Trial reportJournal of biomedical informatics2019
Linguistic summarization of in-home sensor data.
Trial report in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Classifying Adverse Events from SOAP Notes and Sensor Features in a Clinical Trial of Older Adults.medRxiv : the preprint server for health sciences · 2025Article
- Enhancing Eldercare: Assessing Clinician's Perception of Linguistic Summaries in Health Monitoring Alert Systems.Applied clinical informatics · 2025Article
- Smart Technology for Aging and Reducing Disability (STAReD): Study protocol for a randomized pragmatic clinical trial.Contemporary clinical trials · 2024Article
- A semi-supervised approach to unobtrusively predict abnormality in breathing patterns using hydraulic bed sensor data in older adults aging in place.Journal of biomedical informatics · 2023Article
- Linguistic Explanations of Black Box Deep Learning Detectors on Simulated Aerial Drone Imagery.Sensors (Basel, Switzerland) · 2023Article
- Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention.ACM transactions on computing for healthcare · 2022Article
- New Linguistic Description Approach for Time Series and its Application to Bed Restlessness Monitoring for Eldercare.IEEE transactions on fuzzy systems : a publication of the IEEE Neural Networks Council · 2022Article
- Fuzzy Protoform for Hyperactive Behaviour Detection Based on Commercial Devices.International journal of environmental research and public health · 2020Article
- Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges.Nursing outlookArticle
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
introductionWith the increase in the population of older adults around the world, a significant amount of work has been done on in-home sensor technology to aid the elderly age independently. However, due to the large amounts of data generated by the sensors, it takes a lot of effort and time for the clinicians to makes sense of this data. In this work, we develop a system to help make this data more useful by presenting it in the form of natural language.
methodsWe start by identifying important attributes in the sensor data that are relevant to the health of the elderly. We then develop algorithms to extract these important health related features from the sensor parameters and summarize them in natural language. We focus on making the natural language summaries to be informative, accurate and concise.
resultsWe designed multiple surveys using real and synthetic data to validate the summaries produced by our algorithms. We show that the algorithms produce meaningful results comparable to human subjects. We also implemented our linguistic summarization system to produce summaries of data leading to health alerts derived from the sensor data. The system is running live in 110 apartments currently. By the means of retrospective case studies, we illustrate that the linguistic summaries are able to make the connection between changes in the sensor data and the health of the elderly.
conclusionsWe present a system that extracts important clinically relevant features from in-home sensor data generated in the apartments of the elderly and summarize those features in natural language. The preliminary testing of our summarization system shows that it has the potential to help the clinicians utilize this data effectively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.