Environment crowd-sensing for asthma management

Vasilățeanu, Andrei; Radu, Ioan Cosmin; Buga, Andreea

doi:10.1109/ehb.2015.7391363

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…For example, in previous works, we found interesting insights on data gathered with the TrackYourTinnitus (TYT) mHealth crowdsensing platform [1], [2]. Also other works have revealed new insights when using mobile crowdsensing in the context of chronic diseases [3]. In general, these findings are based on measurements in daily life, i.e., ambulatory assessments / Ecological Momentary Assessments (EMA).…”

Section: Introductionmentioning

confidence: 92%

Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform

Pryss

Schlee

Reichert

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Section: Introductionmentioning

confidence: 92%

Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform

Pryss

Schlee

Reichert

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…Regarding the medical context, three situations can arise. The first is activity management [133,134], the second, activity monitoring [7,83,126,128,129] and third, activity encouraging [135,136]. Activity management requires a reaction to a specific detected activity/condition, while activity monitoring and activity encouraging require only activity information collection.…”

Section: The Optimization Of Energy Consumption and Latency In Harmentioning

confidence: 99%

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Dinarevic

Husić

Baraković

2019

Sensors

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Human activity recognition (HAR) is a classification process that is used for recognizing human motions. A comprehensive review of currently considered approaches in each stage of HAR, as well as the influence of each HAR stage on energy consumption and latency is presented in this paper. It highlights various methods for the optimization of energy consumption and latency in each stage of HAR that has been used in literature and was analyzed in order to provide direction for the implementation of HAR in health and wellbeing applications. This paper analyses if and how each stage of the HAR process affects energy consumption and latency. It shows that data collection and filtering and data segmentation and classification stand out as key stages in achieving a balance between energy consumption and latency. Since latency is only critical for real-time HAR applications, the energy consumption of sensors and devices stands out as a key challenge for HAR implementation in health and wellbeing applications. Most of the approaches in overcoming challenges related to HAR implementation take place in the data collection, filtering and classification stages, while the data segmentation stage needs further exploration. Finally, this paper recommends a balance between energy consumption and latency for HAR in health and wellbeing applications, which takes into account the context and health of the target population.

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“…The term Mobile Crowd Sensing has been used to refer to this scenario with a formal definition presented by Guo et al [ 42 ] as: “a new sensing paradigm that empowers ordinary citizens to contribute data sensed or generated from their mobile devices, aggregates and fuses the data in the cloud for crowd intelligence extraction and people-centric service delivery.” One of the important areas that has benefited from Mobile Crowd Sensing is healthcare. For example, for the monitoring of tinnitus (the perception of noise in the ears) [ 43 ], asthma management [ 44 ], and mood recognition [ 45 ], to name a few. Other applications of crowd sensing are social network inference [ 46 ], traffic congestion avoidance [ 47 ], indoor location [ 48 ], etc .…”

Section: Related Workmentioning

confidence: 99%

Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances

Garcia-Ceja

Brena

2016

Sensors

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Human Activity Recognition (HAR) is an important part of ambient intelligence systems since it can provide user-context information, thus allowing a greater personalization of services. One of the problems with HAR systems is that the labeling process for the training data is costly, which has hindered its practical application. A common approach is to train a general model with the aggregated data from all users. The problem is that for a new target user, this model can perform poorly because it is biased towards the majority type of users and does not take into account the particular characteristics of the target user. To overcome this limitation, a user-dependent model can be trained with data only from the target user that will be optimal for this particular user; however, this requires a considerable amount of labeled data, which is cumbersome to obtain. In this work, we propose a method to build a personalized model for a given target user that does not require large amounts of labeled data. Our method uses data already labeled by a community of users to complement the scarce labeled data of the target user. Our results showed that the personalized model outperformed the general and the user-dependent models when labeled data is scarce.

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Environment crowd-sensing for asthma management

Cited by 6 publications

References 5 publications

Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform

Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform

Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications

Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances

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