In the research area of Advanced Electronic Materials and Sensors, in 2022, Professor Tavakoli was awarded the Prestigious European Research Council Consolidator Grant with the value of 2.8 million Euros. Liquid3D proposes bioinspired electronics and machines that are soft, resilient, self-healing, shape-morphing, and fully recyclable. The project has developed (and is continuing to develop) a series of game changer Liquid Metal based composites that can be 3D printed to constitute functional cells of such soft machines. This includes printed batteries, actuators, and sensors that can be printed side by side. This provides an excellent design freedom to scientists for manufacturing complex “living” electronics, while guaranteeing that any possible product coming from these inventions will be Resilient, Repairable, and Recyclable.  Liquid3D foresees to develop fundamental understanding, and mathematical modelling of biphasic systems, and develops novel room temperature printable composites with sensing/acting/energy storage properties, and methods for recycling them. It is as well investigating novel forms of implementing truly 3D electronics, with distributed functional cells. Liquid3D intends to fundamentally rethink the concept of electronics, as we know today. From rigid and brittle to soft, resilient and repairable; From polluting to recyclable; from battery dependent to self-powered; from 2D to truly 3D; It proposes a radically new way of making “greener” electronics. Liquid3D aims to establish a world leading center on recyclable, and green electronics.

Brain-computer interfaces (BCI) face a significant usability challenge. Their low reliability, combined with the high mental workload required for their control, necessitates the exploration of new approaches for their effective use. The Human-centered Mobile Robotics (HcMR) team has developed innovative methods combined into a single framework to steer a robotic wheelchair with an electroencephalography P300-based BCI. The system integrates self-paced control, allowing users to send BCI commands at their own pace, with a collaborative controller that combines sparse BCI commands (user intentions) with navigation information, generating safe and smooth trajectories in complex environments. Additionally, the system incorporates a dynamic time window to detect BCI commands, allowing it to adapt to user attention shifts and fatigue. Altogether, this framework has enabled wheelchair control with nearly 100% accuracy and low mental workload, achieving unprecedented results. Experimental validation included able-bodied participants and participants with severe motor disabilities (cerebral palsy, spinal cord injury, agenesis of the four members, limb-girdle muscular dystrophy, and Duchene muscular dystrophy). This achievement represents a milestone in BCI development, and a significant advancement in its use as Assistive Technology. One of the latest publications   related this Robotic wheelchair controlled with a BCI received the prestigious 2022 Andrew P. Sage Best Transactions Paper Award.

Arthroscopy is a modality of orthopedic surgery in which instruments and endoscopic camera (the arthroscope) are inserted into the articular cavity through small incisions (the surgical ports). Arthroscopy is highly beneficial for the patient but clinical execution is difficult to accomplish because of indirect visualization and limited maneuverability inside the joint. This is a scenario where surgical assistive technologies can have strong impact in improving clinical outcome and disseminating the benefits of arthroscopy by increasing the number of adopters. The ISR-UC created the first effective system for accomplishing navigated arthroscopy that combines real-time video processing for accurate 3D measurements on the anatomy, with augmented reality for overlaying meaningful guidance information in images. It is the first of the kind not requiring additional intra-operative sensing modalities that preclude the application in arthroscopy. Moreover, the improved usability, higher metric accuracy, and avoidance of additional capital equipment make video-based navigation also appealing for open orthopedic surgery. The research effort conducted to several high profile publications and patents that were licensed in exclusive to a spin-off company backed by Venture Capital called Perceive3D (P3D). In early 2021 P3D was discretely acquired by an incumbent in orthopedics with almost 20000 workers worldwide. In the follow-up of the acquisition this incumbent established an advanced research unit in the city with a collaboration protocol with the ISR-UC. The research topics being tackled include medical image segmentation, visual 3D registration of anatomic tissues, 6D pose estimation of instruments, and marker less surgical navigation.

ID documents such as the passports and national ID cards are used as a physical (now becoming also digital) personal portable document with a face portrait that assures that the citizen is the genuine owner of the document. Additionally, Facial Recognition Systems can, nowadays, help institutions and companies in the authentications of citizens too, sometimes also scanning the ID documents, for instance in airports. However, the portrait of these documents is their most attacked security element, which urges the need to study and develop new scientific and technical solutions to protect authentication systems against attacks. The Computer Vision Lab of ISR-UC works since 2019, in partnership with the Portuguese Mint and Official Printing Office, in two projects to investigate and develop facial recognition systems resilient to attacks. One of the biggest threat to passports is the attack of their portrait using images with morphing, where the face printed in the document is the result of fusing face images of two or more alike persons, thus allowing that both persons can enter an arbitrary border using the same document. Detection and blocking of Morphing attacks was identified by the European Commission as one of the priorities for EES (EU Entry/Exit System). In this context, we have developed several algorithms to detect morphing in face images and submitted to the MORPH benchmark of the NIST (American National Institute of Systems and Technology). This benchmark is the de facto evaluation of morphing algorithms, both from academia and research centers and from industrial companies selling commercial solutions. The benchmark is organized in several sub-datasets, for different types of scenarios. Our submission of august/2022 achieved the Top-1 performer of more than half of the sub datasets and second place in the others. This achievement allowed ISR-UC to continue to improve its morphing attack detection algorithms, materialized in several publications in the last years and to envision several other applications in the future.

An innovative dynamic identification model for robot manipulators has been developed.  The work addresses physical feasibility of robot dynamics identification using Linear Matrix Inequalities (LMIs). Inertia tensor inequalities (namely the positive definite property) have been extensively used among other physical constraints to check physical consistency of identification methods. Recently, an extra inequality associated to inertia tensor eigenvalues has been included to check physical consistency, showing that the previous methods were incomplete. In this paper we show that this extra inequality incorporates the positive definite property, being more restrictive. We also include it in the linear matrix inequality framework for robot dynamics identification to obtain fully physical estimates. The relevance of the extra inequality is verified through real 7-DOF robot manipulator experiments, described in the paper “Inertia Tensor Properties in Robot Dynamics Identification: A Linear Matrix Inequality Approach”, IEEE/ASME Transactions on Mechatronics, Vol. 24, N. 1, pp. 406-411, 2019. Several top international researcher have validated our model and confirmed its outstanding performance. Recently, in 2021, a paper of Italian authors (see: G. Golluccio, G. Gillini, A. Marino and G. Antonelli, "Robot Dynamics Identification: A Reproducible Comparison With Experiments on the Kinova Jaco," in IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp. 128-140, Sept. 2021) performed a comparative study, showing that our method is the state-of-art in this area.  Additionally, our method is used by the group of Prof. Jean-Jacques Slotine from MIT (see early reference to our work: https://arxiv.org/pdf/1701.04395v1.pdf), and also by the group of Prof. Bruno Siciliano “Non-prehensile manipulation of deformable objects: Achievements and perspectives from the rodyman project,” IEEE Robotics and Automation Magazine, Vol. 25, No. 3, pp. 83-92, 2018).