Teaching the user by learning from the user: personalizing movement control in physical human-robot interaction

Safavi, Ali Akbar; Zadeh, Mehrdad Hosseini

doi:10.1109/jas.2017.7510634

Cited by 7 publications

(4 citation statements)

References 34 publications

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“…In fact, the simulation strives to imitate real operation rooms as much as possible because it leads expert surgeons to function naturally, thereby capturing more meaningful data. It is good to say that the aforementioned setup in conjunction with the simulation has contributed in other studies in our research Lab at Kettering University (Safavi and Zadeh, 2017;Zahedi et al, 2017Zahedi et al, , 2019.…”

Section: Simulation and Experimental Setup For Bone Drilling Surgerymentioning

confidence: 97%

“…Statistical algorithms such as HMM and CRF have been used to extract the aforementioned features (Reiley and Hager, 2009 ; Ramón Medina et al, 2012 ; Tao et al, 2013 ; Zahedi et al, 2017 , 2019 ). In addition, control algorithms have been applied to the human-in-the-loop system for guiding human via robots based on the model obtained from dynamic data (Chipalkatty et al, 2011 ; Safavi and Zadeh, 2015 , 2017 ; Safavi et al, 2015 ). However, previous works have an impressive progress in modeling the dynamic data in the skill transfer system, advent of machine learning and deep learning algorithms is thought as a gigantic step toward developing more trustworthy predictive systems.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

2021

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Having a trusted and useful system that helps to diminish the risk of medical errors and facilitate the improvement of quality in the medical education is indispensable. Thousands of surgical errors are occurred annually with high adverse event rate, despite inordinate number of devised patients safety initiatives. Inadvertently or otherwise, surgeons play a critical role in the aforementioned errors. Training surgeons is one of the most crucial and delicate parts of medical education and needs more attention due to its practical intrinsic. In contrast to engineering, dealing with mortal alive creatures provides a minuscule chance of trial and error for trainees. Training in operative rooms, on the other hand, is extremely expensive in terms of not only equipment but also hiring professional trainers. In addition, the COVID-19 pandemic has caused to establish initiatives such as social distancing in order to mitigate the rate of outbreak. This leads surgeons to postpone some non-urgent surgeries or operate with restrictions in terms of safety. Subsequently, educational systems are affected by the limitations due to the pandemic. Skill transfer systems in cooperation with a virtual training environment is thought as a solution to address aforesaid issues. This enables not only novice surgeons to enrich their proficiency but also helps expert surgeons to be supervised during the operation. This paper focuses on devising a solution based on deep leaning algorithms to model the behavior of experts during the operation. In other words, the proposed solution is a skill transfer method that learns professional demonstrations using different effective factors from the body of experts. The trained model then provides a real-time haptic guidance signal for either instructing trainees or supervising expert surgeons. A simulation is utilized to emulate an operating room for femur drilling surgery, which is a common invasive treatment for osteoporosis. This helps us with both collecting the essential data and assessing the obtained models. Experimental results show that the proposed method is capable of emitting guidance force haptic signal with an acceptable error rate.

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Section: Simulation and Experimental Setup For Bone Drilling Surgerymentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

2021

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“…In this spirit, Chipalkatty et al proposes an MPC formulation that optimized inputs to a robotic system such that user intent is preserved while enforcing state constraints of the low-level robotic task (Chipalkatty, Droge, & Egerstedt, 2013;Chipalkatty & Egerstedt, 2010). In another work, model predictive control was used to optimize the rendered forces of a robotic system in physical human-robot interactions by predicting the performance of the user (Safavi & Zadeh, 2017). Similarly, Jorgensen et al, 2017 used model predictive control with mixed integer constraints to generate human-aware control policies.…”

Section: Model Predictive Control For Adaptive Systemsmentioning

confidence: 99%

Optimal Control to Support High-Level User Goals in Human-Computer Interaction

Gebhardt

Hilliges

2021

Human–Computer Interaction Series

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“…The tracking system includes robot manipulator, PC, STM32 processor, touch screen device. The upper monitor is used to accept and judge the check command, achieving communication with the robot manipulator in the VS to obtain the real-time coordinates data [10].Then, through the coordinate mapping, the data is displayed in coordinate curve in real time.…”

Section: Robot Manipulator Systemmentioning

confidence: 99%

Implementing Multi-DOF Trajectory Tracking Control System for Robotic Arm Experimental Platform

Yang²,

Li³

et al. 2018

2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

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To implement the control system of a multi-DOF robotic manipulator (Dobot), the robot dynamics, trajectory planning algorithm and motion control strategy are studied for designing the trajectory tracking control system. In this paper, the hardware and software of Dobot magician control system are designed. The hardware mainly includes STM32 controller. The software part mainly builds the host computer display interface, completes the protocol communication between the robot manipulator and the PC, so as to realize the trajectory tracking control of the robot manipulator and implement the trackfollowing in real time. The experimental results show that the control system can accurately track the trajectory of robotic manipulator with a certain degree of real-time and stability.

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Teaching the user by learning from the user: personalizing movement control in physical human-robot interaction

Cited by 7 publications

References 34 publications

Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

Optimal Control to Support High-Level User Goals in Human-Computer Interaction

Implementing Multi-DOF Trajectory Tracking Control System for Robotic Arm Experimental Platform

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