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Background: It is essential to differentiate bipolar disorder (BD) from unipolar disorder (UD) as the course of illness and treatment guidelines differ between the two disorders. Measurements of activity and mobility could assist in this discrimination.Aims: 1) To investigate differences in smartphone-based location data between BD and UD, and 2) to investigate the sensitivity, specificity, and AUC of combined location data in classifying BD and UD.Methods: Patients with BD and UD completed smartphone-based self-assessments of mood for six months, along with same-time passively collected smartphone data on location reflecting mobility patterns, routine and location entropy (chaos). A total of 65 patients with BD and 75 patients with UD were included.Results: A total of 2594 (patients with BD) and 2088 (patients with UD) observations of smartphone-based location data were available. During a depressive state, compared with patients with UD, patients with BD had statistically significantly lower mobility (e.g., total duration of moves per day (eB 0.74, 95% CI 0.57; 0.97, p = 0.027)). In classification models during a depressive state, patients with BD versus patients with UD, there was a sensitivity of 0.70 (SD 0.07), a specificity of 0.77 (SD 0.07), and an AUC of 0.79 (SD 0.03).Limitations: The relative low symptom severity in the present study may have contributed to the magnitude of the AUC.Conclusion: Mobility patterns derived from mobile location data is a promising digital diagnostic marker in discriminating between patients with BD and UD. [ABSTRACT FROM AUTHOR]

Background: Smartphone-based monitoring constitutes a cost-effective instrument to assess and predict affective symptom trajectories. Large-scale transdiagnostic studies utilizing this methodology are yet lacking in psychiatric research. Thus, we introduce the Remote Monitoring Application in Psychiatry (ReMAP) and evaluate its feasibility and adherence in a large transdiagnostic sample.Methods: The ReMAP app was distributed among n = 997 healthy control participants and psychiatric patients, including affective, anxiety, and psychotic disorders. Passive sensor data (acceleration, geolocation, walking distance, steps), optional standardized self-reports on mood and sleep, and voice samples were assessed. Feasibility and adherence were evaluated based on frequency of transferred data, and participation duration. Preliminary results are presented while data collection is ongoing.Results: Retention rates of 90.25% for the minimum study duration of two weeks and 33.09% for one year were achieved (median participation 135 days, IQR=111). Participants transferred an average of 51.83 passive events per day. An average of 34.59 self-report events were transferred per user, with a considerable range across participants (0-552 events). Clinical and non-clinical subgroups did not differ in participation duration or rate of data transfer. The mean rate of days with passive data was higher and less heterogeneous in iOS (91.85%, SD=21.25) as compared to Android users (63.04%, SD=35.09).Limitations: Subjective user experience was not assessed limiting conclusions about app acceptance.Conclusions: ReMAP is a technically feasible tool to assess affective symptoms with high temporal resolution in large-scale transdiagnostic samples with good adherence. Future studies should account for differences between operating systems. [ABSTRACT FROM AUTHOR]

Researchers have measured social functioning in schizophrenia using many different strategies. Recent technological advances have made it possible to passively measure behaviors in real-world social situations—allowing for more objective, ecologically valid assessments. Yet, research testing the convergent validity among real-world and laboratory-based social functioning assessment is sparse. The purpose of this study was to test the convergent validity among four social functioning measures: two interview-based rating scales, a self-reported ecological momentary assessment (EMA), and a passive, ambulatory ecological assessment. Data was collected from 36 people with schizophrenia and 33 control participants. Across the entire sample, relationships between interview-based ratings and real-world measures of social functioning only demonstrated small correlations (r 's = 0.17–0.19), whereas real-world measures exhibited moderate correlations with one another (r = 0.36). Within groups, real-world measures showed moderate, significant relationships in the control group (r = 0.44) but not in the schizophrenia group (r = 0.27). For those with schizophrenia, the interview-based measures of social functioning were moderately associated with ambulatory ecological assessment (r 's = 0.38 and 0.47), but only small associations were observed with self-reported EMA (r 's = 0.15 and 0.17). Results suggest social functioning assessments are not highly convergent and likely target different aspects of social functioning. Laboratory-based measures offer global impressions of social functioning whereas real-world measures represent a more nuanced approach. Moreover, ambulatory ecological assessment may most accurately gauge frequency of daily social interactions for those with schizophrenia as it circumvents common pitfalls of self-report and offers a less-biased, in-depth evaluation of social behavior. [ABSTRACT FROM AUTHOR]

• Reconstruct vehicle trajectories on freeways mixed with CAVs and HVs. • Determine whether there are HVs in the undetected range of CAVs. • Estimate the initial insertion position and speed of the inserted HVs. • Estimate the trajectories of the inserted HVs using cellular automation. • Examine the method under different traffic densities and PRs of CAVs. The development of technologies related to connected and automated vehicles (CAVs) allows for a new approach to collect vehicle trajectory. However, trajectory data collected in this way represent only sampled traffic flow owing to low penetration rates (PRs) of CAVs and privacy concerns, and fail to provide a comprehensive picture of traffic flow. This study proposes a method to reconstruct vehicle trajectories in fully sampled traffic flow on freeways that consists of human-driven vehicles (HVs) and CAVs by using the mobile sensing data acquired from CAVs. The expected behavior of vehicles within the detection range of CAVs is determined based on the driving state classified by the Wiedemann model, i.e., free driving, emergency, closing, and following. If the actual behavior is different from the expected, it is deemed to be influenced by the undetected HVs. Then, new HVs are inserted based on the estimated local traffic density and speed of the freeway. The trajectories of the inserted HVs are further reconstructed by using the established update rules of cellular automation, i.e., uniform motion, acceleration, deceleration, randomization and position update. Last, the proposed method was examined by simulation experiments under different traffic densities and PRs of CAVs. The results show that the trajectories of fully sampled mixed traffic flow can be reconstructed reasonably well, not only under traffic conditions without explicit congestion but in congested environments. [ABSTRACT FROM AUTHOR]

Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to locate the same devices across different levels and locations inside a building. Typically, localization in dense environments is a computationally expensive process when done with just video data; hence hard to scale. DenseTrack combines Wi-Fi and video data to improve the accuracy of tracking people that are represented by video objects from non-overlapping video feeds. DenseTrack is a scalable and device-agnostic solution as it does not require any app installation on user smartphones or modifications to the Wi-Fi system. At the core of DenseTrack , is our algorithm — inCremental Association of Independent Variables under Uncertainty (CAIVU). CAIVU is inspired by the multi-armed bandits model and is designed to handle various complex features of practical real-world environments. CAIVU matches the devices reported by an off-the-shelf Wi-Fi system using connectivity information to specific video blobs obtained through a computationally efficient analysis of video data. By exploiting data from heterogeneous sources, DenseTrack offers an effective real-time solution for individual tracking in heavily populated indoor environments. We emphasize that no other previous system targeted nor was validated in such dense indoor environments. We tested DenseTrack extensively using both simulated data, as well as two real-world validations using data from an extremely dense convention center and a moderately dense university environment. Our simulation results show that DenseTrack achieves an average video-to-Wi-Fi matching accuracy of up to 90% in dense environments with a matching latency of 60 s on the simulator. When tested in a real-world extremely dense environment with over 500,000 people moving between different non-overlapping camera feeds, DenseTrack achieved an average match accuracy of 83% to within a 2-people distance with an average latency of 48 s. [ABSTRACT FROM AUTHOR]

