scholarly journals Toward an Integrated Intervention and Assessment of Robot-Based Rehabilitation

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Amirhossein Majidirad ◽  
Yimesker Yihun ◽  
Laila Cure
Keyword(s):  
Force Sensor ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Feature Reduction ◽  
Physiological Data ◽  
Training Procedure ◽  
Interaction Forces ◽  
Response Data ◽  
Robotic Devices ◽  
Research Challenge

Abstract This study presents robot-based rehabilitation and its assessment. Robotic devices have significantly been useful to help therapists do the training procedure consistently. However, as robotic devices interface with humans, quantifying the interaction and its intended outcomes is still a research challenge. In this study, human–robot interaction during rehabilitation is assessed based on measurable interaction forces and human physiological response data, and correlations are established to plan the intervention and effective limb trajectories within the intended rehabilitation and interaction forces. In this study, the Universal Robot 5 (UR5) is used to guide and support the arm of a subject over a predefined trajectory while recording muscle activities through surface electromyography (sEMG) signals using the Trigno wireless DELSYS devices. The interaction force is measured through the force sensor mounted on the robot end-effector. The force signals and the human physiological data are analyzed and classified to infer the related progress. Feature reduction and selection techniques are used to identify redundant inputs and outputs.

scholarly journals Fiber Bragg Gratings in CYTOP Fibers Embedded in a 3D-Printed Flexible Support for Assessment of Human–Robot Interaction Forces

Materials ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2305 ◽  
Author(s):  
Arnaldo Leal-Junior ◽  
Antreas Theodosiou ◽  
Camilo Díaz ◽  
Carlos Marques ◽  
Maria Pontes ◽  
...  
Keyword(s):  
Thermoplastic Polyurethane ◽  
Fiber Bragg Gratings ◽  
Acrylonitrile Butadiene Styrene ◽  
Bragg Gratings ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Polymer Optical Fiber ◽  
Robot Interaction ◽  
Interaction Forces ◽  
Flexible Support

We developed a flexible support with embedded polymer optical fiber (POF) sensors for the assessment of human–robot interaction forces. The supports were fabricated with a three-dimensional (3D) printer, where an acrylonitrile butadiene styrene (ABS) rigid structure was used in the region of the support in which the exoskeleton was attached, whereas a thermoplastic polyurethane (TPU) flexible structure was printed in the region where the users placed their legs. In addition, fiber Bragg gratings (FBGs), inscribed in low-loss, cyclic, transparent, optical polymer (CYTOP) using the direct-write, plane-by-plane femtosecond laser inscription method, were embedded in the TPU structure. In this case, a 2-FBG array was embedded in two supports for human–robot interaction force assessment at two points on the users’ legs. Both FBG sensors were characterized with respect to temperature and force; additionally, the creep response of the polymer, where temperature influences the force sensitivity, was analyzed. Following the characterization, a compensation method for the creep and temperature influence was derived, showing relative errors below 4.5%. Such errors were lower than the ones obtained with similar sensors in previously published works. The instrumented support was attached to an exoskeleton for knee rehabilitation exercises, where the human–robot interaction forces were measured in flexion and extension cycles.

scholarly journals Sensing small interaction forces through proprioception

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fazlur Rashid ◽  
Devin Burns ◽  
Yun Seong Song
Keyword(s):  
Muscle Contraction ◽  
Interaction Force ◽  
Initial Position ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Physical Interaction ◽  
Robot Interaction ◽  
Interaction Forces ◽  
Force Direction ◽  
Human Motor

AbstractUnderstanding the human motor control strategy during physical interaction tasks is crucial for developing future robots for physical human–robot interaction (pHRI). In physical human–human interaction (pHHI), small interaction forces are known to convey their intent between the partners for effective motor communication. The aim of this work is to investigate what affects the human’s sensitivity to the externally applied interaction forces. The hypothesis is that one way the small interaction forces are sensed is through the movement of the arm and the resulting proprioceptive signals. A pHRI setup was used to provide small interaction forces to the hand of seated participants in one of four directions, while the participants were asked to identify the direction of the push while blindfolded. The result shows that participants’ ability to correctly report the direction of the interaction force was lower with low interaction force as well as with high muscle contraction. The sensitivity to the interaction force direction increased with the radial displacement of the participant’s hand from the initial position: the further they moved the more correct their responses were. It was also observed that the estimated stiffness of the arm varies with the level of muscle contraction and robot interaction force.

