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Allemang-Trivalle A, Donjat J, Bechu G, Coppin G, Chollet M, Klaproth OW, Mitschke A, Schirrmann A, Cao CGL. Modeling Fatigue in Manual and Robot-Assisted Work for Operator 5.0. IISE Trans Occup Ergon Hum Factors 2024; 12:135-147. [PMID: 38441578 DOI: 10.1080/24725838.2024.2321460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 02/17/2024] [Indexed: 05/08/2024]
Affiliation(s)
- Arnaud Allemang-Trivalle
- IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest, France
- Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany
| | | | - Gaëlic Bechu
- IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest, France
| | - Gilles Coppin
- IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest, France
| | | | | | | | | | - Caroline G L Cao
- IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest, France
- Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
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Mavridis IN, Lo WB, Wimalachandra WSB, Philip S, Agrawal S, Scott C, Martin-Lamb D, Carr B, Bill P, Lawley A, Seri S, Walsh AR. Pediatric stereo-electroencephalography: effects of robot assistance and other variables on seizure outcome and complications. J Neurosurg Pediatr 2021; 28:404-415. [PMID: 34298516 DOI: 10.3171/2021.2.peds20810] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/19/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The safety of stereo-electroencephalography (SEEG) has been investigated; however, most studies have not differentiated pediatric and adult populations, which have different anatomy and physiology. The purpose of this study was to assess SEEG safety in the pediatric setting, focusing on surgical complications and the identification of patient and surgical risk factors, if any. The authors also aimed to determine whether robot assistance in SEEG was associated with a change in practice, surgical parameters, and clinical outcomes. METHODS The authors retrospectively studied all SEEG cases performed in their department from December 2014 to March 2020. They analyzed both demographic and surgical variables and noted the types of surgery-related complications and their management. They also studied the clinical outcomes of a subset of the patients in relation to robot-assisted and non-robot-assisted SEEG. RESULTS Sixty-three children had undergone 64 SEEG procedures. Girls were on average 3 years younger than the boys (mean age 11.1 vs 14.1 years, p < 0.01). The overall complication rate was 6.3%, and the complication rate for patients with left-sided electrodes was higher than that for patients with right-sided electrodes (11.1% vs 3.3%), although the difference between the two groups was not statistically significant. The duration of recording was positively correlated to the number of implanted electrodes (r = 0.296, p < 0.05). Robot assistance was associated with a higher number of implanted electrodes (mean 12.6 vs 7.6 electrodes, p < 0.0001). Robot-assisted implantations were more accurate, with a mean error of 1.51 mm at the target compared to 2.98 mm in nonrobot implantations (p < 0.001). Clinical outcomes were assessed in the first 32 patients treated (16 in the nonrobot group and 16 in the robot group), 23 of whom proceeded to further resective surgery. The children who had undergone robot-assisted SEEG had better eventual seizure control following subsequent epilepsy surgery. Of the children who had undergone resective epilepsy surgery, 42% (5/12) in the nonrobot group and 82% (9/11) in the robot group obtained an Engel class IA outcome at 1 year (χ2 = 3.885, p = 0.049). Based on Kaplan-Meier survival analysis, the robot group had a higher seizure-free rate than the nonrobot group at 30 months postoperation (7/11 vs 2/12, p = 0.063). Two complications, whose causes were attributed to the implantation and head-bandaging steps, required surgical intervention. All complications were either transient or reversible. CONCLUSIONS This is the largest single-center, exclusively pediatric SEEG series that includes robot assistance so far. SEEG complications are uncommon and usually transient or treatable. Robot assistance enabled implantation of more electrodes and improved epilepsy surgery outcomes, as compared to those in the non-robot-assisted cases.
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Affiliation(s)
| | | | | | | | | | - Caroline Scott
- 3Neurophysiology, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Darren Martin-Lamb
- 3Neurophysiology, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Bryony Carr
- 3Neurophysiology, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Peter Bill
- 3Neurophysiology, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Andrew Lawley
- 3Neurophysiology, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Stefano Seri
- 3Neurophysiology, Birmingham Children's Hospital, Birmingham, United Kingdom
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Abstract
Image guidance is a common methodology of minimally invasive procedures. Depending on the type of intervention, various imaging modalities are available. Common imaging modalities are computed tomography, magnetic resonance tomography, and ultrasound. Robotic systems have been developed to enable and improve the procedures using these imaging techniques. Spatial and technological constraints limit the development of versatile robotic systems. This paper offers a brief overview of the developments of robotic systems for image-guided interventions since 2015 and includes samples of our current research in this field.