Devices equipped with sensors to track mobility, such as through Global Position Systems (GPS) and accelerometery, are increasingly being used for research. Following Canadian, US, and International guidelines there is a need to give special consideration when conducting research with vulnerable populations. This paper examines specific ethical concerns for conducting research with mobile sensing devices for use by vulnerable populations, considering aspects of both research design and research process. Drawing on insights from feminist design and aligned fields, such as participatory design and action research, we contend that any research design and process for working with vulnerable populations must be developed in collaboration with the particular groups and communities who are part of the research. As part of this process of collaborative research, we discuss risks in terms of the lack of control over data associated with choosing commercial devices, as well as practicality and obtrusiveness of devices for the wearer. We also discuss the significance of informed consent and refusal and issues relating to security and safety during research. As part of the collaborative research design and process, we argue that participants should be given as much control over their data as possible. Based on this discussion, we provide recommendations for researchers to consider, which are broadly relevant for research using mobile sensing devices but particularly significant in relation to vulnerable populations. • Ethical considerations of mobile sensing research neglect vulnerable populations. • Feminist design principles help frame ethical considerations of mobile sensing work. • Recommendations for ethical mobile sensing research with vulnerable groups considered. [ABSTRACT FROM AUTHOR]

Stress levels among people rise through the years, and passive sensing data from mobile phones or other ubiquitous devices have started to found its place in applications of mental health observation. With the ultimate goal of creating an automatic human mental health assistant that helps people to have a better mental condition, a step is taken by creating a stress recognition model. In previous works, the researchers have found correlations between sensor data and mental health conditions. They attempted to predict the stress level by different data sources. Due to there is no direct link between any sensor data with mental health, Machine Learning algorithms are employed to uncover relations with multiple sensors and mental well-being. The utilized machine learning algorithms work with non-sequence data hence the researchers need to extract features that represent historical sensor data as one value features. However, extracted features cannot completely represent time-varying sequence data. Within the scope of this study, we demonstrate that LSTM, CNN and CNN-LSTM algorithms, which accept sequence data as input can also work in passive mobile phone sensor data to predict human mental stress. The performance of the model on StudentLife dataset that includes passive mobile sensing data and stress feedbacks of college students has 62.83% accuracy on 460 test instances by training with 800 instances with LSTM model. The diversity of the data is limited and the size is tiny for the data-hungry LSTM model. Consequently, the model could not generalize on adapted features with the small sample size. Although we did not adapt complex features, the results encourage us to improve data size and continue to research on this topic. [ABSTRACT FROM AUTHOR]

Abstract Air pollution impact assessment is a major objective for various community councils in large cities, which have lately redirected their attention towards using more low-cost sensing units supported by citizen involvement. However, there is a lack of research studies investigating real-time mobile air-quality measurement through smart sensing units and even more of any data-driven modelling techniques that could be deployed to predict air quality accurately from the generated data-sets. This paper addresses these challenges by: a) proposing a comparative and detailed investigation of various air quality monitoring devices (both fixed and mobile), tested through field measurements and citizen sensing in an eco-neighbourhood from Lorraine, France, and by b) proposing a machine learning approach to evaluate the accuracy and potential of such mobile generated data for air quality prediction. The air quality evaluation consists of three experimenting protocols: a) first, we installed fixed passive tubes for monitoring the nitrogen dioxide concentrations placed in strategic locations highly affected by traffic circulation in an eco-neighbourhood, b) second, we monitored the nitrogen dioxide registered by citizens using smart and mobile pollution units carried at breathing level; results revealed that mobile-captured concentrations were 3–5 times higher than the ones registered by passive-static monitoring tubes and c) third, we compared different mobile pollution stations working simultaneously, which revealed noticeable differences in terms of result variability and sensitivity. Finally, we applied a machine learning modelling by using decision trees and neural networks on the mobile-generated data and show that humidity and noise are the most important factors influencing the prediction of nitrogen dioxide concentrations of mobile stations. Highlights • We proposed an air quality evaluation framework using fixed and mobile sensing units. • It also integrates machine learning methods to predict the air quality from mobile data. • Three experimenting protocols for air pollution monitoring have been implemented. • NO2 pollution at human breathing levels was 3-5 times higher than those of static units. • Decision trees and neural networks can accurately predict mobile air quality. • Humidity and noise are the most important factors affecting the NO2 prediction. [ABSTRACT FROM AUTHOR]

Highlights • Provide a systematic review of CAV based UTC studies in the last few decades. • Highlight the change of design philosophy for UTC. • Summarize the benefits and drawbacks of current CAV-based UTC methods. • Predict the future study directions. Abstract Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field. [ABSTRACT FROM AUTHOR]

Highlights • A detector-free method for fundamental diagram estimation. • Using probe vehicle data and value of the jam density. • Guaranteed exact identification under idealized conditions. • Estimation algorithm for actual traffic conditions. • Validation using real-world probe vehicle data. Abstract The fundamental diagram (FD), also known as the flow–density relation, is one of the most fundamental concepts in the traffic flow theory. Conventionally, FDs are estimated by using data collected by detectors. However, detectors' installation sites are generally limited due to their high cost, making practical implementation of traffic flow theoretical works difficult. On the other hand, probe vehicles can collect spatially continuous data from wide-ranging area, and thus they can be useful sensors for large-scale traffic management. In this study, a novel framework of FD estimation by using probe vehicle data is developed. It determines FD parameters based on trajectories of randomly sampled vehicles and a given jam density that is easily inferred by other data sources. A computational algorithm for estimating a triangular FD based on actual, potentially noisy traffic data obtained by multiple probe vehicles is developed. The algorithm was empirically validated by using real-world probe vehicle data on a highway. The results suggest that the algorithm accurately and robustly estimates the FD parameters. [ABSTRACT FROM AUTHOR]

Individuals are often exposed to multiple environmental factors simultaneously. Understanding their joint effects is essential for developing effective public health policies. However, there has been a lack of research examining individuals' concurrent exposures to multiple environmental factors during people's daily mobility. To address this gap, this study investigated the relationships between and geographic patterns of individual exposures to air pollution (PM 2.5), noise and greenspace using individual-level real-time GPS and mobile sensing data collected in outdoor environments. The findings indicate that the relationships between individual exposures to air pollution, noise and greenspace vary across different value ranges of exposures. The study also reveals that people's concurrent exposures to multiple environmental factors exhibit spatial nonstationary and strong clustering patterns. These results highlight the importance of considering spatial nonstationary and spatial heterogeneity of environmental exposures in understanding the relationships between multiple exposures in environmental health research. • We investigate the geographic patterns of individual exposures to air pollution, noise and greenspace. • We use real-time GPS data, mobile sensing data, and a mobility-based framework. • The relationships between individuals' concurrent exposures vary across different value ranges of exposures. • People's concurrent exposures exhibit spatial nonstationary and strong clustering patterns. • It is necessary to consider spatial nonstationary and spatial heterogeneity in concurrent environmental exposures. [ABSTRACT FROM AUTHOR]

