Gesture-Based Adaptive Haptic Guidance: A Comparison of Discriminative and Generative Modeling Approaches

Zahedi, Ehsan; Dargahi, Javad; Kia, Michael; Zadeh, Mehrdad Hosseini

doi:10.1109/lra.2017.2660071

Cited by 5 publications

(9 citation statements)

References 29 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: 98%

“…These behaviors are categorized into kinesthetic and kinematic demonstrations. Learning kinematic demonstrations is chiefly regarded as the process of extracting positional body movements of the expert during the operation (Abbott et al, 2007 ; Chipalkatty et al, 2011 ; Zahedi et al, 2017 ). Kinesthetic information can be obtained by a physical interaction between robots and users so that a surgeon directly works with the robot to perform a specified task (Rozo et al, 2013 , 2016 ; Kronander and Billard, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

“…Since the data related to both kinematic and kinesthetic demonstrations is time varying and also learning the performance of experts depends on modeling the dynamic behaviors over time, it is crucial to investigate the data with respect to the temporal characteristic. 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 ).…”

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: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

2021

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“…Robotic devices enabled the delivery of haptic feedback generated by the virtual fixture or artificial force field techniques to users, allowing them to perform the tasks with improved stability [4,28]. Over the past few decades, the implementation of HG has made significant progress, and it has been embedded in a variety of forms, including haptic devices [5,10,38,55], sleeve devices [8,16], pen devices [25], and steering wheels [9,21,35,50,53]. Accordingly, the range of HG applications has also expanded, such as in surgical assistance [20,41,55], driving assistance [21,35,50,53], teleoperation of robots [38,47] and UAVs [28,49], and desktop computer interfaces [11,26].…”

Section: Related Work 21 Haptic Guidancementioning

confidence: 99%

“…For example, in a steering task, it guides the users to their individually preferred courses rather than the center of the road [10]. Both types of HG systems have been proven to have several positive effects in terms of task performance improvement [5,10,15,19,27,35,55], user workload reduction [19,27,35], and user subjective satisfaction [15,19,27] in various recent studies.…”

Section: Introductionmentioning

confidence: 99%

Optimal Action-based or User Prediction-based Haptic Guidance: Can You Do Even Better?

Moon

Seo

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Figure 1: Overview of this research. (a) We built a virtual air hockey task environment controlled by a haptic device. Haptic guidance that assists a user's task performance is delivered through this device. (b) We implemented optimal action-based and user prediction-based haptic guidance using deep learning-based approaches, proposed a combined haptic guidance for better performance, and experimentally compared them.

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