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

M., Sergio D. Sierra; Garzón, Mario; Múnera, Marcela; Cifuentes, Carlos A.

doi:10.3390/s19132897

Cited by 49 publications

(53 citation statements)

References 58 publications

(66 reference statements)

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“…The optimal parameter set was then used on human subjects where the controller was directly compared to manual control with a within subjects design. Due to a within-subjects design, the sample size was (N = 6) subjects and is comparable to previous related work (25,26,29).…”

Section: Resultssupporting

confidence: 55%

Human-Robot Interaction with Robust Prediction of Movement Intention Surpasses Manual Control

Veselič

Zito

Farina

2020

Preprint

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Designing robotic assistance devices for manipulation tasks is challenging. This work aims at improving accuracy and usability of physical human-robot interaction (pHRI) where a user interacts with a physical robotic device (e.g., a human operated manipulator or exoskeleton) by transmitting signals which need to be interpreted by the machine. Typically these signals are used as an open-loop control, but this approach has several limitations such as low take-up and high cognitive burden for the user. In contrast, a control framework is proposed that can respond robustly and efficiently to intentions of a user by reacting proactively to their commands. The key insight is to include context- and user-awareness in the controller, improving decision making on how to assist the user. Context-awareness is achieved by creating a set of candidate grasp targets and reach-to grasp trajectories in a cluttered scene. User-awareness is implemented as a linear time-variant feedback controller (TV-LQR) over the generated trajectories to facilitate the motion towards the most likely intention of a user. The system also dynamically recovers from incorrect predictions. Experimental results in a virtual environment of two degrees of freedom control show the capability of this approach to outperform manual control. By robustly predicting the user’s intention, the proposed controller allows the subject to achieve superhuman performance in terms of accuracy and thereby usability.

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Section: Resultssupporting

confidence: 55%

Human-Robot Interaction with Robust Prediction of Movement Intention Surpasses Manual Control

Veselič

Zito

Farina

2020

Preprint

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“…This device is a robotic walker mounted on a commercial robot (Pioneer LX, Omron Adept, Amherst, NH, USA), emulating the structural frame and functionality of an assistive smart walker (See Figure 1). This platform uses several sensors, actuators, and processing units: (1) Two motorized wheels and two caster wheels for propulsion and stability; (2) two encoders and one Inertial Measurement Unit (IMU) to measure position, orientation, and speed; (3) a 2D Light Detection and Ranging Sensor (LiDAR) (S300 Expert, SICK, Waldkirch, Germany) for environment sensing; (4) two ultrasonic boards for user detection and low-rise obstacles detection; (5) two tri-axial force sensors (MTA400, FUTEK, Irvine, CA, USA) to estimate the user's navigation commands; and (6) a 2D Laser Range-Finder (LRF) (Hokuyo URG-04LX-UG01, Osaka, Japan) for the user's gait estimation [19]. The device's onboard CPU runs a Linux distribution to support the Robotic Operating System (ROS) framework and the software requirements [19].…”

Section: Robotic Platform Descriptionmentioning

confidence: 99%

Evaluation of Physical Interaction during Walker-Assisted Gait with the AGoRA Walker: Strategies Based on Virtual Mechanical Stiffness

Múnera

Provot

et al. 2021

Sensors

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Smart walkers are commonly used as potential gait assistance devices, to provide physical and cognitive assistance within rehabilitation and clinical scenarios. To understand such rehabilitation processes, several biomechanical studies have been conducted to assess human gait with passive and active walkers. Several sessions were conducted with 11 healthy volunteers to assess three interaction strategies based on passive, low and high mechanical stiffness values on the AGoRA Smart Walker. The trials were carried out in a motion analysis laboratory. Kinematic data were also collected from the smart walker sensory interface. The interaction force between users and the device was recorded. The force required under passive and low stiffness modes was 56.66% and 67.48% smaller than the high stiffness mode, respectively. An increase of 17.03% for the hip range of motion, as well as the highest trunk’s inclination, were obtained under the resistive mode, suggesting a compensating motion to exert a higher impulse force on the device. Kinematic and physical interaction data suggested that the high stiffness mode significantly affected the users’ gait pattern. Results suggested that users compensated their kinematics, tilting their trunk and lower limbs to exert higher impulse forces on the device.

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“…This device has proven to be feasible in future clinical applications. Another approach to walking assistance is AGORA [5]. It is a robotic walker (smart walker) that includes several system modules: navigation, human detection, safety, user interaction and social interaction.…”

Section: Contributionsmentioning

confidence: 99%

Assistance Robotics and Biosensors 2019

Úbeda

Torres

Méndez

2020

Sensors

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

Cited by 49 publications

References 58 publications

Human-Robot Interaction with Robust Prediction of Movement Intention Surpasses Manual Control

Human-Robot Interaction with Robust Prediction of Movement Intention Surpasses Manual Control

Evaluation of Physical Interaction during Walker-Assisted Gait with the AGoRA Walker: Strategies Based on Virtual Mechanical Stiffness

Assistance Robotics and Biosensors 2019

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