Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929715387826176 |
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| author | Cui, Jin Capstick, Alexander Barnaghi, Payam Scott, Gregory |
| author_facet | Cui, Jin Capstick, Alexander Barnaghi, Payam Scott, Gregory |
| contents | In the analysis of remote healthcare monitoring data, time series representation learning offers substantial value in uncovering deeper patterns of patient behavior, especially given the fine temporal granularity of the data. In this study, we focus on a dataset of home activity records from people living with Dementia. We propose a two-stage self-supervised learning approach. The first stage involves converting time-series activities into text strings, which are then encoded by a fine-tuned language model. In the second stage, these time-series vectors are bi-dimensionalized for applying PageRank method, to analyze latent state transitions to quantitatively assess participants behavioral patterns and identify activity biases. These insights, combined with diagnostic data, aim to support personalized care interventions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09947 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model Cui, Jin Capstick, Alexander Barnaghi, Payam Scott, Gregory Artificial Intelligence Machine Learning In the analysis of remote healthcare monitoring data, time series representation learning offers substantial value in uncovering deeper patterns of patient behavior, especially given the fine temporal granularity of the data. In this study, we focus on a dataset of home activity records from people living with Dementia. We propose a two-stage self-supervised learning approach. The first stage involves converting time-series activities into text strings, which are then encoded by a fine-tuned language model. In the second stage, these time-series vectors are bi-dimensionalized for applying PageRank method, to analyze latent state transitions to quantitatively assess participants behavioral patterns and identify activity biases. These insights, combined with diagnostic data, aim to support personalized care interventions. |
| title | Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.09947 |