Robotic platforms are taking their place in the operating room because they provide more stability and accuracy during surgery. Although most of these platforms are teleoperated, a lot of research is currently being carried out to design collaborative platforms. The objective is to reduce the surgeon workload through the automation of secondary or auxiliary tasks, which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time. One of the most important secondary tasks is the endoscopic camera guidance, whose automation would allow the surgeon to be concentrated on handling the surgical instruments. This paper proposes a novel autonomous camera guidance approach for laparoscopic surgery. It is based on learning from demonstration (LfD), which has demonstrated its feasibility to transfer knowledge from humans to robots by means of multiple expert showings. The proposed approach has been validated using an experimental surgical robotic platform to perform peg transferring, a typical task that is used to train human skills in laparoscopic surgery. The results show that camera guidance can be easily trained by a surgeon for a particular task. Later, it can be autonomously reproduced in a similar way to one carried out by a human. Therefore, the results demonstrate that the use of learning from demonstration is a suitable method to perform autonomous camera guidance in collaborative surgical robotic platforms.

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New wireless technologies make possible the implementation of high level integration wireless devices which allow the replacement of traditional large wired monitoring devices. This kind of devices favours at-home hospitalization, reducing the affluence to sanitary assistance centers to make routine controls. This fact causes a really favourable social impact, especially for elder people, rural-zone inhabitant, chronic patients and handicapped people. Furthermore, it offers new functionalities to physicians and will reduce the sanitary cost. Among these functionalities, biomedical signals can be sent to other devices (screen, PDA, PC...) or processing centers, without restricting the patients' mobility. The aim of this project is the development and implementation of a reduced size multi-channel electrocardiograph based on IEEE 802.11, which allows wireless monitoring of patients, and the insertion of the information into the TCP/IP Hospital network.

Minimally invasive surgery (MIS) techniques are growing in quantity and complexity to cover a wider range of interventions. More specifically, hand-assisted laparoscopic surgery (HALS) involves the use of one surgeon’s hand inside the patient whereas the other one manages a single laparoscopic tool. In this scenario, those surgical procedures performed with an additional tool require the aid of an assistant. Furthermore, in the case of a human–robot assistant pairing a fluid communication is mandatory. This human–machine interaction must combine both explicit orders and implicit information from the surgical gestures. In this context, this paper focuses on the development of a hand gesture recognition system for HALS. The recognition is based on a hidden Markov model (HMM) algorithm with an improved automated training step, which can also learn during the online surgical procedure by means of a reinforcement learning process.

Most of the patients who are in hospitals and, increasingly, patients controlled remotely from their homes, at-home monitoring, are continuously monitored in order to control their evolution. The medical devices used up to now, force the sanitary staff to go to the patients' room to control the biosignals that are being monitored, although in many cases, patients are in perfect conditions. If patient is at home, it is he or she who has to go to the hospital to take the record of the monitored signal. New wireless technologies, such as BlueTooth and WLAN, make possible the deployment of systems that allow the display and storage of those signals in any place where the hospital intranet is accessible. In that way, unnecessary displacements are avoided. This paper presents a network architecture that allows the identification of the biosignal acquisition device as IP network nodes. The system is based on a TCP/IP architecture which is scalable and avoids the deployment of a specific purpose network.