One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study we use smart home and wearable sensors to collect data while (n=84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an AUC value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant.
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 January 2017.
All research outputs
#17,286,645
of 25,377,790 outputs
Outputs from IEEE Journal of Biomedical and Health Informatics
#1,260
of 1,825 outputs
Outputs of similar age
#164,846
of 275,484 outputs
Outputs of similar age from IEEE Journal of Biomedical and Health Informatics
#19
of 24 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,825 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