Annoyance is a major health burden induced by environmental noise. However, our understanding of the health impacts of noise is seriously undermined by the fixed contextual unit and limited sound characteristics (e.g., the sound level only) used in noise exposure assessments as well as the stationarity assumption made for exposure-response relationships. To address these limitations, we analyze the complex and dynamic relationships between personal momentary noise annoyance and real-time noise exposure in various activity microenvironments and times of day, taking into account individual mobility, multiple sound characteristics and nonstationary relationships. Using real-time mobile sensing, we collected individual data of momentary noise annoyance, real-time noise exposure as well as daily activities and travels in Hong Kong. A new sound characteristic, namely sound increment, is defined to capture the sudden increase in sound level over time and is used along with the sound level to achieve a multi-faceted assessment of personal real-time noise exposure at the moment of annoyance responses. Further, the complex noise exposure-annoyance relationships are learned using logistic regression and random forest models while controlling the effects of daily activity microenvironments, individual sociodemographic attributes and temporal contexts. The results indicate that the effects of the real-time sound level and sound increment on personal momentary noise annoyance are nonlinear, despite the overall significant and positive impacts, and different sound characteristics can produce a joint effect on annoyance. We also find that the daily activity microenvironments and individual sociodemographic attributes can affect noise annoyance and its relationship with different sound characteristics to varying degrees. Due to the temporal changes in daily activities and travels, the noise exposure-annoyance relationships can also vary over different times of the day. These findings can inform both local governments and residents with scientific evidence to promote the creation of acoustically comfortable living environments. • The effects of the sound level and increment on momentary annoyance are nonlinear. • Different sound characteristics can produce a joint effect on momentary annoyance. • Activity microenvironments can affect momentary annoyance to varying degrees. • Momentary noise exposure-annoyance relationships can vary throughout the day. [ABSTRACT FROM AUTHOR]

• Excessive time spent passively on smartphones is associated with negative measures of youth mental health. • Active use of smartphones appears to be associated with fewer internalizing symptoms. • Mobile sensing apps provide an objective and low-burden way to quantify behaviour. Smartphone use provides a significant amount of screen-time for youth, and there have been growing concerns regarding its impact on their mental health. While time spent in a passive manner on the device is frequently considered deleterious, more active engagement with the phone might be protective for mental health. Recent developments in mobile sensing technology provide a unique opportunity to examine behaviour in a naturalistic manner. The present study sought to investigate, in a sample of 451 individuals (mean age 20.97 years old, 83% female), whether the amount of time spent on the device, an indicator of passive smartphone use, would be associated with worse mental health in youth and whether an active form of smartphone use, namely frequent checking of the device, would be associated with better outcomes. The findings highlight that overall time spent on the smartphone was associated with more pronounced internalizing and externalizing symptoms in youth, while the number of unlocks was associated with fewer internalizing symptoms. For externalizing symptoms, there was also a significant interaction between the two types of smartphone use observed. Using objective measures, our results suggest interventions targeting passive smartphone use may contribute to improving the mental health of youth. [ABSTRACT FROM AUTHOR]

Abstract Human attention has bottlenecked today's ubiquitous computing environment where users are consuming increasing amounts of information from numerous applications and services. Since the system-to-user provision of information is becoming more proactive, mainly via push notifications that often cause interruption at the users' side, attention management is becoming very important. Despite the many existing studies concerned with detecting opportune moments to present such push information to the users (in a way that preserves the user's attention and lowers their cognitive load and frustration), there is little evaluation of such systems in the real-world production environments. Overlooked areas of study also include the examination of real users and notification contents. In this paper, we present various results from the first extensive evaluation on user's interruptibility and engagement in the real-world environment with a market-leading smartphone application that boasts a large number of users, including real notification content on the application. Following our previous mobile-sensing and machine learning-based interruptibility estimation approach, which was an effective study in its own right (Okoshi et al., 2015 [18,19]), we embedded a logic with the same approach in the "Yahoo! JAPAN" Android app (one of the most popular applications on the national market). The results from our large-scale in-the-wild user study (that included more than 680,000 users and spanned three weeks) indicate that, in most cases, delaying the notification delivery until an interruptible moment is detected is beneficial to users. The practice results in significant reduction of user response time (49.7%) when compared to delivering the notifications immediately. We observed a higher number of notifications opened in our system and constant improvement in user engagement levels throughout the entire study period. We also observed differences in click rates among different days and time among users with different attributes (e.g., age, gender, and occupation). Additional evaluation of our revised system, which can train and distribute different models for weekdays and weekends, improved the click rates during weekends. This negated the performance degradation previously observed during weekends. [ABSTRACT FROM AUTHOR]

Smart user devices are becoming increasingly ubiquitous and useful for detecting the user’s context and his/her current activity. This work analyzes and proposes several techniques to improve the robustness of a Human Activity Recognition (HAR) system that uses accelerometer signals from different smartwatches and smartphones. This analysis reveals some of the challenges associated with both device heterogeneity and the different use of smartwatches compared to smartphones. When using smartwatches to recognize whole body activities, the arm movements introduce additional variability giving rise to a significant degradation in HAR. In this analysis, we describe and evaluate several techniques which successfully address these challenges when using smartwatches and when training and testing with different devices and/or users. [ABSTRACT FROM AUTHOR]

Smartphones have become an integral part in life of users, mainly because over the course of recent years, they have become extremely mainstream, cheap, flexible, and they pack high-end hardware that offers high computational capabilities. Many, if not all of today’s smartphones are equipped with sophisticated sensors which enable smart mobile sensing. The programmable nature of these sensors in the smartphones enable a wide array of possibilities to achieve user-centric or environmental sensing. Even though there have been different approaches proposed to develop a smartphone app, platform, design frameworks, APIs, and even application-specific architectures, there is a lack of generalised referenceable architecture in the literature. In this paper, we propose a generic reference architecture, which can be derived to create more concrete mobile sensing or mobile app architectures. Furthermore, we realise the proposed reference architecture in a healthcare use case, specifically in the context of applying smart mobile sensing to support tinnitus research. [ABSTRACT FROM AUTHOR]

Indoor environmental quality (IEQ) is a critical aspect of the built environment to ensure occupant health, comfort, well-being and productivity. Existing IEQ monitoring approaches rely on sensor networks deployed at selected locations to collect environmental measurements, and are limited in scale and adaptability due to infrastructure cost and maintenance requirement. To enable high-granularity IEQ monitoring with agile adaption to the dynamic indoor environment, we propose an “automated mobile sensing” system that dispatches a sensor-rich navigation-capable robot to actively survey the indoor space. Data collected in this fashion is sparse in the joint temporal and spatial domain, and cannot be used directly for IEQ evaluation. To deal with this special characteristics, we developed a spatio-temporal interpolation algorithm to capture the global trend and local variation in order to use the data efficiently to reconstruct the IEQ dynamics. We compared the performance of the automated mobile sensing with a dense sensor network in a laboratory where we measured the air-change effectiveness (ASHRAE standard 129) for four different conditions. Results indicate that automated mobile sensing is able to accurately estimate the parameters with a minimal sensor cost and calibration effort. Potential applications of this system include indoor thermal comfort, lighting, indoor air quality and acoustic monitoring, pollutant source identification, and building commissioning. We shared publicly the source codes for robot control, sensor setup, and interpolation algorithm to encourage comparison study and further development. [ABSTRACT FROM AUTHOR]

