Objective: The Development of a Novel Mixed Reality (MR) Simulation. An evolving training environment emphasizes the importance of simulation. Current haptic temporal bone simulators have difficulty representing realistic contact forces and while 3D printed models convincingly represent vibrational properties of bone, they cannot reproduce soft tissue. This paper introduces a mixed reality model, where the effective elements of both simulations are combined; haptic rendering of soft tissue directly interacts with a printed bone model. This paper addresses one aspect in a series of challenges, specifically the mechanical merger of a haptic device with an otic drill. This further necessitates gravity cancelation of the work assembly gripper mechanism. In this system, the haptic end-effector is replaced by a high-speed drill and the virtual contact forces need to be repositioned to the drill tip from the mid wand. Previous publications detail generation of both the requisite printed and haptic simulations.
Method: Custom software was developed to reposition the haptic interaction point to the drill tip. A custom fitting, to hold the otic drill, was developed and its weight was offset using the haptic device. The robustness of the system to disturbances and its stable performance during drilling were tested. The experiments were performed on a mixed reality model consisting of two drillable rapid-prototyped layers separated by a free-space. Within the free-space, a linear virtual force model is applied to simulate drill contact with soft tissue.
Results: Testing illustrated the effectiveness of gravity cancellation. Additionally, the system exhibited excellent performance given random inputs and during the drill's passage between real and virtual components of the model. No issues with registration at model boundaries were encountered.
Conclusion: These tests provide a proof of concept for the initial stages in the development of a novel mixed-reality temporal bone simulator.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4746989 | PMC |
http://dx.doi.org/10.1186/s40463-014-0023-9 | DOI Listing |
Health Justice
January 2025
Burnet Institute, Melbourne, Australia.
Background: During the COVID-19 pandemic, governments worldwide introduced law enforcement measures to deter and punish breaches of emergency public health orders. For example, in Victoria, Australia, discretionary fines of A$1,652 were issued for breaching stay-at-home orders, and A$4,957 fines for 'unlawful gatherings'; to date, approximately 30,000 fines remain outstanding or not paid in full. Studies globally have revealed how the expansion of policing powers produced significant collateral damage for marginalized populations, including people from low-income neighboorhoods, Indigenous Peoples, sex workers, and people from culturally diverse backgrounds.
View Article and Find Full Text PDFJ Exp Psychol Hum Percept Perform
January 2025
Department of Experimental Clinical and Health Psychology, Ghent University.
Motivational theories of imitation state that we imitate because this led to positive social consequences in the past. Because movement imitation typically only leads to these consequences when perceived by the imitated person, it should increase when the interaction partner sees the imitator. Current evidence for this hypothesis is mixed, potentially due to the low ecological validity in previous studies.
View Article and Find Full Text PDFRev Med Suisse
January 2025
Service de neurologie, Clinique bernoise Montana, 3963 Crans-Montana.
Parkinson's disease affects around 6 million people worldwide. It causes both motor and non-motor symptoms. Since there is no cure, medical treatment aims to improve patients' quality of life.
View Article and Find Full Text PDFFront Psychol
January 2025
Department of Psychology, Università degli Studi di Torino, Turin, Italy.
J Bone Oncol
February 2025
School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou, 362001, China.
Objective: Segmenting and reconstructing 3D models of bone tumors from 2D image data is of great significance for assisting disease diagnosis and treatment. However, due to the low distinguishability of tumors and surrounding tissues in images, existing methods lack accuracy and stability. This study proposes a U-Net model based on double dimensionality reduction and channel attention gating mechanism, namely the DCU-Net model for oncological image segmentation.
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