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Showing papers by "Eemil Lagerspetz published in 2020"


Journal ArticleDOI
TL;DR: A research vision of real-time massive scale air quality sensing that integrates tens of thousands or even millions of air quality sensors to monitor air quality at fine spatial and temporal resolution is presented.
Abstract: Dangers associated with poor air quality are driving deployments of air quality monitoring technology. These deployments rely on either professional-grade measurement stations or a small number of low-cost sensors integrated into urban infrastructure. In this article, we present a research vision of real-time massive scale air quality sensing that integrates tens of thousands or even millions of air quality sensors to monitor air quality at fine spatial and temporal resolution. We highlight opportunities and challenges of our vision by discussing use cases, key requirements, and reference technologies in order to establish a roadmap on how to realize this vision. We address the feasibility of our vision, introducing a testbed deployment in Helsinki, Finland, and carrying out controlled experiments that address collaborative and opportunistic sensor calibration, a key research challenge for our vision.

58 citations


Journal ArticleDOI
TL;DR: It is shown that even category-level aggregated application usage can predict Big Five traits at up to 86%–96% prediction fit in the authors' sample, and that when studying personality, application categories can provide sufficient predictions in general traits.

17 citations


Proceedings ArticleDOI
09 Nov 2020
TL;DR: The design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality and its applications have applications in policy intervention management mechanisms and design of clean air routing and healthier navigation applications to reduce pollution exposure.
Abstract: Air pollution is a contributor to approximately one in every nine deaths annually. To counteract health issues resulting from air pollution, air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality monitoring stations are expensive to maintain, resulting in sparse coverage. In this paper, we introduce the design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality. MegaSense is able to produce aggregated, privacy-aware maps and history graphs of collected pollution data. It provides a feedback loop in the form of personal outdoor and indoor air pollution exposure information, allowing citizens to take measures to avoid future exposure. We present a battery-powered, portable low-cost air quality sensor design for sampling PM 2.5 and air pollutant gases in different micro-environments. We validate the approach with a use case in Helsinki, deploying MegaSense with citizens carrying low-maintenance portable sensors, and using smart phone exposure apps. We demonstrate daily air pollution exposure profiles and the air pollution hot-spot profile of a district. Our contributions have applications in policy intervention management mechanisms and design of clean air routing and healthier navigation applications to reduce pollution exposure.

16 citations


Proceedings ArticleDOI
10 Sep 2020
TL;DR: This paper presents preliminary insights into behavioral patterns between smartphone usage and sleep quality by using crowdsensed data and provides a methodological pipeline for future work towards understanding the relationship between daily smartphone usage patterns andSleep quality in the wild.
Abstract: Smartphone usage and sleep quality have established connections in psychological research, but in the HCI context, the topic is still understudied. In this paper, we present preliminary insights into behavioral patterns between smartphone usage and sleep quality by using crowdsensed data. We utilize a large-scale mobile usage dataset and a PHQ-8 depression questionnaire answered by 743 participants from varying age groups and socioeconomic backgrounds. Based on our preliminary results, we provide a methodological pipeline for future work towards understanding the relationship between daily smartphone usage patterns and sleep quality in the wild.

7 citations


Journal ArticleDOI
10 Jan 2020
TL;DR: In this article, the authors consider the problem of measuring the energy footprint of IoT devices using hardware power monitors (such as Monsoon power meter), which can provide an accurate view of instantaneous power use, however, power meters require direct connection with the device's power source (e.g., battery).
Abstract: With great power comes - besides great responsibility - big energy drain, especially where Internet of Things (IoT) devices are considered. Indeed, despite significant improvements in design and manufacturing, energy efficiency remains a critical design consideration for IoT, particularly for devices operating continuous sensing. The energy footprint of these devices has traditionally been measured using hardware power monitors (such as Monsoon power meter), which provide an accurate view of instantaneous power use. However, power meters require direct connection with the device's power source (such as battery) and hence can be used to measure energy drain only on devices with detachable power sources.

7 citations


Journal ArticleDOI
30 Sep 2020
TL;DR: Today, an increasing number of systems produce, process, and store personal and intimate data, which has plenty of potential for entirely new types of software applications, as well as for impr...
Abstract: Today, an increasing number of systems produce, process, and store personal and intimate data. Such data has plenty of potential for entirely new types of software applications, as well as for improving old applications, particularly in the domain of smart healthcare. However, utilizing this data, especially when it is continuously generated by sensors and other devices, with the current approaches is complex—data is often using proprietary formats and storage, and mixing and matching data of different origin is not easy. Furthermore, many of the systems are such that they should stimulate interactions with humans, which further complicates the systems. In this article, we introduce the Human Data Model—a new tool and a programming model for programmers and end users with scripting skills that help combine data from various sources, perform computations, and develop and schedule computer-human interactions. Written in JavaScript, the software implementing the model can be run on almost any computer either inside the browser or using Node.js. Its source code can be freely downloaded from GitHub, and the implementation can be used with the existing IoT platforms. As a whole, the work is inspired by several interviews with professionals, and an online survey among healthcare and education professionals, where the results show that the interviewed subjects almost entirely lack ideas on how to benefit the ever-increasing amount of data measured of the humans. We believe that this is because of the missing support for programming models for accessing and handling the data, which can be satisfied with the Human Data Model.

2 citations