The spontaneous recognition of emotional states and personality traits of individuals has been puzzling researchers for years whereas pertinent studies demonstrating the progress in the field, despite their diversity, are still encouraging. This work surveys the most well-known research studies and the state-of-the-art on affect recognition domain based on smartphone acquired data, namely smartphone embedded sensors and smartphone usage. Inevitably, supplementary modalities employed in many eminent studies are also reported here for the sake of completeness. Nevertheless, the intention of the survey is threefold; firstly to document all the to-date relevant literature on affect recognition through smartphone modalities, secondly to argue for the full potential of smartphone use in the inference of affect, and thirdly to demonstrate the current research trends towards mobile affective computing. [ABSTRACT FROM AUTHOR]

Being aware of the activities of co-workers is a vital mechanism for efficient work in highly distributed work settings. Thus, automatically recognizing which task phases mobile workers are in and estimating their task progress is crucial for many aiding applications (e.g., task scheduling) utilizing coordination mechanisms (e.g., visualization of co-workers’ task progresses and notifications based on context awareness). This paper presents methods to sense and detect highly mobile workers’ task phases and progresses in large building complexes. These methods make use of data from sensing systems common in large-scale indoor work environments, such as WiFi infrastructures providing coarse grained indoor positioning, inertial sensors in the workers’ mobile phones, and from task management systems logging scheduled tasks. The methods presented have low requirements on sensing accuracy and thus come with low deployment and maintenance effort in real-world settings. We evaluated the proposed methods in a large hospital complex, where the highly mobile workers were recruited among the non-clinical workforce. The evaluation is based on manually labeled real-world data collected over 4 days of regular work life. The collected data yields 83 tasks in total involving 8 different employees of a major university hospital with a building area of 160,000 m 2 . The results show that the proposed methods can with reasonable accuracy i) distinguish between the four most common task phases present in the workers’ routines, achieving F 1 -Scores of 89.2%, and ii) estimate the task progress, yielding a mean error of 126.82 seconds for estimating the time until task completion and of 9.49 pp for estimating task progress. [ABSTRACT FROM AUTHOR]

A video playing on a television screen emits a characteristic flickering, which serves as an identification feature for the video. This paper presents a method for video recognition by sampling the ambient light sensor of a smartphone or wearable computer. The evaluation shows that given a set of known videos, a recognition rate of up to 100% is possible by sampling a sequence of 15 to 120 s length. Our method works even if the device has no direct line of sight to the television screen, since ambient light reflected from walls is sufficient. A major factor of influence on the recognition is the number of video cuts that change the light emitted by the screen. [ABSTRACT FROM AUTHOR]

Many pervasive computing applications depend upon maps for support of location based services. Individuals can determine their location outdoors on these maps via GPS. Indoor pervasive applications may also need to know the layout of buildings, however indoor maps are less prevalent. This paper presents iFrame, a dynamic approach that leverages existing mobile sensing capabilities for constructing indoor floor plans. We explore how iFrame users may collaborate and contribute to constructing 2-dimensional indoor maps by merely carrying smartphones. A deployment study shows iFrame is an unattended approach that provides a skeleton map of a real building effectively and automatically. [ABSTRACT FROM AUTHOR]

In this paper, we reduce the energy overheads of continuous mobile sensing, specifically for the case of context-aware applications that are interested in collective context or events , i.e., events expressed as a set of complex predicates over sensor data from multiple smartphones. We propose a cloud-based query management and optimization framework, called CloQue , that can support thousands of such concurrent queries, executing over a large number of individual smartphones. Our central insight is that the context of different individuals & groups often have significant correlation, and that this correlation can be learned through standard association rule mining on historical data. CloQue’s exploits such correlation to reduce energy overheads via two key innovations: (i) dynamically reordering the order of predicate processing to preferentially select predicates with not just lower sensing cost and higher selectivity, but that maximally reduce the uncertainty about other context predicates; and (ii) intelligently propagating the query evaluation results to dynamically update the confidence values of other correlated context predicates. We present techniques for probabilistic processing of context queries (to save significant energy at the cost of a query fidelity loss) and for query partitioning (to scale CloQue to a large number of users while meeting latency bounds). An evaluation, using real cellphone traces from two different datasets, shows significant energy savings (between 30% and 50% compared with traditional short-circuit systems) with little loss in accuracy (5% at most). In addition, we utilize parallel evaluation to reduce overall latency. The experiments show our approaches save up to 70% latency. [ABSTRACT FROM AUTHOR]

This paper presents and evaluates a prototype smartphone/server software service entitled the Occupant Mobile Gateway (OMG), developed to collect and analyze real-time occupant subjective feedback and objective thermal data from embedded sensors. The objective of the OMG is to enable mobile sensing as a scalable approach to realize the comfort and energy savings potential of data-driven thermal management using personalized comfort models. In contrast to the universal application of static comfort criteria, data-driven models have the potential to enable flexible and continuous interpretation of thermal comfort, allowing for more personalized control of the thermal environment and minimization of the energy wasted by overheating and overcooling. Novel machine-learning algorithms are applied to synthesize subjective and physical thermal measurements into personalized comfort models which can be interpreted to determine optimal temperature setpoints based on energy and comfort goals. The feasibility of machine learning algorithms to generate useful personalized occupant comfort profiles is examined using field data from four test sites (N = 45 occupants total). The findings demonstrate that it is possible to model the thermal preferences of occupants at both the individual and zone level with data collected over a relatively small time period (2-weeks). Application of occupant-driven comfort models in annual energy simulations shows that thermal management informed by occupant feedback has the potential for significant energy savings. [ABSTRACT FROM AUTHOR]

The emergence of smart phones has had a positive impact on society as the range of features and automation has allowed people to become more productive while they are on the move. On the contrary, the use of these devices has also become a distraction and hindrance, especially for pedestrians who use their phones whilst walking on the streets. This is reinforced by the fact that pedestrian injuries due to the use of mobile phones has now exceeded mobile phone related driver injuries. This paper describes an approach that measures the different levels of distraction encountered by pedestrians whilst they are walking. To distinguish between the distractions within the brain the proposed work analyses data collected from mobile sensors (accelerometers for movement, mobile EEG for electroencephalogram signals from the brain). The long-term motivation of the proposed work is to provide pedestrians with notifications as they approach potential hazards while they walk on the street conducting multiple tasks such as using a smart phone. [ABSTRACT FROM AUTHOR]

