Motif clustering and digital biomarker extraction for free-living physical activity analysis
Abstract Background Analyzing free-living physical activity (PA) data presents challenges due to variability in daily routines and the lack of activity labels. Traditional approaches often rely on summary statistics, which may not capture the nuances of individual activity patterns. To address these...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-025-00424-1 |
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| author | Ya-Ting Liang Charlotte Wang |
| author_facet | Ya-Ting Liang Charlotte Wang |
| author_sort | Ya-Ting Liang |
| collection | DOAJ |
| description | Abstract Background Analyzing free-living physical activity (PA) data presents challenges due to variability in daily routines and the lack of activity labels. Traditional approaches often rely on summary statistics, which may not capture the nuances of individual activity patterns. To address these limitations and advance our understanding of the relationship between PA patterns and health outcomes, we propose a novel motif clustering algorithm that identifies and characterizes specific PA patterns. Methods This paper proposes an elastic distance-based motif clustering algorithm for identifying specific PA patterns (motifs) in free-living PA data. The algorithm segments long-term PA curves into short-term segments and utilizes elastic shape analysis to measure the similarity between activity segments. This enables the discovery of recurring motifs through pattern clustering. Then, functional principal component analysis (FPCA) is then used to extract digital biomarkers from each motif. These digital biomarkers can subsequently be used to explore the relationship between PA and health outcomes of interest. Results We demonstrate the efficacy of our method through three real-world applications. Results show that digital biomarkers derived from these motifs effectively capture the association between PA patterns and disease outcomes, improving the accuracy of patient classification. Conclusions This study introduced a novel approach to analyzing free-living PA data by identifying and characterizing specific activity patterns (motifs). The derived digital biomarkers provide a more nuanced understanding of PA and its impact on health, with potential applications in personalized health assessment and disease detection, offering a promising future for healthcare. |
| format | Article |
| id | doaj-art-7792a8c9f8f34a6fa71921dafedb4d26 |
| institution | DOAJ |
| issn | 1756-0381 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-7792a8c9f8f34a6fa71921dafedb4d262025-08-20T03:10:17ZengBMCBioData Mining1756-03812025-01-0118111610.1186/s13040-025-00424-1Motif clustering and digital biomarker extraction for free-living physical activity analysisYa-Ting Liang0Charlotte Wang1Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan UniversityInstitute of Health Data Analytics and Statistics, College of Public Health, National Taiwan UniversityAbstract Background Analyzing free-living physical activity (PA) data presents challenges due to variability in daily routines and the lack of activity labels. Traditional approaches often rely on summary statistics, which may not capture the nuances of individual activity patterns. To address these limitations and advance our understanding of the relationship between PA patterns and health outcomes, we propose a novel motif clustering algorithm that identifies and characterizes specific PA patterns. Methods This paper proposes an elastic distance-based motif clustering algorithm for identifying specific PA patterns (motifs) in free-living PA data. The algorithm segments long-term PA curves into short-term segments and utilizes elastic shape analysis to measure the similarity between activity segments. This enables the discovery of recurring motifs through pattern clustering. Then, functional principal component analysis (FPCA) is then used to extract digital biomarkers from each motif. These digital biomarkers can subsequently be used to explore the relationship between PA and health outcomes of interest. Results We demonstrate the efficacy of our method through three real-world applications. Results show that digital biomarkers derived from these motifs effectively capture the association between PA patterns and disease outcomes, improving the accuracy of patient classification. Conclusions This study introduced a novel approach to analyzing free-living PA data by identifying and characterizing specific activity patterns (motifs). The derived digital biomarkers provide a more nuanced understanding of PA and its impact on health, with potential applications in personalized health assessment and disease detection, offering a promising future for healthcare.https://doi.org/10.1186/s13040-025-00424-1Digital biomarkerActivity patternFunctional data analysisFeature extractionClusteringAssociation |
| spellingShingle | Ya-Ting Liang Charlotte Wang Motif clustering and digital biomarker extraction for free-living physical activity analysis BioData Mining Digital biomarker Activity pattern Functional data analysis Feature extraction Clustering Association |
| title | Motif clustering and digital biomarker extraction for free-living physical activity analysis |
| title_full | Motif clustering and digital biomarker extraction for free-living physical activity analysis |
| title_fullStr | Motif clustering and digital biomarker extraction for free-living physical activity analysis |
| title_full_unstemmed | Motif clustering and digital biomarker extraction for free-living physical activity analysis |
| title_short | Motif clustering and digital biomarker extraction for free-living physical activity analysis |
| title_sort | motif clustering and digital biomarker extraction for free living physical activity analysis |
| topic | Digital biomarker Activity pattern Functional data analysis Feature extraction Clustering Association |
| url | https://doi.org/10.1186/s13040-025-00424-1 |
| work_keys_str_mv | AT yatingliang motifclusteringanddigitalbiomarkerextractionforfreelivingphysicalactivityanalysis AT charlottewang motifclusteringanddigitalbiomarkerextractionforfreelivingphysicalactivityanalysis |