ArticleSensors (Basel, Switzerland)2019
NurseNet: Monitoring Elderly Levels of Activity with a Piezoelectric Floor.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence, machine learning, and deep learning in women's health nursing.Korean journal of women health nursing · 2020Article
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Authors and funding
2 authors.
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Abstract
Monitoring the activity of elderly individuals in nursing homes is key, as it has been shown that physical activity leads to significant health improvement. In this work, we introduce NurseNet, a system that combines an unobtrusive, affordable, and robust piezoelectric floor sensor with a convolutional neural network algorithm, which aims at measuring elderly physical activity. Our algorithm is trained using signal embedding based on atoms of a pre-learned dictionary and focuses the network's attention on step-related signals. We show that NurseNet is able to avoid the main limitation of floor sensors by recognizing relevant signals (i.e., signals produced by patients) and ignoring events related to the medical staff, offering a new tool to monitor elderly activity in nursing homes efficiently.
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Registered trials
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