scholarly journals Design on the Bowden Cable-Driven Upper Limb Soft Exoskeleton

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Wei ◽  
Zhicheng Qu ◽  
Wei Wang ◽  
Pengcheng Zhang ◽  
Fuchun Hao
Keyword(s):  
Upper Limb ◽  
Force Sensor ◽  
Interaction Force ◽  
Human Interaction ◽  
Motion Simulation ◽  
Skeletal System ◽  
Interaction Forces ◽  
Human Arm ◽  
Bearing Force ◽  
Machine Interaction

To assist hemiplegic patients with the activities of daily life, many upper limb soft exoskeletons have been developed. In this paper, we propose the structure of upper limb soft exoskeleton for rehabilitation training based on human biomechanics. The soft driving structure based on Bowden cable is devised. Man-machine interaction force must be considered because it can damage on the joint and lead to arm discomfort. We focus on structural optimization to minimize man-machine interaction force. Human arm model is established to perform motion simulation in ADAMS. To summarize optimality conditions, the movements of elbow are simulated in ADAMS when the number and location of force bearing points are changed. This paper describes the movement of the shoulder skeletal system through a mathematical model based on the Bowden cable transmission and utilizes man-machine contact force sensor to detect human interaction forces for analysis of experimental data. The experimental results show that man-machine interaction force can be reduced when the number of bearing force points is increased and bearing force point is away from the elbow.

scholarly journals Design and Development of an Upper Limb Rehabilitative Robot with Dual Functionality

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 870
Author(s):  
Md Rasedul Islam ◽  
Md Assad-Uz-Zaman ◽  
Brahim Brahmi ◽  
Yassine Bouteraa ◽  
Inga Wang ◽  
...  
Keyword(s):  
Upper Limb ◽  
Research Work ◽  
Human Robot Interaction ◽  
Control Technique ◽  
Upper Arm ◽  
Interaction Forces ◽  
End Effector ◽  
Upper Limb Rehabilitation ◽  
End Point ◽  
Limb Rehabilitation

The design of an upper limb rehabilitation robot for post-stroke patients is considered a benchmark problem regarding improving functionality and ensuring better human–robot interaction (HRI). Existing upper limb robots perform either joint-based exercises (exoskeleton-type functionality) or end-point exercises (end-effector-type functionality). Patients may need both kinds of exercises, depending on the type, level, and degree of impairments. This work focused on designing and developing a seven-degrees-of-freedom (DoFs) upper-limb rehabilitation exoskeleton called ‘u-Rob’ that functions as both exoskeleton and end-effector types device. Furthermore, HRI can be improved by monitoring the interaction forces between the robot and the wearer. Existing upper limb robots lack the ability to monitor interaction forces during passive rehabilitation exercises; measuring upper arm forces is also absent in the existing devices. This research work aimed to develop an innovative sensorized upper arm cuff to measure the wearer’s interaction forces in the upper arm. A PID control technique was implemented for both joint-based and end-point exercises. The experimental results validated both types of functionality of the developed robot.

scholarly journals An eye-tracking based robotic scrub nurse: proof of concept

2021 ◽  
Author(s):  
Ahmed Ezzat ◽  
Alexandros Kogkas ◽  
Josephine Holt ◽  
Rudrik Thakkar ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Eye Tracking ◽  
Technology Acceptance ◽  
Operating Time ◽  
Human Robot Interaction ◽  
Motion Sensors ◽  
Scrub Nurse ◽  
Task Load ◽  
Significant Difference ◽  
Nasa Tlx ◽  
Robotic Devices

Abstract Background Within surgery, assistive robotic devices (ARD) have reported improved patient outcomes. ARD can offer the surgical team a “third hand” to perform wider tasks and more degrees of motion in comparison with conventional laparoscopy. We test an eye-tracking based robotic scrub nurse (RSN) in a simulated operating room based on a novel real-time framework for theatre-wide 3D gaze localization in a mobile fashion. Methods Surgeons performed segmental resection of pig colon and handsewn end-to-end anastomosis while wearing eye-tracking glasses (ETG) assisted by distributed RGB-D motion sensors. To select instruments, surgeons (ST) fixed their gaze on a screen, initiating the RSN to pick up and transfer the item. Comparison was made between the task with the assistance of a human scrub nurse (HSNt) versus the task with the assistance of robotic and human scrub nurse (R&HSNt). Task load (NASA-TLX), technology acceptance (Van der Laan’s), metric data on performance and team communication were measured. Results Overall, 10 ST participated. NASA-TLX feedback for ST on HSNt vs R&HSNt usage revealed no significant difference in mental, physical or temporal demands and no change in task performance. ST reported significantly higher frustration score with R&HSNt. Van der Laan’s scores showed positive usefulness and satisfaction scores in using the RSN. No significant difference in operating time was observed. Conclusions We report initial findings of our eye-tracking based RSN. This enables mobile, unrestricted hands-free human–robot interaction intra-operatively. Importantly, this platform is deemed non-inferior to HSNt and accepted by ST and HSN test users.

