Modeling Heart Rate and Activity Data for Personalized Fitness Recommendation
Open accessAbstract
Activity logs collected from wearable devices (e.g. Apple Watch, Fitbit, etc.) are a promising source of data to facilitate a wide range of applications such as personalized exercise scheduling, workout recommendation, and heart rate anomaly detection. However, such data are heterogeneous, noisy, diverse in scale and resolution, and have complex interdependencies, making them challenging to model. In this paper, we develop context-aware sequential models to capture the personalized and temporal patterns of fitness data. Specifically, we propose FitRec - an LSTM-based model that captures two levels of context information: context within a specific activity, and context across a user's activity history. We are specifically interested in (a) estimating a user's heart rate profile for a candidate activity; and (b) predicting and recommending suitable activities on this basis. We evaluate our model on a novel dataset containing over 250 thousand workout records coupled with hundreds of millions of parallel sensor measurements (e.g. heart rate, GPS) and metadata. We demonstrate that the model is able to learn contextual, personalized, and activity-specific dynamics of users' heart rate profiles during exercise. We evaluate the proposed model against baselines on several personalized recommendation tasks, showing the promise of using wearable data for activity modeling and recommendation.
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FitRec, an LSTM-based approach to modelling HR and activity data for personalized fitness recommendations, was able to learn activity-specific contextual and personalized dynamics of individual user HR profiles during exercise segments.
“For example, FitRec, an LSTM-based approach to modelling HR and activity data for personalized fitness recommendations, was able to learn activity-specific contextual and personalized dynamics of individual user HR profiles during exercise segments [56].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [56]
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A Novel Automatic Way of Finding Direct and Linear Sequential Inherent Relationships in the Data
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Using Wearables Data for Differentiating Between Injured and Non-Injured Athletes
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- Authors
- Ni, J., Muhlstein, L., & McAuley, J.
- Affiliations
- University of California San Diego (US)
- Venue
- The World Wide Web Conference
- Publisher
- ACM
- Published
- 2019-05-13
- Pages
- 1343-1353
- OpenAlex
- W2914622731
- Cited by
- 94
- References
- 25
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 20 · 2023: 20 · 2022: 11 · 2021: 16 · 2020: 9 · 2019: 1
- FWCI
- 3.02
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- heart rate prediction, personalized exercise recommendations, LSTM, GPS data
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