Smartphones are ubiquitous devices that enable users to perform many of their routine tasks anytime and anywhere. With the advancement in information technology, smartphones are now equipped with sensing and networking capabilities that provide context-awareness for a wide range of applications. Due to ease of use and access, many users are using smartphones to store their private data, such as personal identifiers and bank account details. This type of sensitive data can be vulnerable if the device gets lost or stolen. The existing methods for securing mobile devices, including passwords, PINs and pattern locks are susceptible to many bouts such as smudge attacks. This paper proposes a novel framework to protect sensitive data on smartphones by identifying smartphone users based on their behavioral traits using smartphone embedded sensors. A series of experiments have been conducted for validating the proposed framework, which demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Researchers have recently devoted considerable attention to acquiring location awareness of assets. They have explored various technologies, such as video cameras, radio signal strength indicator-based sensors, and motion sensors, in the development of tracking systems. However, each system presents unique drawbacks especially when applied in complex indoor construction environments; this paper classifies them into two categories: absolute tracking and relative tracking. By understanding the nature of problems in each tracking category, this research develops a novel tracking methodology that uses knowledge of the strengths and weaknesses of various components used in the proposed tracking system. This paper presents the development of a hybrid-tracking system that integrates Bluetooth Low Energy (BLE) technology, motion sensors, and Building Information Model (BIM). The hypothesis tested through this integration was whether such knowledge-based integration could provide a method that can correct errors found in each of the used sensing technologies and thereby improve the reliability of the tracking system. Field experimental trials were conducted in a full-scale indoor construction site to assess the performance of individual components and the integrated system. The results indicated that the addition of map knowledge from a BIM model showed the capability of correcting improbable movements. Furthermore, the knowledge-based decision making process demonstrated its capability to make positive interaction by reducing the positioning errors by 42% on average. In sum, the proposed hybrid-tracking system presented a novel method to compensate for the weakness of each system component and thus achieve a more accurate and precise tracking in dynamic and complex indoor construction sites. [ABSTRACT FROM AUTHOR]

The recent arrival of smartphone-sensing methods has made it possible to objectively track consequential everyday health-related behaviors rather than rely on self-reports. To evaluate the viability of using sensing methods to monitor such behaviors in detail, the present research used a smartphone-sensing application to describe the patterns of stability and change that characterize a cohort of students' activity and sociability behaviors over the course of a 10-week academic term. Data were collected from 48 students using a smartphone-sensing application, StudentLife , which was designed to track daily durations of activity (via the accelerometer sensor) and sociability (via the microphone sensor). Results showed stability estimates were moderate to high for activity ( r mean = 0.66) and sociability ( r mean = 0.72) across the 10 weeks. Students started the term with generally healthy levels of activity ( M = 1.87 h) and sociability ( M = 4.99 h), which then dropped (activity by 0.42 h, sociability by 0.90 h) over the first half of the term (i.e., before midterm exams). Over the second half of the term, activity levels did not change but sociability increased (by 0.88 h). Students’ ethnicity and academic class predicted variation in the activity and sociability trajectories. Discussion focuses on the implications of our results for designing mHealth interventions to address consequential student outcomes (e.g., mental health, physical health). [ABSTRACT FROM AUTHOR]

We demonstrate a surface plasmon resonance imaging platform integrated with a smartphone to be used in the field with high-throughput biodetection. Inexpensive and disposable SPR substrates are produced by metal coating of commercial Blu-ray discs. A compact imaging apparatus is fabricated using a 3D printer which allows taking SPR measurements from more than 20.000 individual pixels. Real-time bulk refractive index change measurements yield noise equivalent refractive index changes as low as 4.12 × 10 −5 RIU which is comparable with the detection performance of commercial instruments. As a demonstration of a biological assay, we have shown capture of mouse IgG antibodies by immobilized layer of rabbit anti-mouse (RAM) IgG antibody with nanomolar level limit of detection. Our approach in miniaturization of SPR biosensing in a cost-effective manner could enable realization of portable SPR measurement systems and kits for point-of-care applications. [ABSTRACT FROM AUTHOR]

Recent accidents ( News, 2006, 2010, 2013, 2015 ) show that crowded events can quickly turn into tragedies. The goal of crowd management is to avoid such accidents through careful planning and implementation. Crowd management practices are collaborative efforts between the different actors of the crowd management team and the crowd that depend on effective handling, sharing, and communication of information. Safety and comfort of a crowd depend on the success of such efforts. We have studied current practices and the role of technology through interviews to crowd managers. Our findings show that event planning and monitoring can be complex and sophisticated, but are operated with little support from technology. Crowd managers intend to increase their use of technology, but they have been so far dissatisfied by existing solutions. We provide recommendations for a bigger role of technology in crowd management. [ABSTRACT FROM AUTHOR]

Data-driven approaches to addressing climate change are increasingly becoming a necessary solution to deal with the scope and scale of interventions required to reach net zero. In the UK, housing contributes to over 30% of the national energy consumption, and a massive rollout of retrofit is needed to meet government targets for net zero by 2050. This paper introduces a modular framework for quantifying building features using drive-by image capture and utilising them to estimate energy consumption. The framework is demonstrated on a case study of houses in a UK neighbourhood, showing that it can perform comparatively with gold standard datasets. The paper reflects on the modularity of the proposed framework, potential extensions and applications, and highlights the need for robust data collection in the pursuit of efficient, large-scale interventions. • Proposes a modular framework to estimate energy consumption from mobile sensing data. • Uses computer vision techniques to detect and measure features of buildings. • Combines features with other data sources to produce energy models. • Demonstrates efficacy of framework with results comparable to EPC estimates. • Reflects on the limitations and potential extensions of the proposed framework. [ABSTRACT FROM AUTHOR]

To integrate mobile sensing and digital phenotyping into youth mental health care, the norms of comfort among adolescent patients and their parents with mobile sensing and digital phenotyping must be understood. The current study sought to elucidate the norms of comfort among adolescent patients and the parents of adolescent patients. It was hypothesized that the type of data sensor, level of detail, and participant group would affect comfort ratings and that there would be an interaction between group membership and the level of detail. Finally, comfort with the collection of speech data was explored. One hundred nine adolescent patients and 85 parents completed an online questionnaire about comfort with various mobile data sensors. Overall, the results support our hypotheses and match the results of previous research in adult patients, with adolescent patients' comfort decreasing with more detailed and personal digital data collected. These results indicate that comfort with mobile sensing and digital phenotyping in the context of mental health care depends on the individual's role, the type of data sensor, and the level of detail of the data sensor. Understanding these norms of comfort can help to improve the integration of information technology into the mental health care of youth. • Overall, patients and parents were comfortable with the use of mobile sensing and digital phenotyping. • Youth patient and parent comfort is influenced by the type of data being collected via mobile sensing. • Comfort is also influenced by the level of detail of the data being collected. • Parents' and patients' comfort levels differ at the highest level of detail. • Speech data shows similar patterns in ratings of comfort to other data types. [ABSTRACT FROM AUTHOR]

The ubiquitous deployment of personal mobile devices today has boosted numerous mobile sensing applications where sensing data should be timely collected and exchanged among participating sensors. An important bootstrapping primitive in such applications is neighbor discovery. Designing distributed neighbor discovery protocols in mobile sensing applications is particularly challenging because of the duty cycling operation mode where mobile devices, usually battery-powered, switch between active and dormant modes periodically to conserve energy. In this paper, we give a comprehensive survey on the latest advance and development in this field by covering probabilistic, deterministic and collaborative neighbor discovery approaches developed in the literature. The focus of our survey on the developed neighbor discovery protocols is their design ideas and methodologies that may inspire and guide the development of new solutions in the future research. We also highlight a number of important and relevant research challenges that have not been addressed in the existing literature and that deserve further attention and investigation. [ABSTRACT FROM AUTHOR]

