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Most mobile health apps employ data visualization to help people view their health and activity data, but these apps provide limited support for visual data exploration. Furthermore, despite its huge potential benefits, mobile visualization research in the personal data context is sparse. This work aims to empower people to easily navigate and compare their personal health data on smartphones by enabling flexible time manipulation with speech. We designed and developed Data@Hand, a mobile app that leverages the synergy of two complementary modalities: speech and touch. Through an exploratory study with 13 long-term Fitbit users, we examined how multimodal interaction helps participants explore their own health data. Participants successfully adopted multimodal interaction (i.e., speech and touch) for convenient and fluid data exploration. Based on the quantitative and qualitative findings, we discuss design implications and opportunities with multimodal interaction for better supporting visual data exploration on mobile devices.
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Multimodal Time-Series Activity Forecasting for Adaptive Lifestyle Intervention Design
Physical activity is a cornerstone of chronic conditions and one of the most critical factors in reducing the risks of cardiovascular diseases, the leading cause of death in the United States. App-based lifestyle interventions have been utilized to promote physical activity in people with or at risk for chronic conditions. However, these mHealth tools have remained largely static and do not adapt to the changing behavior of the user. In a step toward designing adaptive interventions, we propose BeWell24Plus, a framework for monitoring activity and user engagement and developing computational models for outcome prediction and intervention design. In particular, we focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance. Knowing in advance how active a person is going to be in the next day can help with designing adaptive interventions that help individuals achieve their physical activity goals. Our technique combines the recent history of a person's physical activity with app engagement metrics such as when, how often, and for how long the app was used to forecast the near future's activity. We formulate the problem of multimodal activity forecasting and propose an LSTM-based realization of our proposed model architecture, which estimates physical activity outcomes in advance by examining the history of app usage and physical activity of the user. We demonstrate the effectiveness of our forecasting approach using data collected with 58 prediabetic people in a 9-month user study. We show that our multimodal forecasting approach outperforms single-modality forecasting by 2.2$ to 11.1% in mean-absolute-error.
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- PAR ID:
- 10389477
- Date Published:
- Journal Name:
- 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN)
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BSN56160.2022.9928521
