Haptic User Interfaces and Practice-based Learning for Minimally Invasive Surgical Training
March 08, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Felix G. Hamza-Lup, Adrian Seitan, Costin Petre, Mihai Polceanu, Crenguta M. Bogdan, Dorin M. Popovici
arXiv ID
1903.04882
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.RO
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Recent advances in haptic hardware and software technology have generated interest in novel, multimodal interfaces based on the sense of touch. Such interfaces have the potential to revolutionize the way we think about human computer interaction and open new possibilities for simulation and training in a variety of fields. In this paper we review several frameworks, APIs and toolkits for haptic user interface development. We explore these software components focusing on minimally invasive surgical simulation systems. In the area of medical diagnosis, there is a strong need to determine mechanical properties of biological tissue for both histological and pathological considerations. Therefore we focus on the development of affordable visuo-haptic simulators to improve practice-based education in this area. We envision such systems, designed for the next generations of learners that enhance their knowledge in connection with real-life situations while they train in mandatory safety conditions.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted