ISR-AIWALKER: Robotic Walker for Intuitive and Safe Mobility Assistance and Gait Analysis
Robotic walkers are assistive robotic devices that provide mobility assistance, in a domestic or clinical scenario, to individuals suffering from a gait disorder, being age related or due to injuries, surgery, or diseases. Walkers also provide a significant potential for lower limb rehabilitation. I...
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Published in | IEEE transactions on human-machine systems Vol. 47; no. 6; pp. 1110 - 1122 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
IEEE
01.12.2017
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Subjects | |
Online Access | Get full text |
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Summary: | Robotic walkers are assistive robotic devices that provide mobility assistance, in a domestic or clinical scenario, to individuals suffering from a gait disorder, being age related or due to injuries, surgery, or diseases. Walkers also provide a significant potential for lower limb rehabilitation. In this paper, we present a novel multimodal robotic walker platform, the ISR-AIWALKER, where innovative contributions were made both in the human- machine interface (HMI) and in a gait analysis system placed on board the platform. Taking into account the application potential of these devices, an effort was made to use low-cost sensors without sacrificing the overall performance of the system. A change was made in the HMI paradigm, moving from a force-sensing to a vision-based approach, while maintaining a natural user interaction and adding complementary safety features like correct gripping enforcement. To cope with the close proximity of the user's body, a multimodal sensor setup was considered. Using both RGB and depth map data, a kinematic model of the user's lower limbs is obtained, allowing the identification of a set of features that are used in a machine learning approach to discriminate gait asymmetries. Experiments made with several subjects revealed that the proposed HMI is able to correctly estimate the user intention in a natural and intuitive way. The gait analysis system was also evaluated and evidenced a good discrimination capability to distinguish between different gait patterns. |
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ISSN: | 2168-2291 2168-2305 |
DOI: | 10.1109/THMS.2017.2759807 |