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


Cite as:

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 IEEE ICC 2019, pp. 1–6, May 2019.

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Abstract:

Four legged walkers are used by many elderly persons to retain mobility. They are also used by patients recovering from leg injuries to facilitate rehabilitation. Unfortunately, these walkers are also associated with many injuries, some of which are caused by incorrect use. In this paper, we describe a walker augmented with IoT sensors which continuously monitors the weight distribution on the legs of the walker. We describe an approach where this data stream is processed by a deep neural network based classifier, which learns to recognize dangerous use patterns that can lead to falls and injury. The classifier is trained by providing examples of unsafe use, thus eliminating the costly engineering necessary to customize the algorithm to the specific user and walker. By alerting the user in real time about unsafe use patterns, the user can learn the correct and safe use of the walker.

BibTeX:

@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 = "IEEE ICC 2019",
	pages = "1-6",
	year = "2019",
	month = "May",
	abstract = {Four legged walkers are used by many elderly persons to retain mobility. They are also used by patients recovering from leg injuries to facilitate rehabilitation. Unfortunately, these walkers are also associated with many injuries, some of which are caused by incorrect use. In this paper, we describe a walker augmented with IoT sensors which continuously monitors the weight distribution on the legs of the walker. We describe an approach where this data stream is processed by a deep neural network based classifier, which learns to recognize dangerous use patterns that can lead to falls and injury. The classifier is trained by providing examples of unsafe use, thus eliminating the costly engineering necessary to customize the algorithm to the specific user and walker. By alerting the user in real time about unsafe use patterns, the user can learn the correct and safe use of the walker. },
}

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