Abstract
Falls are a significant problem for the elderly living independently in the home. Many falls occur due to household objects left in open spaces. We present KinSpace, a passive obstacle detection system for the home. KinSpace employs the use of a Kinect sensor to learn the open space of an environment through observation of resident walking patterns. It then monitors the open space for obstacles that are potential tripping hazards and notifies the residents accordingly. KinSpace uses real-time depth data and human-in-the-loop feedback to adjust its understanding of the open space of an environment. We present a 5,000-frame deployment dataset spanning multiple homes and classes of objects. We present results showing the effectiveness of our underlying technical solutions in identifying open spaces and obstacles. The results for both lab testing and a deployment in an actual home show roughly 80% accuracy for both open space detection and obstacle detection even in the presence of many real-world issues. Consequently, this new technology shows great potential to reduce the risk of falls in the home due to environmental hazards.
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Greenwood, C. et al. (2014). KinSpace: Passive Obstacle Detection via Kinect. In: Krishnamachari, B., Murphy, A.L., Trigoni, N. (eds) Wireless Sensor Networks. EWSN 2014. Lecture Notes in Computer Science, vol 8354. Springer, Cham. https://doi.org/10.1007/978-3-319-04651-8_12
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DOI: https://doi.org/10.1007/978-3-319-04651-8_12
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