148,588 research outputs found

    Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities

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    There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.Comment: 6 pages, 2 figures, In Proceedings of the NIPS Workshop on Machine Learning for Healthcare 2017 (ML4H 2017). Colocated with NIPS 201

    TMSA: participatory sensing based on mobile phones in urban space

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    A design for a novel mobile sensing system, called Temperature Measurement System Architecture (TMSA), that uses people as mobile sensing nodes in a network to capture spatiotemporal properties of pedestrians in urban environments is presented in this paper. In this dynamic, microservices approach, real-time data and an open-source IoT platform are combined to provide weather conditions based on information generated by a fleet of mobile sensing platforms. TMSA also offers several advantages over traditional methods using participatory sensing or more recently crowd-sourced data from mobile devices, as it provides a framework in which citizens can bring to light data relevant to urban planning services or learn human behaviour patterns, aiming to change users’ attitudes or behaviors through social influence. In this paper, we motivate the need for and demonstrate the potential of such a sensing paradigm, which supports a host of new research and application developments, and illustrate this with a practical urban sensing example.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by na tional funds through FCT – Fundação para a Ciência e Tecnologia through project ˆ UIDB/04728/2020

    An IoT-based solution for monitoring a fleet of educational buildings focusing on energy efficiency

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    Raising awareness among young people and changing their behaviour and habits concerning energy usage iskey to achieving sustained energy saving. Additionally, young people are very sensitive to environmental protection so raising awareness among children is much easier than with any other group of citizens. This work examinesways to create an innovative Information & Communication Technologies (ICT) ecosystem (including web-based, mobile, social and sensing elements) tailored specifically for school environments, taking into account both theusers (faculty, staff, students, parents) and school buildings, thus motivating and supporting young citizenÅ› behavioural change to achieve greater energy efficiency. A mixture of open-source IoT hardware and proprietary platforms on the infrastructure level, are currently being utilized for monitoring a fleet of 18 educational buildings across 3 countries, comprising over 700 IoT monitoring points. Hereon presented is the system's high-level architecture, as well as several aspects of its implementation, related to the application domain of educational building monitoring and energy efficiency. The system is developed based on open-source technologies andservices in order to make it capable of providing open IT-infrastructure and support from different commercial hardware/sensor vendors as well as open-source solutions. The system presented can be used to develop and offer newapp-based solutions that can be used either for educational purposes or for managing the energy efficiency ofthebuilding. The system is replicable and adaptable to settings that may be different than the scenarios envisionedhere (e.g., targeting different climate zones), different IT infrastructures and can be easily extended to accommodate integration with other systems. The overall performance of the system is evaluated in real-world environment in terms of scalability, responsiveness and simplicity

    Efficient FFT Algorithms for Mobile Devices

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    Increased traffic on wireless communication infrastructure has exacerbated the limited availability of radio frequency ({RF}) resources. Spectrum sharing is a possible solution to this problem that requires devices equipped with Cognitive Radio ({CR}) capabilities. A widely employed technique to enable {CR} is real-time {RF} spectrum analysis by applying the Fast Fourier Transform ({FFT}). Today’s mobile devices actually provide enough computing resources to perform not only the {FFT} but also wireless communication functions and protocols by software according to the software-defined radios paradigm. In addition to that, the pervasive availability of mobile devices make them powerful computing platform for new services. This thesis studies the feasibility of using mobile devices as a novel spectrum sensing platform with focus on {FFT}-based spectrum sensing algorithms. We benchmark several open-source {FFT} libraries on an Android smartphone. We relate the efficiency of calculating the {FFT} to both algorithmic and implementation-related aspects. The benchmark results also show the clear potential of special {FFT} algorithms that are tailored for sparse spectrum detection

    An evaluation of plume tracking as a strategy for gas source localization in turbulent wind flows