The rapidly enhancing sensing capabilities of smartphones are enabling the development of a wide range of innovative mobile sensing applications that are impacting on everyday life of mobile users. However, supporting long-term sensing applications is challenging because of their key requirements for continuous access to embedded sensors for gathering raw data, which can deplete the device’s battery in a few hours. This problem is expected to remain in the near future because the improvements on the capacity of batteries are coming at a slower pace than those advances in computing and sensing capabilities. The research community has highlighted the need for power-aware and context-aware sensing techniques deployed at different levels of mobile platforms for making a more efficient use of energy resources. Previous studies have analyzed the optimization of power consumption in mobile devices over different critical axes, like data transmission, computing, and hardware design. However, a comprehensive study focused in the challenges of power-aware smartphone-based sensing and strategies for addressing them has not been produced yet. This survey aims to fill this void with a particular focus on mobility sensing systems (e.g., human activity recognition, location-based services), presenting a comprehensive review of relevant strategies aimed at solving this issue. Also, this survey defines a taxonomy for such solutions, highlighting their strengths and limitations. Finally, most relevant open challenges and trends are discussed for providing insights for future research in the field. [ABSTRACT FROM AUTHOR]

As the amount of information to users increases with the trends of an increasing numbers of devices, applications, and web services, the new bottleneck in computing is human attention. To minimize users attentional overload, we propose a novel middleware “Attelia” that detects breakpoints of user’s mobile interactions to deliver notifications adaptively. Attelia detects such timings in real-time, using only users phones, without any external sensors, and without any modifications to applications. Our extensive evaluation proved Attelias effectiveness. In-the-wild user study with 30 participants for 16 days showed that, specifically for the users with greater sensitivity for interruptive notification timings, notification scheduling in Attelia’s breakpoint timing reduced users frustration by 28% in users’ real smart phone environments. [ABSTRACT FROM AUTHOR]

The question “Where has my battery gone?” remains a common source of frustration for many smartphone users. With the increased complexity of smartphone applications, and the increasing number of system settings affecting them, understanding and optimizing battery use has become a difficult chore. The present paper develops a novel approach for constructing energy models from crowdsourced measurements. In contrast to previous approaches, which have focused on the effect of a specific sensor, system setting or application, our approach can simultaneously capture relationships between multiple factors, and provide a unified view of the energy state of the mobile device. We demonstrate the validity of using crowdsourced measurements for constructing battery models through a combination of large-scale analysis of a dataset containing battery discharge and system state measurements, and hardware power measurements. The results indicate that the models captured by our approach are both in line with previous studies on battery consumption and empirical measurements, providing a cost-effective way to construct energy models during normal operations of the device. The analysis also provides several new insights about battery consumption. For example, our analysis reveals the combined effect of high CPU activity and automatic screen brightness to be higher (resulting in 9 min shorter battery lifetime on average) than the effect of medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can shorten battery life by over 13%; and a smartphone sitting in direct sunlight can witness over 50% shorter battery life than one indoors in cool conditions. Based on the crowdsourced energy models, we develop Constella, a novel recommender system for system settings. Constella provides actionable and human-readable recommendations on how to adjust system settings in order to reduce overall battery drain. We validate the effectiveness of Constella through a hardware power measurement experiment carried out using three application case studies. The results of the evaluation demonstrate that Constella is capable of generating recommendations that can provide up to 61% improvements in battery life. [ABSTRACT FROM AUTHOR]

The frequent hazy weather has brought enormous threaten to human health, especially in developing countries. Modeling a fine-grained distribution of PM 2.5 (Particulate matter with diameters less than 2.5 μ m ) concentrations is of great importance for industrial and environmental applications. The contradiction between the need of fine-grained distribution and the approach of coarse-grained collection has to be addressed immediately. In this paper, we focus on solving the problem of inferring PM 2.5 concentration of unobserved areas based on data samples collected by mobile sensors. We propose a Probabilistic Concentration Estimation Method ( PCEM ) for a regional fine-grained PM 2.5 distribution considering the nature of particle motion. It simulates particles transport in an open air-flow field referring the concept of random walk. In the meantime, quadratic programming and heuristic function are also constructed to optimize the algorithm accuracy and robustness. We employ several mobile sensors to collect original data randomly in a region of HangZhou City for certain period of time and utilize the proposed PCEM to generate the real time fine-grained PM 2.5 concentration distribution. The results can demonstrate up to 100 times higher resolution level of the PM 2.5 concentration distribution than the traditional approaches based on monitoring sites. The degree of correlation between estimated PM 2.5 concentration and real measured data is up to 0.9735. The average calculation error of PCEM can be reduced about 41.0% compared with widely used artificial network (ANN). Furthermore, PCEM can easily adapt to other PM distribution inference with different built-in sensors, which could help with a deeper understanding of an informed air quality monitoring system in the future. [ABSTRACT FROM AUTHOR]

Indoor environmental quality (IEQ) monitoring is an important basis of smart buildings to ensure human comfort and control energy systems. IEQ monitoring conventionally relies on a limited number of stationary sensors deployed at selected locations, which has little capacity to capture fine spatial characteristics due to the cost of infrastructure and maintenance. This paper describes a robot-based mobile sensing system for high-resolution temperature monitoring by selecting air temperature as the targeted IEQ parameter. Experiments were conducted in a classroom to test the mobile sensing performance through comparisons with the dense stationary sensor network. Furthermore, two spatio-temporal processing methods were developed to reconstruct continuous thermal maps from short- and long-term monitoring. The results indicate that the robot velocity of 0.25–0.45 m/s is better than 0.60–0.80 m/s and 0.10–0.20 m/s, and that the proposed methods are suitable for generating accurate, high-resolution thermal maps. • A robot-based mobile sensing system is proposed to achieve high-resolution temperature monitoring. • The mobile sensing performance is tested by comparative experiments with the dense stationary sensor network. • The velocity of 0.25–0.45 m/s is better for mobile sensing as compared to 0.60–0.80 m/s and 0.10–0.20 m/s. • Time-series trend fitting by the mobile sensing data is sufficient to generate thermal map in the short term. • A method combining mobile and stationary sensing is developed for the long-term estimation of continuous thermal map. [ABSTRACT FROM AUTHOR]

[Display omitted] • The progress of portable alternative of conventional electrochemical biosensor is most significance in food safety. • Lab-on-a-leaf, lab-on-a-disc and lab-on-a-glove as green and portable electrochemical methods for monitoring of food safety. • Future perspectives of portable electrochemical sensor towards food contamination were reviewed. Food safety issue is becoming as an international concern for human health around the world. Common substances in food can pose a significant threat to human health containing but not restricted to antibiotics resides, veterinary drugs, pesticides, mycotoxins, heavy metals, food borne-pathogens, illegal additives and allergens. A series of analytical methods were engaged for monitoring of food safety but imparts either practical limitations or analytical defects. Therefore, emerging of alternative methods like portable nanoprobes is of great importance. Over the last few years, sensor platforms have since been shifted towards wearable devices and being portable. Theses probes are movable, accessible, and tiny diagnostic devices which offer in-site analysis of biological contaminants in foods to guarantee their quality and safety. Among different types of portable sensors, low-cost, and flexible sensors as a novel development of portable consumer electronics are a great revolution in living sensors that can be exploited extensively in our daily lives. In this review, recent progress of lab-on-a-glove, leaf, and disc as groundbreaking electrochemical probes for food safety sensing will be discussed, importantly in the aspect of diverse contaminants encountered in food samples. [ABSTRACT FROM AUTHOR]

