Training on surgical environments through Mixed and Virtual Reality (HOLOLENS)
Our project consists on the development of a Training Platform for surgical environments by using Mixed and Virtual Reality.
Surgical training environments have special characteristics compared to other training environments. In surgical training, there is a need for a great number of sanitary professionals, monitorization devices and patients to be involved. The consequence is that a single training course has a high cost on resources and a very detailed planification.
With our project it will be possible to obtain important savings thanks to the virtualization of people and devices. Our challenge is the creation of ultrarealistic training environments that, combined with existing physical elements, will provide an important improvement on the training processes that are taking place.
Nuclear medicine uses radioactive isotopes that are administered to patients. It is necessary to follow a very specific radiology protection rules to avoid the expose to radiation.
With an intrahospital localization device it is possible to know the patient´s location on real time inside the Nuclear Medicine Service (doctor´s office, waiting room, exploration room, etc), other hospital floors (for example, the cafeteria) or even if he is not inside the building (as it is not necessary that he stays inside all the time)
An Augmented Reality project with geolocalization has a double purpose. On the one hand, the patient may have all the details of his appointment, the exact localization of the Nuclear Medicine Service and any incident regarding the time in which he will be attended. On the other, nuclear medicine professionals may know the appointed patient’s location in real time since the moment they enter the building.
Nosocomial Bacteria Detection on Surgical Environments
Nosocomial infections are defined as any infection acquired while the patient is hospitalised, and they can appear on the hospitalisation time or once the patient has been released. These infections must be related to the hospitalisation or to the procedures carried out at the hospital.
In the experiment we want to digitalise all the measurable factors that influence on the monitorisation of the optimal and necessary environmental conditions on surgical settings and recovery rooms.
All the data will be collected and processed on the cloud by means of a personalised algorism that will provide a predictive model of each area to be analysed. This way it will be possible to know the probability of infection and the specific pathogen affecting each area, and it also will allow doctors to know in an easy and precise way which antibiotic best fits each patient. We hope this will help to diminish the resistance to antibiotics and have a better control and knowledge of the studied areas.