Dealing With Inaccurate Sensor Data in the Context of Mobile Crowdsensing and mHealth

• Published in: IEEE journal of biomedical and health informatics 2022-11, Vol.26 (11), p.5439-5449
• Main Authors: Kraft, Robin, Hofmann, Fabian, Reichert, Manfred, Pryss, Rudiger
• Format: Article
• Language: English
• Subjects: Background noise; Bioinformatics; Biomedical measurement; Context; Crowdsensing; Data models; Electronic devices; environmental sound; Error reduction; mHealth; Mobile applications; noise measurement; Performance evaluation; Principal component analysis; Principal components analysis; sensor anomaly detection; Sensors; Smartphones; Telemedicine; Tinnitus; Wireless sensor networks
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2022.3198254

The technological capabilities and ubiquity of smart mobile devices favor the combined utilization of Ecological Momentary Assessments (EMA) and Mobile Crowdsensing (MCS). In the healthcare domain, this combination particularly enables the collection of ecologically valid and longitudinal data. Furthermore, the context in which these data are collected can be captured through the use of smartphone sensors as well as externally connected sensors. The TrackYourTinnitus (TYT) mobile platform uses these concepts to collect the user's individual subjective perception of tinnitus as well as an objective environmental sound level. However, the sound level data in the TYT database are subject to several possible sensor errors and therefore do not allow a meaningful interpretation in terms of correlation with tinnitus symptoms. To this end, a data-centric approach based on Principal Component Analysis (PCA) is proposed in this paper to cleanse MCS mHealth data sets from erroneous sensor data. To further improve the approach, additional information (i.e., responses to the EMA questionnaire) is considered in the PCA and a prior check for constant values is performed. To demonstrate the practical feasibility of the approach, in addition to TYT data, where it is generally unknown which sensor measurements are actually erroneous, a simulation with generated data was designed and performed to evaluate the performance of the approach with different parameters based on different quality metrics. The results obtained show that the approach is able to detect an average of 29.02% of the errors, with an average false-positive rate of 14.11%, yielding an overall error reduction of 22.74%.