Mobile sensing enabled by GPS or smart phones has become an increasingly important source of traffic data. For sufficient coverage of the traffic stream, it is important to maintain a reasonable penetration rate of probe vehicles. From the standpoint of capturing higher-order traffic quantities such as acceleration/deceleration, emission and fuel consumption rates, it is desirable to examine the impact on the estimation accuracy of sampling frequency on vehicle position. Of the two issues raised above, the latter is rarely studied in the literature. This paper addresses the impact of both sampling frequency and penetration rate on mobile sensing of highway traffic. To capture inhomogeneous driving conditions and deviation of traffic from the equilibrium state, we employ the second-order phase transition model (PTM). Several data fusion schemes that incorporate vehicle trajectory data into the PTM are proposed. And, a case study of the NGSIM dataset is presented which shows the estimation results of various Eulerian and Lagrangian traffic quantities. The findings show that while first-order traffic quantities can be accurately estimated even with a low sampling frequency, higher-order traffic quantities, such as acceleration, deviation, and emission rate, tend to be misinterpreted due to insufficiently sampled vehicle locations. We also show that a correction factor approach has the potential to reduce the sensing error arising from low sampling frequency and penetration rate, making the estimation of higher-order quantities more robust against insufficient data coverage of the highway traffic. [ABSTRACT FROM AUTHOR]

• Open-channel hydrodynamics estimated via Kalman filter using Lagrangian sensor. • Proposed framework improved estimates obtained from Eulerian sensors alone. • Real-world experiment on irrigation channel demonstrates improvement in estimation. • Lagrangian sensors reduce deployment cost, improve coverage and increase mobility. To ensure fair distribution of surface water among open water channels, it is important to monitor these channels at a high spatial and temporal resolutions. However, channel monitoring is a challenging task if the channels are minimally gauged, i.e. they have limited water level and water flow sensors to describe channel dynamics. Such sensors are generally installed at the control structures (gates) along a channel. If control structures are distant apart from one another then these sensors cannot properly describe the water levels and water velocities in a channel at high spatial resolution. Moreover, it is economically infeasible to install these sensors throughout the channels as they require permanent installation and infrastructure costs. Therefore, inspired by the low-cost mobile sensing and its portability aspect, this paper provides the formulation of a framework that accommodates the GPS measurements of mobile sensing to provide the channel dynamics at a higher resolution. We demonstrate the effectiveness of the proposed framework in comparison to the frameworks with fixed sensors using an experimental dataset obtained from an irrigation channel in Lahore, Pakistan. The proposed framework employs the state-dependent interacting multiple models (SD-IMM) technique and the estimation results show improvements in comparison to the results obtained by the fixed sensors along the channel. [ABSTRACT FROM AUTHOR]

Abstract: The vast availability of mobile phones with built-in movement and location sensors enables the collection of detailed information about human movement even indoors. As mobility is a key element of many processes and activities, an interesting class of information to extract is movement patterns that quantify how humans move, interact and group. In this paper we propose methods for detecting two common pedestrian movement patterns, namely individual following relations and group leadership. The proposed methods for identifying following patterns employ machine learning on features derived using similarity analysis on time-lagged sequences of WiFi measurements containing either raw signal strength values or derived locations. To detect leadership we combine the individual following relations into directed graphs and detect leadership within groups by graph link analysis. Methods for detecting these movement patterns open up new possibilities in–amongst others–computational social science, reality mining, marketing research and location-based gaming. We provide evaluation results that show error rates down to 7%, improving over state-of-the-art methods with up to eleven percentage points for following patterns and up to twenty percentage points for leadership patterns. Furthermore, we provide an analysis of the computational efficiency of the proposed methods and present visualizations for the analysis of detected patterns. Our methods are, contrary to state of the art, also applicable in challenging indoor environments, e.g., multi-story buildings. This implies that even quite small samples allow us to detect information such as how events and campaigns in multi-story shopping malls may trigger following in small groups, or which group members typically take the lead when triggered by e.g. commercials, or how rescue or police forces act during training exercises. [Copyright &y& Elsevier]

Abstract: Biochemical reactions that occur in sewer pipes produce a considerable amount of hydrogen sulfide gas (H2S corrosive and poisonous), methane gas (CH4 explosive and a major climate change contributor), carbon dioxide (CO2 a major climate change contributor), and other volatile substances (collectively known as in-sewer gases). These toxic gases lead to contamination of natural environment, sewer pipe corrosion, costly operational expense, public safety issues, and legal disputes. In order to prevent biochemical reactions and to maintain healthy sewer pipes, frequent inspections are vital. Thus far, various schemes have been designed and developed to identify functional deficiencies in Wastewater Collection System (WCS). Nevertheless, the current inspection techniques are not for mapping the sewer gas concentration. In addition, because of such a harsh and hazardous environment a comprehensive sewer gases inspection has been prohibitively expensive. In this paper we propose SewerSnort, a low-cost, unmanned, fully automated in-sewer gas monitoring system. A sensor float is introduced at the upstream station and drifts down sewer pipeline, while the sensor float collects gas measurements along with location information of sampling points. At the end of the journey, the gas measurements are retrieved from the float and used to generate gas concentration to be used for maintenance or repair. The key innovations of SewerSnort are the fully automated, end-to-end monitoring solution and the low energy self localizing strategy. From the implementation standpoint, the key enablers are the float mechanical design that fits the sewer constraints and the embedded sensor design that matches the float form factor and complies with the tight energy constraints. Experiments based on a dry land emulator demonstrate the feasibility of the SewerSnort concept, in particular, the localization technique and the embedded sensor design. [Copyright &y& Elsevier]

Abstract: We introduce the Friends and Family study, a longitudinal living laboratory in a residential community. In this study, we employ a ubiquitous computing approach, Social Functional Mechanism-design and Relationship Imaging, or Social fMRI, that combines extremely rich data collection with the ability to conduct targeted experimental interventions with study populations. We present our mobile-phone-based social and behavioral sensing system, deployed in the wild for over 15 months. Finally, we present three investigations performed during the study, looking into the connection between individuals’ social behavior and their financial status, network effects in decision making, and a novel intervention aimed at increasing physical activity in the subject population. Results demonstrate the value of social factors for choice, motivation, and adherence, and enable quantifying the contribution of different incentive mechanisms. [Copyright &y& Elsevier]

While the use of smartphones in extreme temperatures does not necessarily occur every day nor in all parts of the world, numerous use cases can be highlighted where the use of smartphones in cold temperatures is mandatory. Modern smartphones are designed to function in a wide range of temperatures, but when exposed to extreme cold temperatures the performance and reliability can significantly suffer. This paper presents a controlled laboratory experiment, using a clinical cold chamber to expose seven smartphone models to both medium cold (0 °C to −20 °C) and extreme cold (−30 °C) environments. The results showcase the smartphones' sensing software's lack of awareness of the cold environment, as well as reliability issues in the form of device crashes across the whole range of tested devices. We present a strategy for implementing monitoring application designs to both appropriately sense the effect of cold environments, as well as predicting device shutdowns in extreme cold. [ABSTRACT FROM AUTHOR]

