Soft robotic devices have significant potential for medical device applications that warrant safe synergistic interaction with humans. This article describes the optimization of an implantable soft robotic system for heart failure whereby soft actuators wrapped around the ventricles are programmed to contract and relax in synchrony with the beating heart. Elastic elements integrated in to the soft actuators provide recoiling function so as to aid refilling during the diastolic phase of the cardiac cycle. Improved synchronization with the biological system is achieved by incorporating the native ventricular pressure in to the control system to trigger assistance and synchronize the device with the heart. A three-state electro-pneumatic valve configuration allows the actuators to contract at different rates to vary contraction patterns. An in vivo study was performed to test three hypotheses relating to mechanical coupling and temporal synchronization of the actuators and heart. First, that adhesion of the actuators to the ventricles improves cardiac output. Second, that there is a contraction–relaxation ratio of the actuators which generates optimal cardiac output. Third, that the rate of actuator contraction is a factor in cardiac output.

A soft robotic device assists blood flow in isolated left or right heart failure by applying rhythmic loading to either ventricle.

Purpose: Ocular surgery, ear, nose and throat surgery and neurosurgery are typical types of microsurgery. A versatile training platform can assist microsurgical skills development and accelerate the uptake of robot-assisted microsurgery (RAMS). However, the currently available platforms are mainly designed for macro-scale minimally invasive surgery. There is a need to develop a dedicated microsurgical robot research platform for both research and clinical training. Methods: A microsurgical robot research platform (MRRP) is introduced in this paper. The hardware system includes a slave robot with bimanual manipulators, two master controllers and a vision system. It is flexible to support multiple microsurgical tools. The software architecture is developed based on the robot operating system, which is extensible at high-level control. The selection of master–slave mapping strategy was explored, while comparisons were made between different interfaces. Results: Experimental verification was conducted based on two microsurgical tasks for training evaluation, i.e. trajectory following and targeting. User study results indicated that the proposed hybrid interface is more effective than the traditional approach in terms of frequency of clutching, task completion time and ease of control. Conclusion: Results indicated that the MRRP can be utilized for microsurgical skills training, since motion kinematic data and vision data can provide objective means of verification and scoring. The proposed system can further be used for verifying high-level control algorithms and task automation for RAMS research.

The increasingly ageing population and the tendency to live alone have led science and engineering researchers to search for health care solutions. In the COVID 19 pandemic, the elderly have been seriously affected in addition to suffering from isolation and its associated and psychological consequences. This paper provides an overview of the RobWell (Robotic-based Well-Being Monitoring and Coaching System for the Elderly in their Daily Activities) system. It is a system focused on the field of artificial intelligence for mood prediction and coaching. This paper presents a general overview of the initially proposed system as well as the preliminary results related to the home automation subsystem, autonomous robot navigation and mood estimation through machine learning prior to the final system integration, which will be discussed in future works. The main goal is to improve their mental well-being during their daily household activities. The system is composed of ambient intelligence with intelligent sensors, actuators and a robotic platform that interacts with the user. A test smart home system was set up in which the sensors, actuators and robotic platform were integrated and tested. For artificial intelligence applied to mood prediction, we used machine learning to classify several physiological signals into different moods. In robotics, it was concluded that the ROS autonomous navigation stack and its autodocking algorithm were not reliable enough for this task, while the robot’s autonomy was sufficient. Semantic navigation, artificial intelligence and computer vision alternatives are being sought.

Left ventricular failure is strongly associated with secondary mitral valve regurgitation. Implantable soft robotic devices are an emerging technology that enables augmentation of a native function of a target tissue. We demonstrate the ability of a novel soft robotic ventricular assist device to dynamically augment left ventricular contraction, provide native pulsatile flow, simultaneously reshape the mitral valve apparatus, and eliminate the associated regurgitation in an Short-term large animal model of acute left ventricular systolic dysfunction.

In this paper, we present a visualization system to provide assistance in cochlear implant surgery which can be seamlessly integrated within the devices that are currently used in surgery. The system is intended to improve tool alignment in positioning and during insertion, with the aim of reducing the problems encountered during perimodiolar electrode array insertion. Our system is composed of a semi-autonomous hand-held surgical tool, coupled with an optical tracker to monitor the tool position and a operating microscope. The microscope live view is overlaid with guidance information in the form of augmented reality to assist the surgeon in positioning the surgical tool and maintain that position during insertion. Our approach shows promising results in tool alignment, which are comparable to the state of the art.