Development of an Interface for Human-Robot Interaction on a Robotic Platform for Gait Assistance: AGoRA Smart Walker

Sierra, Sergio; Molina, Juan F.; Gómez, Daniel Armando Olivera; Múnera, Marcela; Cifuentes, Carlos A.

doi:10.1109/andescon.2018.8564594

Cited by 14 publications

(5 citation statements)

References 20 publications

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“…Moreover, such navigation tasks on smart walkers require the consideration of user interaction strategies, obstacle detection and avoidance techniques, as well as social interaction strategies. Particularly, the navigation system presented in this work considers map building, autonomous localization, obstacle avoidance and path following strategies and is based on previous developments of the authors [52].…”

Section: Rei Interfacementioning

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: Rei Interfacementioning

confidence: 99%

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

Garzón

Múnera

et al. 2019

Sensors

Self Cite

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“…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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“…A 2D LiDAR, which has some advantages of no external markers and simple installation, has been used in autonomous vehicles [15], [16], mobile robot navigation [17], [18] and human gait tracking [19]- [24]. Comparing to a 3D LiDAR, a 2D LiDAR is less expensive both in terms of price and computational cost.…”

Section: Introductionmentioning

confidence: 99%

“…Comparing to a 3D LiDAR, a 2D LiDAR is less expensive both in terms of price and computational cost. The LiDAR can be placed on a moving platform such as mobile robots or smart walkers to track human gait during walking [19], [20]. Some additional sensors such as IMU and encoders need to be installed in these systems to estimate the orientation of moving platform.…”

Section: Introductionmentioning

confidence: 99%

Human Gait Estimation Using Multiple 2D LiDARs

Duong

Suh

2021

IEEE Access

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This paper proposes a human gait estimation algorithm using a multiple 360 degree 2D LiDARs system. The system is fixed on the ground at the shin height to scan human legs during gait. The multiple LiDARs system is to overcome the drawbacks of a single LiDAR system, which could lose data due to occlusion between legs during walking and has a short tracking range. The performance of a sensor fusion system strongly depends on the calibration. In this paper, we propose a calibration method using a cylinder with known radius as a specific marker. In contrast to other methods, the calibration parameters and the cylinder center points at different positions are estimated by a proposed iterative algorithm. The measurement noises in the LiDAR output are considered to increase the accuracy of calibration and human leg center points estimation. Instead of using least square fitting of circle algorithm to estimate the leg center point, a new iterative algorithm which includes measurement noises is proposed. Although multiple LiDARs are used, the discontinuities of leg center points could still happen. Therefore, a quadratic optimization based eighth-order splines algorithm is derived to interpolate and smooth the data. Two configurations of three LiDARs are tested in the experiment. The former is the triangle configuration in which the whole walking path is covered by all three LiDARs. This configuration minimizes the occlusion between legs. The maximum RMSE of step length estimation of this configuration compared with the optical camera system is 0.03m. The latter is the line configuration in which each LiDAR covers a certain walking path sequentially. This configuration maximizes the tracking range. The experiment with 20m straight walking has the RMSE of about 0.10m.

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Development of an Interface for Human-Robot Interaction on a Robotic Platform for Gait Assistance: AGoRA Smart Walker

Cited by 14 publications

References 20 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

Human Gait Estimation Using Multiple 2D LiDARs

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