Healthwalks: sensing fine-grained individual health condition via mobility data
Abstract
Can health conditions be inferred from an individual's mobility pattern? Existing research has discussed the relationship between individual physical activity/mobility and well-being, yet no systematic study has been done to investigate the predictability of fine-grained health conditions from mobility, largely due to the unavailability of data and unsatisfactory modelling techniques. Here, we present a large-scale longitudinal study, where we collect the health conditions of 747 individuals who visit a hospital and tracked their mobility for 2 months in Beijing, China. To facilitate fine-grained individual health condition sensing, we propose HealthWalks, an interpretable machine learning model that takes user location traces, the associated points of interest, and user social demographics as input, at the core of which a Deterministic Finite Automaton (DFA) model is proposed to auto-generate explainable features to capture useful signals. We evaluate the effectiveness of our proposed model, which achieves 40.29% in micro-F1 and 31.63% in Macro-F1 for the 8-class disease category prediction, and outperforms the best baseline by 22.84% in Micro-F1 and 31.79% in Macro-F1. In addition, deeper analysis based on the SHapley Additive exPlanations (SHAP) showcases that HealthWalks can derive meaningful insights with regard to the correlation between mobility and health conditions, which provide important research insights and design implications for mobile sensing and health informatics.
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Healthwalks framework generates combinations of features using a deterministic automaton and its effectiveness is shown using an eight class disease classification.
“Healthwalks [94] builds such a framework that takes user location traces, associated points of interest and user social demographics as input and automatically generates combinations of features using a deterministic automaton (DFA). The effectiveness of the generated features is shown using an eight class disease classification.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [94]
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Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
- Authors
- Lin, Z., Lyu, S., Cao, H., Xu, F., Wei, Y., Samet, H., & Li, Y.
- Affiliations
- Beijing National Research Center for Information Science and Technology (CN)
- Stanford University (US)
- China Mobile (China) (CN)
- Department of Computer Science, University of Maryland, Maryland
- Venue
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Published
- 2020-12-17
- Volume
- 4
- Issue
- 4
- Pages
- 1-26
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W3116233757
- Cited by
- 18
- References
- 65
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 1 · 2022: 5 · 2021: 3
- FWCI
- 2.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- The National Key Research and Development Program of China (2018YFB1800804)
- research fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology
- the National Nature Science Foundation of China (U1936217, 61971267, 61972223, 61941117, 61861136003)
- Beijing Natural Science Foundation (L182038)
- Beijing National Research Center For Information Science And Technology (20031887521)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- health condition prediction, movement patterns, location traces, points of interest, deterministic finite automata, model interpretability, explainable features, disease classification, mobile sensing, SHAP, social demographics, hospitalized patients
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