S. Zehtabian, S. Khodadadeh, R. Pearlman, B. Willenberg, B. Kim, D. Turgut, L. Bölöni, and E. A. Ross

Supporting rehabilitation prescription compliance with an IoT-augmented four-legged walker


Cite as:

S. Zehtabian, S. Khodadadeh, R. Pearlman, B. Willenberg, B. Kim, D. Turgut, L. Bölöni, and E. A. Ross. Supporting rehabilitation prescription compliance with an IoT-augmented four-legged walker. In Proc. of 2nd Workshop on AI for Aging, Rehabilitation and Independent Assisted Living (ARIAL-2018) at IJCAI-2018, July 2018.

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Abstract:

Four legged walkers enable mobility for elderly people, and can be an important component for rehabilitation after trauma. At the same time, the benefits of using a walker are contingent on its correct use, that sometimes requires some effort from the patient. Incorrect use might delay rehabilitation and can even lead to accidents and injuries. In this paper we present the hardware and software design for a IoT-augmented walker with features specifically targeted towards improving compliance with prescriptions for rehabilitation. The software of the walker autonomously processes sensor readings, identifies problems with prescription following and detects dangerous situations. The walker can provide feedback to the user helping with prescription conformance and alerts him/her for danger. The software agent also logs the data to a central database for later processing. Experiments with a prototype show that the walker can help the user comply better with the prescription.

BibTeX:

@inproceedings{Zehtabian-2018-ARIAL,
author = "S. Zehtabian and S. Khodadadeh and R. Pearlman and B. Willenberg and B. Kim and D. Turgut and L. B{\"o}l{\"o}ni and E. A. Ross",
booktitle={Proc. of 2nd Workshop on AI for Aging, Rehabilitation and Independent Assisted Living (ARIAL-2018) at IJCAI-2018},
title = "Supporting rehabilitation prescription compliance with an {IoT}-augmented four-legged walker",
location = "Stockholm, Sweden",
month = "July",
year = "2018",
abstract = {
  Four legged walkers enable mobility for elderly people, and can be an important component for rehabilitation after trauma. At the same time, the benefits of using a walker are contingent on its correct use, that sometimes requires some effort from the patient. Incorrect use might delay rehabilitation and can even lead to accidents and injuries.
  In this paper we present the hardware and software design for a IoT-augmented walker with features specifically targeted towards improving compliance with prescriptions for rehabilitation. The software of the walker autonomously processes sensor readings, identifies problems with prescription following and detects dangerous situations. The walker can provide feedback to the user helping with prescription conformance and alerts him/her for danger. The software agent also logs the data to a central database for later processing. Experiments with a prototype show that the walker can help the user comply better with the prescription.
 }
}

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