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    Gas source localization is likely the most direct application of a mobile robot endowed with gas sensing capabilities. Multiple algorithms have been proposed to locate the gas source within a known environment, ranging from bio-inspired to probabilistic ones. However, their application to real-world conditions still remains a major issue due to the great difficulties those scenarios bring, among others, the common presence of obstacles which hamper the movement of the robot and notably ncrease the complexity of the gas dispersion. In this work, we consider a plume tracking algorithm based on the well-known silkworm moth strategy and analyze its performance when facing different realistic environments characterized by the presence of obstacles and turbulent wind flows. We rely on computational fluid dynamics and the open source gas dispersion simulator GADEN to generate realistic gas distributions in scenarios where the presence of obstacles breaks down the ideal downwind plume. We first propose some modifications to the original silkworm moth algorithm in order to deal with the presence of obstacles in the environment (avoiding collisions) and then analyze its performance within four challenging environments.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Proyecto de excelencia de la Junata de Andalucia TEP2012-53

    An open - source mobile geospatial platform for promoting climate - smart livelihood - landscape systems in Fiji and Tonga

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    Communities in Fiji and Tonga rely on landscape services to support a variety of livelihoods. These communities are increasingly vulnerable to climate (e.g. increasing cyclone occurrence and intensity) and environmental (e.g. mining and deforestation) stressors. Within these landscape systems, accurate and timely monitoring of human-climate-environment interactions is important to inform landscape management, land use policies, and climate-smart sustainable development. Data collection and monitoring approaches exist to capture landscape-livelihood information such as surveys, participatory GIS (PGIS), and remote sensing. However, these monitoring approaches are challenged by data collection and management burdens, timely integration of databases and data streams, aligning system requirements with local needs, and socio-technical issues associated with low-resource development contexts. Such monitoring approaches only provide static representation of livelihood-landscape interactions failing to capture the dynamic nature of vulnerabilities, and benefit only a small user base. We present a prototype of a mobile, open-source geospatial tool being collaboratively developed with the Ministries of Agriculture in Fiji and Tonga and local stakeholders, to address the above shortcomings of PGIS and other environmental monitoring and data sharing approaches. The tool is being developed using open-source mobile GIS technologies following a formal ICT for Development (ICT4D) framework. We discuss the results for each component of the ICT4D framework which involves multiple landscape stakeholders across the two Small Island Developing States. Based on the ICT4D user requirements analysis, we produced a prototype open-source mobile geospatial data collection, analysis and sharing tool. New dynamic spatial data layers related to landscape use and climate were specifically developed for use in the tool. We present the functionality of the tool alongside the results of field-testing with stakeholders in Fiji and Tonga

    A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN

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    Air quality, especially particulate matter, has recently attracted a lot of attention from governments, industry, and academia, motivating the use of denser air quality monitoring networks based on low-cost sensing strategies. However, low-cost sensors are frequently sensitive to aging, environmental conditions, and pollutant cross-sensitivities. These issues have been only partially addressed, limiting their usage. In this study, we develop a low-cost particulate matter monitoring system based on special-purpose acquisition boards, deployed for monitoring air quality on both stationary and mobile sensor platforms. We explore the influence of all model variables, the quality of different calibration strategies, the accuracy across different concentration ranges, and the usefulness of redundant sensors placed in each station. The collected sensor data amounts to about 50GB of data, gathered in six months during the winter season. Tests of statically immovable stations include an analysis of accuracy and sensors’ reliability made by comparing our results with more accurate and expensive standard β radiation sensors. Tests on mobile stations have been designed to analyze the reactivity of our system to unexpected and abrupt events. These experiments embrace traffic analysis, pollution investigation using different means of transport and pollution analysis during peculiar events. With respect to other approaches, our methodology has been proved to be extremely easy to calibrate, to offer a very high sample rate (one sample per second), and to be based on an open-source software architecture. Database and software are available as open source in [1]

    Towards Odor-Sensitive Mobile Robots

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    J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment
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