A user's intention detection method for smart walker

Cheng, Wood-Hi; Wu, Yanzhi

doi:10.1109/icawst.2017.8256477

Cited by 8 publications

(11 citation statements)

References 13 publications

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“…In addition, other robotic walkers have been reported in the literature, including different HRI interfaces [41,42,43,44]. For instance, the approach developed by Ye et al [42] includes a width changeable walker that adapts to the user’s intentions and environment constraints.…”

Section: Related Workmentioning

confidence: 99%

Human–Robot–Environment Interaction Interface for Smart Walker Assisted Gait: AGoRA Walker

Garzón

Múnera

et al. 2019

Sensors

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The constant growth of the population with mobility impairments has led to the development of several gait assistance devices. Among these, smart walkers have emerged to provide physical and cognitive interactions during rehabilitation and assistance therapies, by means of robotic and electronic technologies. In this sense, this paper presents the development and implementation of a human–robot–environment interface on a robotic platform that emulates a smart walker, the AGoRA Walker. The interface includes modules such as a navigation system, a human detection system, a safety rules system, a user interaction system, a social interaction system and a set of autonomous and shared control strategies. The interface was validated through several tests on healthy volunteers with no gait impairments. The platform performance and usability was assessed, finding natural and intuitive interaction over the implemented control strategies.

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Section: Related Workmentioning

confidence: 99%

Human–Robot–Environment Interaction Interface for Smart Walker Assisted Gait: AGoRA Walker

Garzón

Múnera

et al. 2019

Sensors

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“…Most of the current SWs decode human motion directly, demanding some level of physical intervention from the user and/or an extra load of cognitive effort. This direct mode has been typically implemented with specially designed handlebars, including force/pressure/load sensors (Huang et al, 2005;Rodriguez-losada, 2008;Spenko et al, 2006;Jiménez et al, 2019;Cheng and Wu, 2017), infrared cameras and Light Emitting Diodes (Paulo et al, 2017) or Hall sensors (Park et al, 2019), which require specific hand motions to encode each walking direction. In an indirect mode, the walker becomes responsible for analysing the end-user's movement, inferring, from this, the walking directions.…”

Section: Human Motion Decoding In Smart Walkersmentioning

confidence: 99%

“…Several sensors embedded in SWs have also been used for this purpose, although entailing other limitations that hinder rehabilitation. For instance, force sensors (Cheng and Wu, 2017;Sierra et al, 2018) present a reduced long-term effectiveness (Paulo et al, 2017), infrared sensors (Paulo et al, 2015) can be easily corrupted by light conditions and a handlebar specially designed with hall sensors (Park et al, 2019) implies specific movements besides the natural gait, increasing the patient's cognitive load. Additionally, preprocessing may also be required to clean the output signals, for instance, when resorting to IMU or force sensors.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation

Gonçalves¹,

Lopes²,

Moccia³

et al. 2023

Preprint

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Gait disabilities are among the most frequent impairments worldwide. Their treatment increasingly relies on rehabilitation therapies, in which smart walkers are being introduced to empower the user's recovery state and autonomy, while reducing the clinicians effort. For that, these should be able to decode human motion and needs, as early as possible. Current walkers decode motion intention using information gathered from wearable or embedded sensors, namely inertial units, force sensors, hall sensors, and lasers, whose main limitations imply an expensive solution or hinder the perception of human movement. Smart walkers commonly lack an advanced and seamless human-robot interaction, which intuitively and promptly understands human motions. A contactless approach is proposed in this work, addressing human motion decoding as an early action recognition/detection problematic, using RGB-D cameras. We studied different deep learning-based algorithms, organised in three different approaches, to process lower body RGB-D video sequences, recorded from an embedded camera of a smart walker, and classify them into 4 classes (stop, walk, turn right/left). A custom dataset involving 15 healthy participants walking with the walker device was acquired and prepared, resulting in 28800 balanced RGB-D frames, to train and evaluate the deep learning networks. The best results were attained by a convolutional neural network with a channel-wise attention mechanism, reaching accuracy values of 99.61% and above 93%, for offline early detection/recognition and trial simulations, respectively. Following the hypothesis that human lower body features encode prominent information, fostering a more robust prediction towards real-time applications, the algorithm focus was also quantitatively evaluated using Dice metric, leading to values slightly higher than 30%. Promising results were attained for early action detection as a human motion decoding strategy, with enhancements in the focus of the proposed architectures.

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“…Moving away from using force sensing to pressure sensing, Cheng et al [44] developed a user's intention detection algorithm for their proposed smart walker to assist home care for the elderly. The authors modified a four-wheeled commercial smart walker.…”

Section: Development Of Robotic Walkers For Adultsmentioning

confidence: 99%

Reimagining robotic walkers for real-world outdoor play environments with insights from legged robots: a scoping review

Stewart-Height

Koditschek

Johnson

2021

Disability and Rehabilitation: Assistive Technology

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Purpose: For children with mobility impairments, without cognitive delays, who want to participate in outdoor activities, existing assistive technology (AT) to support their needs are limited. In this review, we investigate the control and design of a selection of robotic walkers while exploring a selection of legged robots to develop solutions that address this gap in robotic AT. Method: We performed a comprehensive literature search from four main databases: PubMed, Google Scholar, Scopus, and IEEE Xplore. The keywords used in the search were the following: "walker", "rollator", "smart walker", "robotic walker", "robotic rollator". Studies were required to discuss the control or design of robotic walkers to be considered. A total of 159 papers were analyzed. Results: From the 159 papers, 127 were excluded since they failed to meet our inclusion criteria. The total number of papers analyzed included publications that utilized the same device, therefore we classified the remaining 32 studies into groups based on the type of robotic walker used. This paper reviewed 15 different types of robotic walkers. Conclusions: The ability of many legged robots to negotiate and transition between a range of unstructured substrates suggests several avenues of future consideration whose pursuit could benefit robotic AT, particularly regarding the present limitations of wheeled pediatric robotic walkers for children's daily outside use.

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A user's intention detection method for smart walker

Cited by 8 publications

References 13 publications

Human–Robot–Environment Interaction Interface for Smart Walker Assisted Gait: AGoRA Walker

Human–Robot–Environment Interaction Interface for Smart Walker Assisted Gait: AGoRA Walker

Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation

Reimagining robotic walkers for real-world outdoor play environments with insights from legged robots: a scoping review

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