A Quasi-Static Model for Studying Physical Interaction Between a Soft Robotic Digit and a Human Finger

2018 ◽  
Author(s):  
Mahdi Haghshenas-Jaryani ◽  
Muthu B. J. Wijesundara
Keyword(s):  
Human Performance ◽  
Theoretical Models ◽  
Element Model ◽  
Human Robot Interaction ◽  
Physical Interaction ◽  
Robotic Hand ◽  
Robot Interaction ◽  
Interaction Forces ◽  
Comparison Of The Results ◽  
Human Finger

This paper presents the development of a framework based on a quasi-statics concept for modeling and analyzing the physical human-robot interaction in soft robotic hand exoskeletons used for rehabilitation and human performance augmentation. This framework provides both forward and inverse quasi-static formulations for the interaction between a soft robotic digit and a human finger which can be used for the calculation of angular motions, interaction forces, actuation torques, and stiffness at human joints. This is achieved by decoupling the dynamics of the soft robotic digit and the human finger with similar interaction forces acting on both sides. The presented theoretical models were validated by a series of numerical simulations based on a finite element model which replicates similar human-robot interaction. The comparison of the results obtained for the angular motion, interaction forces, and the estimated stiffness at the joints indicates the accuracy and effectiveness of the quasi-static models for predicting the human-robot interaction.

scholarly journals Experimental Evaluation on Haptic Feedback Accuracy by Using Two Self-Made Haptic Devices and One Additional Interface in Robotic Teleoperation

Actuators ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Guan-Yang Liu ◽  
Yi Wang ◽  
Chao Huang ◽  
Chen Guan ◽  
Dong-Tao Ma ◽  
...  
Keyword(s):  
Experimental Evaluation ◽  
Haptic Feedback ◽  
Force Sensor ◽  
Interaction Force ◽  
Haptic Device ◽  
Human Hand ◽  
Haptic Devices ◽  
Input Noise ◽  
Output Force ◽  
Robotic Teleoperation

The goal of haptic feedback in robotic teleoperation is to enable users to accurately feel the interaction force measured at the slave side and precisely understand what is happening in the slave environment. The accuracy of the feedback force describing the error between the actual feedback force felt by a user at the master side and the measured interaction force at the slave side is the key performance indicator for haptic display in robotic teleoperation. In this paper, we evaluate the haptic feedback accuracy in robotic teleoperation via experimental method. A special interface iHandle and two haptic devices, iGrasp-T and iGrasp-R, designed for robotic teleoperation are developed for experimental evaluation. The device iHandle integrates a high-performance force sensor and a micro attitude and heading reference system which can be used to identify human upper limb motor abilities, such as posture maintenance and force application. When a user is asked to grasp the iHandle and maintain a fixed position and posture, the fluctuation value of hand posture is measured to be between 2 and 8 degrees. Based on the experimental results, human hand tremble as input noise sensed by the haptic device is found to be a major reason that results in the noise of output force from haptic device if the spring-damping model is used to render feedback force. Therefore, haptic rendering algorithms should be independent of hand motion information to avoid input noise from human hand to the haptic control loop in teleoperation. Moreover, the iHandle can be fixed at the end effector of haptic devices; iGrasp-T or iGrasp-R, to measure the output force/torque from iGrasp-T or iGrasp-Rand to the user. Experimental results show that the accuracy of the output force from haptic device iGrasp-T is approximately 0.92 N, and using the force sensor in the iHandle can compensate for the output force inaccuracy of device iGrasp-T to 0.1 N. Using a force sensor as the feedback link to form a closed-loop feedback force control system is an effective way to improve the accuracy of feedback force and guarantee high-fidelity of feedback forces at the master side in robotic teleoperation.

scholarly journals A neural network for glomerulus classification based on histological images of kidney biopsy

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Giacomo Donato Cascarano ◽  
Francesco Saverio Debitonto ◽  
Ruggero Lemma ◽  
Antonio Brunetti ◽  
Domenico Buongiorno ◽  
...  
Keyword(s):  
Neural Network ◽  
Clinical Practice ◽  
Periodic Acid ◽  
Digital Pathology ◽  
Feature Reduction ◽  
Training Procedure ◽  
Good Precision ◽  
Periodic Acid Schiff ◽  
Cad Systems ◽  
Cad System

Abstract Background Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. Results We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). Conclusions Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.

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