ISR-AIWALKER: Robotic Walker for Intuitive and Safe Mobility Assistance and Gait Analysis

Paulo, João; Peixoto, Paulo; Nunes, Urbano

doi:10.1109/thms.2017.2759807

Cited by 45 publications

(23 citation statements)

References 28 publications

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“…2 Different sensor types (e.g. vision-based sensors, 3 inertial measurement units, 4 force sensors 5 ) have been used to implement gait analysis on a smart walker. Independent of the technical implementations, to our knowledge, previous validation studies of smart walker–integrated gait analysis systems suffered from methodological shortcomings such as small sample sizes, participants not representative of potential users, no criterion standard comparisons and/or no statistical analyses.…”

Section: Introductionmentioning

confidence: 99%

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Assessing the concurrent validity of a gait analysis system integrated into a smart walker in older adults with gait impairments

et al. 2019

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Objective: To assess the concurrent validity of a smart walker–integrated gait analysis system with the GAITRite® system for measuring spatiotemporal gait parameters in potential users of the smart walker. Design: Criterion standard validation study. Setting: Research laboratory in a geriatric hospital. Participants: Twenty-five older adults (⩾65 years) with gait impairments (habitual rollator use and/or gait speed <0.6 m/s) and no severe cognitive impairment (Mini-Mental State Examination ⩾17). Main measures: Stride, swing and stance time; stride length; and gait speed were simultaneously recorded using the smart walker–integrated gait analysis system and the GAITRite system while participants walked along a 7.8-m walkway with the smart walker. Concurrent criterion-related validity was assessed using the Bland–Altman method, percentage errors (acceptable if <30%), and intraclass correlation coefficients for consistency (ICC3,1) and absolute agreement (ICC2,1). Results: Bias for stride, swing and stance time ranged from −0.04 to 0.04 seconds, with acceptable percentage errors (8.7%–23.0%). Stride length and gait speed showed higher bias (meanbias (SD) = 0.20 (0.11) m; 0.19 (0.13) m/s) and not acceptable percentage errors (31.3%–42.3%). Limits of agreement were considerably narrower for temporal than for spatial-related gait parameters. All gait parameters showed good-to-excellent consistency (ICC3,1 = 0.72–0.97). Absolute agreement was good-to-excellent for temporal (ICC2,1 = 0.72–0.97) but only poor-to-fair for spatial-related gait parameters (ICC2,1 = 0.37–0.52). Conclusion: The smart walker–integrated gait analysis system has good concurrent validity with the GAITRite system for measuring temporal but not spatial-related gait parameters in potential end-users of the smart walker. Stride length and gait speed can be measured with good consistency, but with only limited absolute accuracy.

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Section: Introductionmentioning

confidence: 99%

“…Independent of the technical implementations, to our knowledge, previous validation studies of smart walker–integrated gait analysis systems suffered from methodological shortcomings such as small sample sizes, participants not representative of potential users, no criterion standard comparisons and/or no statistical analyses. 3–5…”

Section: Introductionmentioning

confidence: 99%

Assessing the concurrent validity of a gait analysis system integrated into a smart walker in older adults with gait impairments

et al. 2019

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“…Paulo et al [ 9 ] implemented ISR-AIWALKER, a robotic walker using computer vision as the primary human–machine interface modality. This contrasts with previous approaches that primarily relied on force sensing.…”

Section: Related Workmentioning

confidence: 99%

A Smart Walker for People with Both Visual and Mobility Impairment

Mostofa

Feltner

Fullin

et al. 2021

Sensors

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In recent years, significant work has been done in technological enhancements for mobility aids (smart walkers). However, most of this work does not cover the millions of people who have both mobility and visual impairments. In this paper, we design and study four different configurations of smart walkers that are specifically targeted to the needs of this population. We investigated different sensing technologies (ultrasound-based, infrared depth cameras and RGB cameras with advanced computer vision processing), software configurations, and user interface modalities (haptic and audio signal based). Our experiments show that there are several engineering choices that can be used in the design of such assistive devices. Furthermore, we found that a holistic evaluation of the end-to-end performance of the systems is necessary, as the quality of the user interface often has a larger impact on the overall performance than increases in the sensing accuracy beyond a certain point.

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“…For examples, a passive walker is powered by the user-supplied forces with controlled brakes [ 8 , 9 , 10 ]. Recently, the walker powered by motors to steer the walker has attracted a lot of attention [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Patel et al [ 11 ] used an active walker as the experimental platform to exploit the interactions between an intelligent mobility aid and the human operator.…”

Section: Introductionmentioning

confidence: 99%

ROS-Based Smart Walker with Fuzzy Posture Judgement and Power Assistance

Chang

Sahoo

Chen

et al. 2021

Sensors

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In recent years the increased rate of the aging population has become more serious. With aging, the elderly sometimes inevitably faces many problems which lead to slow walking, unstable or weak limbs and even fall-related injuries. So, it is very important to develop an assistive aid device. In this study, a fuzzy controller-based smart walker with a distributed robot operating system (ROS) framework is designed to assist in independent walking. The combination of Raspberry Pi and PIC microcontroller acts as the control kernel of the proposed device. In addition, the environmental information and user postures can be recognized with the integration of sensors. The sensing data include the road slope, velocity of the walker, and user’s grip forces, etc. According to the sensing data, the fuzzy controller can produce an assistive force to make the walker moving more smoothly and safely. Apart from this, a mobile application (App) is designed that allows the user’s guardian to view the current status of the smart walker as well as to track the user’s location.

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ISR-AIWALKER: Robotic Walker for Intuitive and Safe Mobility Assistance and Gait Analysis

Cited by 45 publications

References 28 publications

Assessing the concurrent validity of a gait analysis system integrated into a smart walker in older adults with gait impairments

Assessing the concurrent validity of a gait analysis system integrated into a smart walker in older adults with gait impairments

A Smart Walker for People with Both Visual and Mobility Impairment

ROS-Based Smart Walker with Fuzzy Posture Judgement and Power Assistance

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