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Affiliation(s)
- Michael Unger
- Innovation Center Computer Assisted Surgery, Leipzig, Germany
| | - Johann Berger
- Innovation Center Computer Assisted Surgery, Leipzig, Germany
| | - Andreas Melzer
- Innovation Center Computer Assisted Surgery, Leipzig, Germany.,Institute for Medical Science and Technology, IMSaT, University Dundee, Dundee, United Kingdom
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He XF, Xiao YY, Zhang X, Zhang XB, Zhang X, Wei YT, Zhang ZL, Wiggermann P. Preliminary clinical application of the robot-assisted CT-guided irreversible electroporation ablation for the treatment of pancreatic head carcinoma. Int J Med Robot 2020; 16:e2099. [PMID: 32112493 DOI: 10.1002/rcs.2099] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 01/08/2020] [Accepted: 02/23/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND To evaluate the feasibility and safety of a robot-guided irreversible electroporation (IRE) ablation system for the treatment of pancreatic head carcinoma. METHODS A total of 20 cases with pancreatic head carcinoma were divided into two groups: 11 cases in group A with manual probe placement and 9 cases in group B with robotic navigated probe placement. The two groups were compared in terms of planning time before puncture, puncture time, the total time of electrode deployment, number of scans, and punctual accuracy of the single electrode. RESULTS Each probe was successfully punctured, and no complications were detected. P-values were calculated for all the parameters, using the SPSS 25.0 software and the t test. CONCLUSIONS The new robot can reduce the total operating time as compared to the manual probe placement with the same accuracy in the IRE of pancreatic head carcinoma.
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Affiliation(s)
- Xiao F He
- Department of Diagnostic Radiology, Medical School of Chinese PLA, Beijing, China
| | - Yue Y Xiao
- Department of Diagnostic Radiology, Medical School of Chinese PLA, Beijing, China
| | - Xiao Zhang
- Department of Diagnostic Radiology, Medical School of Chinese PLA, Beijing, China
| | - Xiao B Zhang
- Department of Diagnostic Radiology, Medical School of Chinese PLA, Beijing, China
| | - Xin Zhang
- Department of Diagnostic Radiology, Medical School of Chinese PLA, Beijing, China
| | - Ying T Wei
- Department of Diagnostic Radiology, Medical School of Chinese PLA, Beijing, China
| | - Zhong L Zhang
- Department of Diagnostic Radiology, Medical School of Chinese PLA, Beijing, China
| | - Philipp Wiggermann
- Chefarzt des Instituts für Röntgendiagnostik u. Nuklearmedizin Städtisches Klinikum Braunschweig gGmbH, Braunschweig, Germany
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Jiang B, Pennington Z, Zhu A, Matsoukas S, Ahmed AK, Ehresman J, Mahapatra S, Cottrill E, Sheppell H, Manbachi A, Crawford N, Theodore N. Three-dimensional assessment of robot-assisted pedicle screw placement accuracy and instrumentation reliability based on a preplanned trajectory. J Neurosurg Spine 2020; 33:519-528. [PMID: 32470927 DOI: 10.3171/2020.3.spine20208] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 03/30/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Robotic spine surgery systems are increasingly used in the US market. As this technology gains traction, however, it is necessary to identify mechanisms that assess its effectiveness and allow for its continued improvement. One such mechanism is the development of a new 3D grading system that can serve as the foundation for error-based learning in robot systems. Herein the authors attempted 1) to define a system of providing accuracy data along all three pedicle screw placement axes, that is, cephalocaudal, mediolateral, and screw long axes; and 2) to use the grading system to evaluate the mean accuracy of thoracolumbar pedicle screws placed using a single commercially available robotic system. METHODS The authors retrospectively reviewed a prospectively maintained, IRB-approved database of patients at a single tertiary care center who had undergone instrumented fusion of the thoracic or lumbosacral spine using robotic assistance. Patients with preoperatively planned screw trajectories and postoperative CT studies were included in the final analysis. Screw accuracy was measured as the net deviation of the planned trajectory from the actual screw trajectory in the mediolateral, cephalocaudal, and screw long axes. RESULTS The authors identified 47 patients, 51% male, whose pedicles had been instrumented with a total of 254 screws (63 thoracic, 191 lumbosacral). The patients had a mean age of 61.1 years and a mean BMI of 30.0 kg/m2. The mean screw tip accuracies were 1.3 ± 1.3 mm, 1.2 ± 1.1 mm, and 2.6 ± 2.2 mm in the mediolateral, cephalocaudal, and screw long axes, respectively, for a net linear deviation of 3.6 ± 2.3 mm and net angular deviation of 3.6° ± 2.8°. According to the Gertzbein-Robbins grading system, 184 screws (72%) were classified as grade A and 70 screws (28%) as grade B. Placement of 100% of the screws was clinically acceptable. CONCLUSIONS The accuracy of the discussed robotic spine system is similar to that described for other surgical systems. Additionally, the authors outline a new method of grading screw placement accuracy that measures deviation in all three relevant axes. This grading system could provide the error signal necessary for unsupervised machine learning by robotic systems, which would in turn support continued improvement in instrumentation placement accuracy.
