Machine Learning Findings on Geospatial Data of Users from the TrackYourStress mHealth Crowdsensing Platform

Pryss, Rüdiger; John, Dennis; Reichert, Manfred; Hoppenstedt, Burkhard; Schmid, Lukas; Schlee, Winfried; Spiliopoulou, Myra; Schobel, Johannes; Schickler, Marc; Langguth, Berthold; Probst, Thomas

doi:10.1109/iri.2019.00061

Cited by 16 publications

(12 citation statements)

References 32 publications

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“…In their insightful comparison of the results of EMA recordings with the TrackY-ourTinnitus mHealth app versus retrospective ratings of the users, only users with at least 10 days of interaction were considered [11]. For findings with the TrackYourStress platform that records EMA geolocation, only users with at least 10 recordings per day were considered [10].…”

Section: Related Workmentioning

confidence: 99%

Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?

Unnikrishnan

Shah

Schleicher

et al. 2020

Lecture Notes in Computer Science

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Some mHealth apps record user activity continuously and unobtrusively, while other apps rely by nature on user engagement and self-discipline: users are asked to enter data that cannot be assessed otherwise, e.g., on how they feel and what non-measurable symptoms they have. Over time, this leads to substantial differences in the length of the time series of recordings for the different users. In this study, we propose two algorithms for wellbeing-prediction from such time series, and we compare their performance on the users of a pilot study on diabetic patients - with time series length varying between 8 and 87 recordings. Our first approach learns a model from the few users, on which many recordings are available, and applies this model to predict the 2nd, 3rd, and so forth recording of users newly joining the mHealth platform. Our second approach rather exploits the similarity among the first few recordings of newly arriving users. Our results for the first approach indicate that the target variable for users who use the app for long are not predictive for users who use the app only for a short time. Our results for the second approach indicate that few initial recordings suffice to inform the predictive model and improve performance considerably.

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Section: Related Workmentioning

confidence: 99%

Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?

Unnikrishnan

Shah

Schleicher

et al. 2020

Lecture Notes in Computer Science

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“…In addition, when gathering additional contextual information from the TYT users, such as geospatial data, new investigations become possible. In a recent work [ 27 ], for example, we investigated geospatial data of mobile crowdsensing users and whether their movement behavior could be a predictor for their current stress situation. As this work also revealed promising results, in the next version of TYT, GPS data can be gathered while filling out the EMA-D questionnaire, if a user allows this measurement.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, there is an increasing consensus about its potential in the context of mobile technology [ 20 - 23 ]. However, the application of machine learning to a large group of users of an mHealth crowdsensing platform that gathers EMA datasets is still rare [ 19 , 24 - 27 ]. As we already found relevant differences between Android and iOS pertaining to the TYT users’ static characteristics at registration [ 12 ], this work investigates the following research question: Is it possible to predict the mobile OS used based on dynamic TYT data with high accuracy using machine learning methods?…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study

Pryss¹,

Schlee²,

Hoppenstedt³

et al. 2020

J Med Internet Res

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Background Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient’s quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)—Android and iOS—to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. Objective In this study, we explored whether the mobile OS—Android and iOS—used during user assessments can be predicted by the dynamic daily-life TYT data. Methods TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods—a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine—were applied to address the research question. Results Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. Conclusions In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder.

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“…Finding an adequate representation format for this kind of data is a key task in the design phase of such a platform. In general, computations on geospatial data are complex as operations on high-resolution coordinates, that are needed in order to aggregate geographically and hierarchically related data, are costly [ 29 ]. Therefore, it is of utmost importance to select an efficient approach for indexing and aggregating geospatial data.…”

Section: Background Informationmentioning

confidence: 99%

Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform

Birk

Reichert

Deshpande

et al. 2020

Sensors

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Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.

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Machine Learning Findings on Geospatial Data of Users from the TrackYourStress mHealth Crowdsensing Platform

Cited by 16 publications

References 32 publications

Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?

Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?

Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study

Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform

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