Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults

• URL: http://arxiv.org/abs/2606.00345v1
• Date: Fri, 29 May 2026 20:34:50 GMT
• Title: Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults
• Abstract summary: We present a longitudinal multimodal study of 66 older adults conducted in real-world conditions.<n>We investigate how the alignment between sensed signals and target variables affects predictive performance.
• Score: 1.1744028458220426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings. However, predictive modeling in longitudinal multimodal data remains challenging, particularly when targeting complex or clinically derived outcomes. In this work, we present a longitudinal multimodal study of 66 older adults conducted in real-world conditions and combining wearable sensing, behavioral monitoring, and clinical assessments. This setting provides a rare opportunity to study an underrepresented population in long-term, into-the-wild conditions. Building on this dataset, we investigate how the alignment between sensed signals and target variables affects predictive performance across health-related tasks. We design a unified evaluation framework spanning tasks with increasing levels of observability, including Activity Levels prediction, Sleep Duration estimation, and Sleep Apnea Severity classification. Our results reveal a clear gradient of predictability: highly observable behavioral targets achieve robust performance (macro-F1 65%), while more abstract outcomes remain challenging despite consistent improvements over baseline models. Moreover, through explainability analysis, we show that historical features consistently emerge as the most informative predictors, highlighting the central role of longitudinal information.

Related papers

• World Models for Embodied Intelligence: From Plausible to Controllable to Actionable [100.13060753774657]
  World models connect perception and decision-making in embodied intelligence by maintaining hidden state, anticipating consequences, comparing interventions, and adapting when execution departs from expectations.<n>This raises a central question: which predictive capabilities improve behavior?<n>We introduce three progressively stronger capability levels: Plausible models preserve task-relevant temporal, geometric, or physical structure; Controllable models additionally predict how interventions alter that structure; and Actionable models translate predictions into measurable gains in planning, action, learning, evaluation, verification, recovery, or data selection.
  arXiv  Detail & Related papers  (2026-09-15T06:22:42Z)
• Measuring the Behavioral Fidelity of Long-Horizon Human Activity Simulations [14.694276552510685]
  We introduce a framework for evaluating behavioral fidelity in long-horizon activity simulations.<n>As a case study, we collect a 43-hour dataset of in-the-wild office activity.<n>We find that behavioral fidelity is not uniform across metrics.
  arXiv  Detail & Related papers  (2026-09-01T13:51:11Z)
• SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics [49.771521708359955]
  SurgVista is a surgical world model that mitigates spatial interaction incoherence and temporal fidelity collapse.<n>It consistently outperforms state-of-the-art methods across visual quality, temporal consistency, and interaction fidelity.
  arXiv  Detail & Related papers  (2026-06-18T07:47:28Z)
• Towards a General Intelligence and Interface for Wearable Health Data [45.854819784803055]
  We propose a foundation model for wearable health that is pretrained on more than one trillion minutes of unlabeled sensor signals.<n>We find that this population scale representation unlocks label-efficient few-shot learning and generative capabilities for robust daily metric estimation.
  arXiv  Detail & Related papers  (2026-05-21T17:24:06Z)
• Longitudinal Digital Phenotyping for Early Cognitive-Motor Screening [6.034170250427599]
  We propose an AI-driven longitudinal framework to model developmental trajectories in children aged 18 months to 8 years.<n>Using a dataset of tablet-based interactions collected over multiple academic years, we analyzed six cognitive-motor tasks.<n>Our analysis reveals three distinct profiles: low, medium, and high performance.
  arXiv  Detail & Related papers  (2026-03-26T17:27:49Z)
• Signals of Success and Struggle: Early Prediction and Physiological Signatures of Human Performance across Task Complexity [15.610977452709617]
  We use early ocular and cardiac signals to predict later performance in a game environment.<n>High performers exhibited targeted gaze and adjusted visual sampling, and sustained more stable cardiac activation as demands intensified.
  arXiv  Detail & Related papers  (2026-03-19T11:47:57Z)
• The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs [60.15472325639723]
  Personality traits have long been studied as predictors of human behavior.<n>Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems.
  arXiv  Detail & Related papers  (2025-09-03T21:27:10Z)
• Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia [44.39545678576284]
  This study analyzes home activity data from individuals living with dementia by proposing a two-stage, self-supervised learning approach.<n>The first stage converts time-series activities into text sequences encoded by a pre-trained language model.<n>This PageRank vector captures latent state transitions, effectively compressing complex behaviour data into a succinct form.
  arXiv  Detail & Related papers  (2025-02-13T10:57:25Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• A Self-supervised Framework for Improved Data-Driven Monitoring of Stress via Multi-modal Passive Sensing [7.084068935028644]
  We propose a multi-modal semi-supervised framework for tracking physiological precursors of the stress response. Our methodology enables utilizing multi-modal data of differing domains and resolutions from wearable devices. We perform training experiments using a corpus of real-world data on perceived stress.
  arXiv  Detail & Related papers  (2023-03-24T20:34:46Z)
• Dataset Bias in Human Activity Recognition [57.91018542715725]
  This contribution statistically curates the training data to assess to what degree the physical characteristics of humans influence HAR performance. We evaluate the performance of a state-of-the-art convolutional neural network on two HAR datasets that vary in the sensors, activities, and recording for time-series HAR.
  arXiv  Detail & Related papers  (2023-01-19T12:33:50Z)
• Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data [75.23250968928578]
  Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
  arXiv  Detail & Related papers  (2020-09-25T01:50:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.