One of the biggest impediments to independent aging-in-place is the ability to respond when something goes wrong. By combining passive sensors like wearables with machine learning algorithms, modern technology can offer unobtrusive, private ways to monitor activities and learn when response is needed.
Just in the US, more than 10 million adults are 65+ years of age and live alone. One of the biggest impediments to independent aging-in-place is the ability to detect and respond when something goes wrong. We now have the ability to use passive sensors (like motion detectors and accelerometers) to monitor some aspects of an older person’s activity privately and unobtrusively. But we don’t yet have reliable ways to turn this information into an actionable view of what is happening to them in real life.
The SPHERE Inter-disciplinary Research Collaboration assembled a unique dataset in their research home in Bristol, UK. In addition to recording the outputs from smart technologies like wearables and environmental sensors, the center also has human-labeled descriptions of what seniors were doing—their activities, like meal preparation or watching TV; and their positions, like walking or sitting—while those technologies were in use. Competitors were charged with building computer algorithms that could translate the easierto- collect sensor data into harder-to-collect information about real activities.
Of the nearly 1000 submission generated during the competition, the winning models were able to make that translation correctly (predicting what seniors were doing as described by human researchers) with ~90% accuracy, a hugely impressive result. This performance improved ~35% over the previous benchmark and surpassed even internal state-of-the-art models that the researchers had developed.
Senior Data Science: Safe Aging with SPHERE: Rules and Terms of Data Use
DrivenData Competition Rules
One account per participant
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Private sharing of code
Privately sharing code or data outside of teams is not permitted.
Public dissemination of entries
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Open licensing of winners
Winning solutions need to be made available under The MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT) in order to be eligible for recognition and prize money if offered.
Documentation for winning solutions
Winning solutions must be documented using the provided Winning Model Documentation Template in order to be eligible for recognition and prize money if offered.
The number of submissions per day is restricted to a fixed value on a per-competition basis. Any attempt to circumvent this limit will result in disqualification.
You may use any other data sources provided that these data sources are (1) publicly available to all competitors during the entire length of the competition, (2) unencumbered by any usage costs, and (3) are disclosed at the end of the competition. As long as these conditions are met, any such external data need not be compatible with or releasable under the MIT license governing your source code.
ECML/PKDD 2016 Competitors
This platform is also being used to receive submissions for participants in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery workshop on the same data set (ECML/PKDD 2016, or the "ECML/PKDD Workshop Competition"). You do not have to compete as part of the ECML/PKDD Workshop Competition, but if you do then you must agree to be bound by the applicable rules established for that competition ("ECML/PKDD Rules"), described at http://www.irc-sphere.ac.uk/sphere-challenge/rules in addition to these Rules. In the event of any apparent contradiction or inconsistency between these rules and the workshop competition rules, these rules shall take precedence.
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Selection of winners
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Data use and code sharing
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