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Abstract The increasing prevalence of wearable devices enables low-cost, long-term collection of health relevant data such as heart rate, exercise, and sleep signals. Currently these data are used to monitor short term changes with limited interpretation of their relevance to health. These data provide an untapped resource to monitor daily and long-term activity patterns. Changes and trends identified from such data can provide insights and guidance to the management of many chronic conditions that change over time. In this study we conducted a machine learning based analysis of longitudinal heart rate data collected over multiple years from Fitbit devices. We built a multi-resolutional pipeline for time series analysis, using model-free clustering methods inspired by statistical conformal prediction framework. With this method, we were able to detect health relevant events, their interesting patterns (e.g., daily routines, seasonal differences, and anomalies), and correlations to acute and chronic changes in health conditions. We present the results, lessons, and insights learned, and how to address the challenge of lack of labels. The study confirms the value of long-term heart rate data for health monitoring and surveillance, as complementary to extensive yet intermittent examinations by health care providers.
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HeartInsightify: Interpreting Longitudinal Heart Rate Data for Health Insights through Conformal Clustering
Heart rate, a commonly accessible health data from most wearables, carries rich information of a person’s well-being, yet remains of limited deep health applications, due to the lack of groundtruth of health events and their impact on heart rate patterns. Specifically, standard health analytics usually are designed based on well-modeled health conditions thus known data patterns and rich training data. To bridge the gap, we propose HeartInsightify, an exploratory framework that facilitates the process of deriving health-relevant measurable indicators from longitudinal heart rate data, without any of the above knowledge. HeartInsightify focuses on comparative and qualitative study, using model-free statistical methods such as conformal prediction, to study similarities, perform clustering and detect outliers, and build multi-resolutional data summaries, allowing human experts to efficiently examine and verify their health relevance. We conduct extensive experiments to evaluate HeartInsightify using individuals’ free-living heart rate data collected through Fitbit over 6 years. We illustrate the process of analyzing heart rate data for its health relevance and demonstrate the effectiveness of HeartInsightify. We envision that HeartInsightify lays the groundwork for personalized health analytics with continuous monitoring data from wearables.
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- Award ID(s):
- 1951880
- PAR ID:
- 10536446
- Publisher / Repository:
- IEEE International Conference on Bioinformatics and Biomedicine
- Date Published:
- Format(s):
- Medium: X
- Location:
- Istanbul, Turkey
- Sponsoring Org:
- National Science Foundation
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