S. Khodadadeh, S. Zehtabian, J. Guilbe, R. Pearlman, B. Willenberg, B. Kim, E. A. Ross, L. Bölöni, and D. Turgut. Detecting unsafe use of a four-legged walker using IoT and deep learning. In Proc. of IEEE International Conference on Communications (ICC-2019), May 2019.
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Many elderly persons and patients recovering from trauma use four legged walkers to regain or retain mobility. Unfortunately, these walkers are also associated with many injuries, some of which are caused due to incorrect use. In this paper, we describe a sensor augmented walker, where the walker continuously monitors gait information. We describe an approach through which a deep learning-based algorithm learns to recognize dangerous use patterns which can lead to falls and injury. By notifying the user in real time about these patterns, the user can be trained to use the walker correctly in a safe manner.
@inproceedings{Khodadadeh-2019-ICC,
author = "S. Khodadadeh and S. Zehtabian and J. Guilbe and R. Pearlman and B. Willenberg and B. Kim and E. A. Ross and L. B{\"o}l{\"o}ni and D. Turgut",
title = "Detecting unsafe use of a four-legged walker using {IoT} and deep learning",
booktitle = "Proc. of IEEE International Conference on Communications (ICC-2019)",
year = "2019",
month = "May",
doi = "10.1109/ICC.2019.8761068",
abstract = {
Many elderly persons and patients recovering from trauma use four legged walkers to regain or retain mobility. Unfortunately, these walkers are also associated with many injuries, some of which are caused due to incorrect use. In this paper, we describe a sensor augmented walker, where the walker continuously monitors gait information. We describe an approach through which a deep learning-based algorithm learns to recognize dangerous use patterns which can lead to falls and injury. By notifying the user in real time about these patterns, the user can be trained to use the walker correctly in a safe manner.
},
}
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