A framework of goal-oriented sensing has been proposed with the emphasis on the integration of an inference mechanism with an active sensing module, which is equipped with a driving mechanism and a set of sensors. The validity of the dynamic failure identification based on the proposed framework of goal-oriented sensing has been examined under realistic experimental conditions using a small-scale test loop. Though the loops are small in scale, the validity of introducing mobile sensing mechanism has been successfully shown through the experiments emulating realistic failure conditions. [Copyright &y& Elsevier]

We review the challenges and opportunities for biosensor research in North America aimed to accelerate translational research. We call for platform approaches based on: i) tools that can support interoperability between food, environment and agriculture, ii) open-source tools for analytics, iii) algorithms used for data and information arbitrage, and iv) use-inspired sensor design. We summarize select mobile devices and phone-based biosensors that couple analytical systems with biosensors for improving decision support. Over 100 biosensors developed by labs in North America were analyzed, including lab-based and portable devices. The results of this literature review show that nearly one quarter of the manuscripts focused on fundamental platform development or material characterization. Among the biosensors analyzed for food (post-harvest) or environmental applications, most devices were based on optical transduction (whether a lab assay or portable device). Most biosensors for agricultural applications were based on electrochemical transduction and few utilized a mobile platform. Presently, the FEAST of biosensors has produced a wealth of opportunity but faces a famine of actionable information without a platform for analytics. Image 1 • This review focuses on innovations in food, environmental, agricultural, and related core sensing technology meta-domains. • Smartphone-based biosensors pave the way forward for future integration with existing data platforms such as PEAS. • We discuss challenges and opportunities for enhancing data-informed decision support by integrating the IoT design metaphor. [ABSTRACT FROM AUTHOR]

• Developed a mobile sensing system for collecting detailed data on E-Scooter riding. • A first study quantitatively investigating E-Scooter riding risk on different facilities. • Proposed surrogate safety metrics to depict safety challenges during E-Scooter riding. • Assessed obstacles and vibration impact experienced when riding E-Scooters. • Sensed data can be analyzed for assessing the quality of riding facilities. The emergence of shared electric scooter (E-Scooter) systems offers a new micro-mobility mode in many urban areas worldwide. These systems have rapidly attracted numerous trips on various types of facilities such as sidewalks and bike lanes. After their burst of popularity, there are also growing safety concerns about E-Scooter riding. Consequently, a few cities have banned or temporarily suspended E-Scooters as severe crashes occurred. As an emerging micro-mobility mode, its safety performance is significantly understudied as compared to other travel modes such as cars and bicycles. The lack of crash records further prevents it from understanding the underlying mechanisms that drive the occurrences of E-Scooter crashes. The overarching goal of this paper is to probe the safety risk when riding E-Scooters. Specifically, it aims to study the interactions between e-scooter riding and the environment settings through naturalistic riding experiments. Rather than focusing on the analysis of individual riders' heterogeneous behavior (e.g., swinging, hard braking, etc.) and rider characteristics (e.g., age, gender, etc.), the naturalistic riding study examines the riding process in different riding circumstances. A mobile sensing system has been developed to collect data for quantifying the surrogate safety metrics in terms of experienced vibrations, speed changes, and proximity to surrounding objects. The results from naturalistic riding experiments show that E-Scooters can experience notable impacts from different riding facilities. Specifically, compared to bicycle riding, more severe vibration events were associated with E-Scooter riding, regardless of the pavement types. Riding on concrete pavements was found to experience a multiple times higher frequency of vibration events when compared to riding on asphalt pavements of the same length. Riding on both sidewalks and vehicle lanes can both encounter high-frequency close contacts in terms of proximity with other objects. These experimental results suggest that E-Scooters are subject to increased safety challenges due to the increased vibrations, speed variations, and constrained riding environments. [ABSTRACT FROM AUTHOR]

High-quality temperature data at a finer spatio-temporal scale is critical for analyzing the risk of heat exposure and hazards in urban environments. The variability of urban landscapes makes cities a challenging environment for quantifying heat exposure. Most of the existing heat hazard studies have inherent limitations on two fronts; first, the spatio-temporal granularities are too coarse, and second, the inability to track the ambient air temperature (AAT) instead of land surface temperature (LST). Overcoming these limitations requires developing models for mapping the variability in heat exposure in urban environments. We investigated an integrated approach for mapping urban heat hazards by harnessing a diverse set of high-resolution measurements, including both ground-based and satellite-based temperature data. We mounted vehicle-borne mobile sensors on city buses to collect high-frequency temperature data throughout 2018 and 2019. Our research also incorporated key biophysical parameters and Landsat 8 LST data into Random Forest regression modeling to map the hyperlocal variability of heat hazard over areas not covered by the buses. The vehicle-borne temperature sensor data showed large temperature differences within the city, with the largest variations of up to 10 °C and morning-afternoon diurnal changes at a magnitude around 20 °C. Random Forest modeling on noontime (11:30 am – 12:30 pm) data to predict AAT produced accurate results with a mean absolute error of 0.29 °C and successfully showcased the enhanced granularity in urban heat hazard mapping. These maps revealed well-defined hyperlocal variabilities in AAT, which were not evident with other research approaches. Urban core and dense residential areas revealed larger than 5 °C AAT differences from their nearby green spaces. The sensing framework developed in this study can be easily implemented in other urban areas, and findings from this study will be beneficial in understanding the heat vulnerabilities of individual communities. It can be used by the local government to devise targeted hazard mitigation efforts such as increasing green space, developing better heat-safety policies, and exposure warning for workers. • Vehicle-borne temperature data show a range of 5–10 °C within the city • We developed a novel machine learning model to predict ambient air temperature • LST and other biophysical parameters have high predictive power in modeling • Model output is consistent with the observed temperature variability [ABSTRACT FROM AUTHOR]

Abstract: The Timed Up and Go (TUG) is one of the most widely used clinical tests to assess balance and mobility. An instrumented Timed Up and Go (iTUG) makes use of a specialized measurement system (e.g. an accelerometer) to identify and evaluate specific mobility skills. Nowadays a Smartphone (SP) comes with a large set of embedded sensors, including an accelerometer. An SP is a user-friendly device able to perform ubiquitous sensing with a variety of connectivity options. In this study we evaluate the validity of an SP for instrumenting the TUG. We examined 49 subjects (59±16 years old), without defining any inclusion criteria, using both an SP and a McRoberts Dynaport Hybrid, a device specifically designed for movement analysis. The statistical agreement between the two measurement systems is good for some of the parameters described in literature, which are of clear clinical value. Inter-rater reliability is often excellent and intra-rater reliability has been assessed in a subgroup of 25 subjects finding the same results for the two devices. In conclusion we found evidence that the SP is capable of becoming a pervasive and low-cost tool for the quantitative analysis of balance and mobility. [Copyright &y& Elsevier]