Towards Automated Smart Mobile Crowdsensing for Tinnitus Research

Abstract

Tinnitus is a disorder that is not entirely understood, and many of its correlations are still unknown. On the other hand, smartphones became ubiquitous. Their modern versions provide high computational capabilities, reasonable battery size, and a bunch of embedded high-quality sensors, combined with an accepted user interface and an application ecosystem. For tinnitus, as for many other health problems, there are a number of apps trying to help patients, therapists, and researchers to get insights into personal characteristics but also into scientific correlations as such. In this paper, we present the first approach to an app in this context, called TinnituSense that does automatic sensing of related characteristics and enables correlations to the current condition of the patient by a combined participatory sensing, e.g., a questionnaire. For tinnitus, there is a strong hypothesis that weather conditions have some influence. Our proof-of-concept implementation records weather-related sensor data and correlates them to the standard Tinnitus Handicap Inventory (THI) questionnaire. Thus, TinnituSense enables therapists and researchers to collect evidence for unknown facts, as this is the first opportunity to correlate weather to patient conditions on a larger scale. Our concept as such is limited neither to tinnitus nor to built-in sensors, e.g., in the tinnitus domain, we are experimenting with mobile EEG sensors. TinnituSense is faced with several challenges of which we already solved principle architecture, sensor management, and energy consumption. Index Terms-mobile crowdsensing; tinnitus; automated mobile sensing; mobile health;

Multidisciplinary Tinnitus Research: Challenges and Future Directions From the Perspective of Early Stage Researchers

“…With mobile apps, thousands of observations from users can be collected, and patterns of variation in tinnitus loudness and distress could be used to identify potential tinnitus subtypes. For a review on this topic, see (Mehdi et al, 2019). Similarly, online recruitment databases could be used to increase sample sizes and, in turn, capture aspects of tinnitus heterogeneity different from clinical samples.…”

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

“…Diet self-monitoring and exercise management [52], [53], [83], [96] Understanding population eating patterns, episodes, and disorders [97] Tinnitus Tinnitus self measurement and retraining therapy [98], [99] Studying symptoms, causes, and treatments of tinnitus population [100], [101], [102], [103] COVID-19 Automatic self-diagnosis [104], [105], [106], [107] Population screening the spread of COVID-19 [108], [109], [110], [111] Contact tracing for infectious risk estimation [112], [113], [114], [115] Public Health Policy Evaluation and Development [116]- [118], [119]- [121], [122]- [124] tionnaires. For example, by studying the correlation between anxiety and behavioral indicators (e.g., activity locations, text messages, and calls) in a 54-students group over two weeks, Boukhechba et al [73] proposed flexible anxiety assessment methods for monitoring college students via mobile phone apps.…”

Smartphone and Mobile Health Apps for Tinnitus: Systematic Identification, Analysis, and Assessment

“…Specifically, smartphones provide an app ecosystem that can easily be extended with new apps. For instance, on one hand, smartphone-based app solutions can be applied to monitor the ecological or environmental surroundings of patients to better understand health phenomena [ 9 , 10 ]. On the other hand, these solutions can also easily be designed or tailored to assist patients in managing or mitigating the symptoms of their health problems [ 11 ].…”