Safe <i>physical</i> human–robot interaction of mobility assistance robots: evaluation index and control

Pervez, Aslam; Ryu, Jeha

doi:10.1017/s0263574710000706

Cited by 8 publications

(4 citation statements)

References 45 publications

(62 reference statements)

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“…In terms of safety provision during each of the assistance levels, several aspects are worth mentioning. In particular, Pervez et al, proposed a danger index to estimate user’s safety during mobility assistance [ 50 ]. Even though this study does not calculate such indicators, it avoids most of the unsafe situations highlighted in Pervez et al, On the one hand, it is pointed out that for safe assistance there should not be any appreciable speed mismatch between the assistive robot and the user.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…The proposed Concept Mobility System is a robotic system with sensing and actuation systems that adopts a human centric robotics approach [ 21 , 22 ] to achieve the following physical attributes and functions:…”

Section: Methodsmentioning

confidence: 99%

IMOVE—An Intuitive Concept Mobility Systems for Perioperative Transfer and Induction of Anaesthesia for Special Needs Children

Hee

Ng²,

Cruz³

et al. 2020

Sensors

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Children with autistic spectrum disorder (ASD) often exhibit uncontrollable disruptive behaviour during transfer to the operating room and operating table and at the induction of anaesthesia (sleep). This process often involves the physical restraining of children. These children are then lifted onto the operating table by healthcare staff after being anaesthetized. This predisposes children to fall risk and hospital staff to musculoskeletal injuries. We developed two concept mobility devices, IMOVE-I and -II, based on robotics systems comprising of restraint modules and multi-positional modality (sitting, supine, Trendelenburg). The aim was to intuitively secure children to facilitate the safe induction of sleep and ease of transfer onto operating tables upon sleep. IMOVE-I loads the child in standing position using a dual arm restraint module that is activated by trained healthcare staff. IMOVE-II loads the child in the sitting position by motivating the self-application of restraints. Opinions were obtained from 21 operating theatre healthcare staff with experience in the care of ASD children and parents with ASD children. The mean satisfaction rating of IMOVE-I was 5.62 (95% CI 5.00, 6.27) versus 8.10 (95% CI 7.64, 8.55) in IMOVE-II, p < 0.001. IMOVE-II is favoured over IMOVE-I in system operation and safety, ease of use and module functionality.

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“…pHMI-based technologies should consider additional uncertainties which can result from dissimilar behavioral attitudes due to psychological, sociological, or cultural differences or involve different physical-physiological characteristics [14]. A well-posed pHMI-based technology should adjust to or dampen out these uncertainties, assuring safe operational conditions [13, 15], satisfy appropriate efficiency, and avoid possible execution and evaluation errors, providing accurate and intuitive feedback responses [16]. The real-time control and monitoring of the contact forces are one of the main design specifications for pHMI applications.…”

Section: Introductionmentioning

confidence: 99%

Contact force regulation in physical human-machine interaction based on model predictive control

Pacheco Quiñones,

Paterna,

De Benedictis

et al. 2023

Robotica

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With increasing attention to physical human-machine interaction (pHMI), new control methods involving contact force regulation in collaborative and coexistence scenarios have spread in recent years. Thanks to its internal robustness, high dynamic performance, and capabilities to avoid constraint violations, a Model Predictive Control (MPC) action can pose a viable solution to manage the uncertainties involved in those applications. This paper uses an MPC-driven control method that aims to apply a well-defined and tunable force impulse on a human subject. After describing a general control design suitable to achieve this goal, a practical implementation of such a logic, based on an MPC controller, is shown. In particular, the physical interaction considered is the one occurring between the body of a patient and an external perturbation device in a dynamic posturography trial. The device prototype is presented in both its hardware architecture and software design. The MPC-based main control parameters are thus tuned inside hardware-in-the-loop and human-in-the-loop environments to get optimal behaviors. Finally, the device performance is analyzed to assess the MPC algorithm’s accuracy, repeatability, flexibility, and robustness concerning the several uncertainties due to the specific pHMI environment considered.

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Safe physical human–robot interaction of mobility assistance robots: evaluation index and control

Cited by 8 publications

References 45 publications

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

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

IMOVE—An Intuitive Concept Mobility Systems for Perioperative Transfer and Induction of Anaesthesia for Special Needs Children

Contact force regulation in physical human-machine interaction based on model predictive control

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