The main objective of this work is to provide a review of existing research work into predictive, personalized, preventive and participatory medicine in telemedicine and ehealth. The academic databases used for searches are IEEE Xplore, PubMed, Science Direct, Web of Science and ResearchGate, taking into account publication dates from 2010 up to the present day. These databases cover the greatest amount of information on scientific texts in multidisciplinary fields, from engineering to medicine. Various search criteria were established, such as ("Predictive" OR "Personalized" OR "Preventive" OR "Participatory") AND "Medicine" AND ("eHealth" OR "Telemedicine") selecting the articles of most interest. A total of 184 publications about predictive, personalized, preventive and participatory (4P) medicine in telemedicine and ehealth were found, of which 48 were identified as relevant. Many of the publications found show how the P4 medicine is being developed in the world and the benefits it provides for patients with different illnesses. After the revision that was undertaken, it can be said that P4 medicine is a vital factor for the improvement of medical services. It is hoped that one of the main contributions of this study is to provide an insight into how P4 medicine in telemedicine and ehealth is being applied, as well as proposing outlines for the future that contribute to the improvement of prevention and prediction of illnesses.

Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature

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.

https://www.mdpi.com/1424-8220/16/6/877/pdf

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

Crowdsensing in the case of health-related information can enable a new mode of health data collection, able to collect more detailed and real-world health and physiological data than possible via traditional means. Various incentivization schemes for crowdsensing in general have been proposed, but there are characteristics specific to the health scenario that have been less well explored. Health crowdsensing can also be considered on a health condition-centric basis as 1) those with specific health conditions more often inherently collect data relevant to that condition; and 2) they are more motivated to see knowledge and research developments relevant to that condition and hence potentially more willing to take the steps needed to be a participant. In this paper we introduce a taxonomy of inventive mechanisms for health crowdsensing, and review the specific nature of incentivization for the case of health-related data. Finally, we present an incentive mechanism for collecting health-related data based on the well known best response dynamics in which participants have incomplete information on other participant’s costs. A game theoretical multi-window framework is introduced where a stable solution is achieved after a number of iterations.

Incentivization for Health Crowdsensing

mHealth technologies are increasingly utilized in various medical contexts. Mobile crowdsensing is such a technology, which is often used for data collection scenarios related to questions on chronic disorders. One prominent reason for the latter setting is based on the fact that powerful Ecological Momentary Assessments (EMA) can be performed. Notably, when mobile crowdsensing solutions are used to integrate EMA measurements, many new challenges arise. For example, the measurements must be provided in the same way on different mobile operating systems. However, the newly given possibilities can surpass the challenges. For example, if different mobile operating systems must be technically provided, one direction could be to investigate whether users of different mobile operating systems pose a different behaviour when performing EMA measurements. In a previous work, we investigated differences between iOS and Android users from the TrackYourTinnitus mHealth crowdsensing platform, which has the goal to reveal insights on the daily fluctuations of tinnitus patients. In this work, we investigated differences between iOS and Android users from the TrackYourHearing mHealth crowdsensing platform, which aims at insights on the daily fluctuations of patients with hearing loss. We analyzed 3767 EMA measurements based on a daily applied questionnaire of 84 patients. Statistical analyses have been conducted to see whether these 84 patients differ with respect to the used mobile operating system and their given answers to the EMA measurements. We present the obtained results and compare them to the previous mentioned study. Our insights show the differences in the two studies and that the overall results are worth being investigated in a more in-depth manner. Particularly, it must be investigated whether the used mobile operating system constitutes a confounder when gathering EMA-based data through a crowdsensing platform.

/pdf/ecological-momentary-assessment-based-differences-between-3xlx88r59w.pdf

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

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.

https://www.mdpi.com/1424-8220/19/23/5206/pdf

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

Chronic diseases are the main cause of mortality in developed countries. Among them, asthma has an increasing prevalence in urban areas. We propose to use the crowd-sensing paradigm for monitoring the environment, especially the air quality parameters relevant for asthma patients. Asthma is a good candidate for participatory medicine approaches as current medical guidelines promote self-management of the disease by informed and ICT enabled patients. The paper continues our work for m-health applications for asthma patients and focuses on decentralized air quality monitoring by the patients themselves using affordable and available hardware platforms based on Arduino.

Ioan Cosmin Radu

Papers

Environment crowd-sensing for asthma management