, control the selection of data, select alternative modules to execute, and the potential to remotely retrieve data and execute modules. The framework is investigated through a prototype, which generated travel demand across a full day and performed simulation utilising two third-party traffic simulators based on the configuration by the user schema. The problem of travel demand generation was separated into discrete task modules with identification of features for alternative design or further modularisation. The prototype evaluation generated multimode travel demand that was successfully tested on third-party traffic simulators and evidenced the fitness of semantic technologies in building simulator-agnostic interchangeable framework modules that satisfy the need for configurable travel modelling. The findings of the paper also contribute to the understanding of the challenges in utilising Semantic Web technologies for implementing travel demand generation. It is proposed that the framework provides a basis to develop new and existing approaches to travel demand generation to improve modelling outcomes and adoption. [ABSTRACT FROM AUTHOR]

Given the prominent role that smartphones have in everyday life, research in the field has proliferated. From a theoretical perspective, problematic smartphone use (PSPU) is described as a multi-faceted phenomenon entailing a variety of dysfunctional manifestations (e.g., addictive, antisocial and dangerous use). To date, however, there is still a lack of empirical evidence supporting the identification of PSPU as a potential behavioural addiction. Driven by theory, the aim of the present study was to provide an empirically validated model by testing the contribution of specific factors leading to PSPU. Relationships among individual characteristics (internalised psychopathology, impulsivity and personality traits) and PSPU uses (addictive, antisocial and dangerous) were investigated according to the updated version of the theoretical framework provided by the Pathway Model of problematic smartphone use (Billieux et al., 2015). An online survey was administered to a convenience sample (N = 511) of smartphone users in order to examine their daily engagement, problematic usage patterns and related psychological correlates. Path analysis revealed important information about different PSPU components and results are discussed in light of the available literature. Recommendations for future research are proposed to further investigate the problematic behaviour, including the study of additional variables, such as the fear of missing out (FoMO), nomophobia and excessive social media use. • Problematic smartphone use (PSPU) entails addictive use, prohibited use, and risky behaviors. • The tested PSPU pathway model showed an overall reasonable model fit. • Addictive use is linked to excessive reassurance sought via smartphone use. • Internalised psychopathology did not predict smartphone addiction. • Attention impulsivity predicted addictive and antisocial use. [ABSTRACT FROM AUTHOR]

Participate was a 3-year collaboration between industry and academia to explore how mobile, Web and broadcast technologies could combine to deliver environmental campaigns. In a series of pilot projects, schools used mobile sensors to enhance science learning; visitors to an ecological attraction employed mobile phones to access and generate locative media; and the public played a mobile phone game that challenged their environmental behaviours. Key elements of these were carried forward into an integrated trial in which participants were assigned a series of environmental missions as part of an overarching narrative that was delivered across mobile, broadcast and Web platforms. These experiences use a three-layered structure for campaigns that draw on experts, local groups and the general public, who engage through a combination of playful characterisation and social networking. [ABSTRACT FROM AUTHOR]