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Affiliation(s)
- Bowen Jiang
- 1Department of Neurosurgery, Johns Hopkins School of Medicine
| | - Zach Pennington
- 1Department of Neurosurgery, Johns Hopkins School of Medicine
| | - Alex Zhu
- 1Department of Neurosurgery, Johns Hopkins School of Medicine
| | - Stavros Matsoukas
- 2Aristotle University of Thessaloniki School of Medicine, Thessaloniki, Greece; and
| | - A Karim Ahmed
- 1Department of Neurosurgery, Johns Hopkins School of Medicine
| | - Jeff Ehresman
- 1Department of Neurosurgery, Johns Hopkins School of Medicine
| | - Smruti Mahapatra
- 3Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Ethan Cottrill
- 1Department of Neurosurgery, Johns Hopkins School of Medicine
| | - Hailey Sheppell
- 3Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Amir Manbachi
- 3Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
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Petrič T, Peternel L, Morimoto J, Babič J. Assistive Arm-Exoskeleton Control Based on Human Muscular Manipulability. Front Neurorobot 2019; 13:30. [PMID: 31191289 PMCID: PMC6548979 DOI: 10.3389/fnbot.2019.00030] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 05/08/2019] [Indexed: 11/13/2022] Open
Abstract
This paper introduces a novel control framework for an arm exoskeleton that takes into account force of the human arm. In contrast to the conventional exoskeleton controllers where the assistance is provided without considering the human arm biomechanical force manipulability properties, we propose a control approach based on the arm muscular manipulability. The proposed control framework essentially reshapes the anisotropic force manipulability into the endpoint force manipulability that is invariant with respect to the direction in the entire workspace of the arm. This allows users of the exoskeleton to perform tasks effectively in the whole range of the workspace, even in areas that are normally unsuitable due to the low force manipulability of the human arm. We evaluated the proposed control framework with real robot experiments where subjects wearing an arm exoskeleton were asked to move a weight between several locations. The results show that the proposed control framework does not affect the normal movement behavior of the users while effectively reduces user effort in the area of low manipulability. Particularly, the proposed approach augments the human arm force manipulability to execute tasks equally well in the entire workspace of the arm.
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Affiliation(s)
- Tadej Petrič
- Laboratory for Neuromechanics and Biorobotics, Department for Automatics, Biocybernetics and Robotics, Jožef Stean Institute, Ljubljana, Slovenia
| | - Luka Peternel
- Department of Cognitive Robotics, Delft University of Technology, Delft, Netherlands
| | - Jun Morimoto
- Department of Brain-Robot Interface, ATR Computational Neuroscience Labs, Kyoto, Japan
| | - Jan Babič
- Laboratory for Neuromechanics and Biorobotics, Department for Automatics, Biocybernetics and Robotics, Jožef Stean Institute, Ljubljana, Slovenia
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Wilson G, Pereyda C, Raghunath N, de la Cruz G, Goel S, Nesaei S, Minor B, Schmitter-Edgecombe M, Taylor ME, Cook DJ. Robot-Enabled Support of Daily Activities in Smart Home Environments. COGN SYST RES 2019; 54:258-272. [PMID: 31565029 PMCID: PMC6764768 DOI: 10.1016/j.cogsys.2018.10.032] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Smart environments offer valuable technologies for activity monitoring and health assessment. Here, we describe an integration of robots into smart environments to provide more interactive support of individuals with functional limitations. RAS, our Robot Activity Support system, partners smart environment sensing, object detection and mapping, and robot interaction to detect and assist with activity errors that may occur in everyday settings. We describe the components of the RAS system and demonstrate its use in a smart home testbed. To evaluate the usability of RAS, we also collected and analyzed feedback from participants who received assistance from RAS in a smart home setting as they performed routine activities.
