Akshay Jain1, Mihail Popescu2, James Keller3, Marilyn Rantz4, Brianna Markway5. 1. Electrical Engineering and Computer Science, University of Missouri, USA. Electronic address: aj4g2@mail.missouri.edu. 2. Health Management and Informatics, University of Missouri, USA. Electronic address: popescum@health.missouri.edu. 3. Electrical Engineering and Computer Science, University of Missouri, USA. Electronic address: kellerj@missouri.edu. 4. Sinclair School of Nursing, University of Missouri, USA. Electronic address: rantzm@health.missouri.edu. 5. Electrical Engineering and Computer Science, University of Missouri, USA. Electronic address: markwayb@missouri.edu.
Abstract
INTRODUCTION: With 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. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: We 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.
INTRODUCTION: With 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. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: We 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.
Keywords:
Aging in place; Data to text; Early illness detection; In-home sensor monitoring; Linguistic protoform summary; Natural Language Generation; Sensor summarization
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