Previous soft robotic ventricular assist devices have generally targeted biventricular heart failure and have not engaged the interventricular septum that plays a critical role in blood ejection from the ventricle. We propose implantable soft robotic devices to augment cardiac function in isolated left or right heart failure by applying rhythmic loading to either ventricle. Our devices anchor to the interventricular septum and apply forces to the free wall of the ventricle to cause approximation of the septum and free wall in systole and assist with recoil in diastole. Physiological sensing of the native hemodynamics enables organ-in-the-loop control of these robotic implants for fully autonomous augmentation of heart function. The devices are implanted on the beating heart under echocardiography guidance. We demonstrate the concept on both the right and the left ventricles through in vivo studies in a porcine model. Different heart failure models were used to demonstrate device function across a spectrum of hemodynamic conditions associated with right and left heart failure. These acute in vivo studies demonstrate recovery of blood flow and pressure from the baseline heart failure conditions. Significant reductions in diastolic ventricle pressure were also observed, demonstrating improved filling of the ventricles during diastole, which enables sustainable cardiac output.

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 into 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 into 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.

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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.

Soft robotic devices have been proposed as an alternative solution for ventricular assistance. Unlike conventional ventricular assist devices (VADs) that pump blood through an artificial lumen, soft robotic VADs (SRVADs) use pneumatic artificial muscles (PAM) to assist native contraction and relaxation of the ventricle. Synchronization of SRVADs is critical to ensure maximized and physiologic cardiac output. We developed a proof-of-concept synchronization algorithm that uses an epicardial electrogram as an input signal and evaluated the approach on adult Yorkshire pigs (n = 2). An SRVAD previously developed by our group was implanted on the right ventricle (RV). We demonstrated an improvement in the synchronization of the SRVAD using an epicardial electrogram signal versus a RV pressure signal of 4 ± 0.5% in heart failure and 3.2 ± 0.5% during actuation for animal 1 and 7.4 ± 0.6% in heart failure and 8.2% ± 0.8% during actuation for animal 2. Results suggest that improved synchronization is translated in greater cardiac output. The pulmonary artery (PA) flow was restored to a 107% and 106% of the healthy baseline during RV electrogram actuation and RV pressure actuation, respectively, in animal 1, and to a 100% and 87% in animal 2. Therefore, the presented system using the RV electrogram signal as a control input has shown to be superior in comparison with the use of the RV pressure signal.

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