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Abstract
To successfully deploy a robot into a healthcare setting, it must be accepted by the end users. This study explored healthcare providers' perceptions of a mobile manipulator class personal robot assisting with caregiving tasks for older adult patients. Participants were 14 healthcare providers with an average of 12 years of continuous work experience with older patients. Quantitative and qualitative methods were used. Participants indicated a willingness to use a mobile manipulator robot as an assistant, yet they expressed discretion in their acceptance for different tasks. Benefits of robot assistance noted by participants included saving time, being accurate when conducting medical tasks, and enabling them to be more productive. Participants expressed concern about robots being unreliable, hazardous to patients, and inappropriate for performing some tasks (e.g., those that involve close patient contact). These findings provide insights into healthcare providers' attitudes and preferences for assistance from a mobile manipulator robot.
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Affiliation(s)
- Tracy L. Mitzner
- Center for Assistive Technology and Environmental Access (CATEA), Georgia Institute of Technology, Atlanta, Georgia, United States
| | | | - Charles C. Kemp
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States
| | - Wendy A. Rogers
- Department of Kinesiology & Community Health, University of Illinois Urbana-Champaign, Champaign, IL, United States
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De Santis D, Zenzeri J, Casadio M, Masia L, Riva A, Morasso P, Squeri V. Robot-assisted training of the kinesthetic sense: enhancing proprioception after stroke. Front Hum Neurosci 2015; 8:1037. [PMID: 25601833 PMCID: PMC4283673 DOI: 10.3389/fnhum.2014.01037] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Accepted: 12/10/2014] [Indexed: 11/13/2022] Open
Abstract
Proprioception has a crucial role in promoting or hindering motor learning. In particular, an intact position sense strongly correlates with the chances of recovery after stroke. A great majority of neurological patients present both motor dysfunctions and impairments in kinesthesia, but traditional robot and virtual reality training techniques focus either in recovering motor functions or in assessing proprioceptive deficits. An open challenge is to implement effective and reliable tests and training protocols for proprioception that go beyond the mere position sense evaluation and exploit the intrinsic bidirectionality of the kinesthetic sense, which refers to both sense of position and sense of movement. Modulated haptic interaction has a leading role in promoting sensorimotor integration, and it is a natural way to enhance volitional effort. Therefore, we designed a preliminary clinical study to test a new proprioception-based motor training technique for augmenting kinesthetic awareness via haptic feedback. The feedback was provided by a robotic manipulandum and the test involved seven chronic hemiparetic subjects over 3 weeks. The protocol included evaluation sessions that consisted of a psychometric estimate of the subject's kinesthetic sensation, and training sessions, in which the subject executed planar reaching movements in the absence of vision and under a minimally assistive haptic guidance made by sequences of graded force pulses. The bidirectional haptic interaction between the subject and the robot was optimally adapted to each participant in order to achieve a uniform task difficulty over the workspace. All the subjects consistently improved in the perceptual scores as a consequence of training. Moreover, they could minimize the level of haptic guidance in time. Results suggest that the proposed method is effective in enhancing kinesthetic acuity, but the level of impairment may affect the ability of subjects to retain their improvement in time.
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Affiliation(s)
- Dalia De Santis
- Motor Learning and Robotic Rehabilitation Laboratory, Department of Robotics, Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia , Genova , Italy
| | - Jacopo Zenzeri
- Motor Learning and Robotic Rehabilitation Laboratory, Department of Robotics, Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia , Genova , Italy
| | - Maura Casadio
- Motor Learning and Robotic Rehabilitation Laboratory, Department of Robotics, Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia , Genova , Italy ; NeuroLab, Department of Informatics, Bioengineering, Robotics and Systems (DIBRIS), University of Genova , Genova , Italy
| | - Lorenzo Masia
- Motor Learning and Robotic Rehabilitation Laboratory, Department of Robotics, Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia , Genova , Italy ; Assistive Robotics and Interactive Ergonomic Systems Laboratory, Division of Mechatronics and Design, Robotic Research Center, School of Mechanical and Aerospace Engineering (MAE), Nanyang Technological University (NTU) , Singapore
| | - Assunta Riva
- SI4LIFE - Innovation Hub for Elderly and Disabled People , Genova , Italy
| | - Pietro Morasso
- Motor Learning and Robotic Rehabilitation Laboratory, Department of Robotics, Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia , Genova , Italy ; NeuroLab, Department of Informatics, Bioengineering, Robotics and Systems (DIBRIS), University of Genova , Genova , Italy
| | - Valentina Squeri
- Motor Learning and Robotic Rehabilitation Laboratory, Department of Robotics, Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia , Genova , Italy
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