A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot

In this work, we introduce HaReS, a hand rehabilitation system. Our proposal integrates a series of exercises, jointly developed with a foundation for those with motor and cognitive injuries, that are aimed at improving the skills of patients and the adherence to the rehabilitation plan. Our system takes advantage of a low-cost hand-tracking device to provide a quantitative analysis of the performance of the patient. It also integrates a low-cost surface electromyography (sEMG) sensor in order to provide insight about which muscles are being activated while completing the exercises. It is also modular and can be deployed on a social robot. We tested our proposal in two different facilities for rehabilitation with high success. The therapists and patients felt more motivation while using HaReS, which improved the adherence to the rehabilitation plan. In addition, the therapists were able to provide services to more patients than when they used their traditional methodology.This work was funded by a Spanish Government PID2019-104818RB-I00 grant, supported by Feder funds. It was also supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887

Inferring Static Hand Poses from a Low-Cost Non-Intrusive sEMG Sensor

Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology. This work proposes a learning-based approach that performs gesture recognition using a surface electromyography-based device, the Myo Armband released by Thalmic Labs, which is a commercial device and has eight non-intrusive low-cost sensors. With 35 able-bodied subjects, and using the Myo Armband device, which is able to record data at about 200 MHz, we collected a dataset that includes six dissimilar hand gestures. We used a gated recurrent unit network to train a system that, as input, takes raw signals extracted from the surface electromyography sensors. The proposed approach obtained a 99.90% training accuracy and 99.75% validation accuracy. We also evaluated the proposed system on a test set (new subjects) obtaining an accuracy of 77.85%. In addition, we showed the test prediction results for each gesture separately and analyzed which gestures for the Myo armband with our suggested network can be difficult to distinguish accurately. Moreover, we studied for first time the gated recurrent unit network capability in gesture recognition approaches. Finally, we integrated our method in a system that is able to classify live hand gestures.This work was supported by the Spanish Government TIN2016-76515R grant, supported with Feder funds. It has also been funded by the University of Alicante project GRE16-19, by the Valencian Government project GV/2018/022, and by a Spanish grant for PhD studies ACIF/2017/243

Momordica charantia extracts protect against inhibition of endothelial angiogenesis by advanced glycation endproducts in vitro.

Diabetes mellitus characterized by hyperglycemia favors formation of advanced glycation endproducts (AGEs) capable of triggering vascular complications by interfering with imbalanced inflammation and angiogenesis to eventually impede wound-healing. Momordica charantia (MC, bitter melon) has been shown to prevent AGE formation and to promote angiogenesis in diabetic wounds in animal models. However, the mechanism underlying its effects on angiogenesis is unclear. We investigated the effects of methanolic extracts of MC pulp (MCP), flesh (MCF) and charantin (active component of MC) using an in vitro model of angiogenesis. MC extracts or low concentrations of bovine serum albumin-derived AGEs (BSA-AGEs) stimulated proliferation, migration (using wound-healing assay) and tube formation (using Matrigel™-embedded 3D culture) of bovine aortic endothelial cells (BAEC) together with increases in the phosphorylation of extracellular signal-regulated kinase (ERK)1/2, the key angiogenic signaling cytoplasmic protein. Blocking the receptor for AGEs (RAGE) inhibited low BSA-AGE- and MC extract-induced ERK1/2 phosphorylation and tube formation, indicating the crucial role of RAGE in the pro-angiogenic effects of MC extracts. Moreover, inhibitory effects of high BSA-AGE concentration on cell proliferation and migration were reduced by the addition of MC extracts, which reversed the BSA-AGE anti-angiogenic effect on tube formation. Thus, MC extracts exert direct pro-angiogenic signaling mediated via RAGE to overcome the anti-angiogenic effects of high BSA-AGEs, highlighting the biphasic RAGE-dependent mechanisms involved. This study enhances our understanding of the mechanisms underlying the pro-angiogenic effects of MC extracts in improvement of diabetes-impaired wound-healing

Assistive Robot with an AI-Based Application for the Reinforcement of Activities of Daily Living: Technical Validation with Users Affected by Neurodevelopmental Disorders

In this work, we propose the first study of a technical validation of an assistive robotic platform, which has been designed to assist people with neurodevelopmental disorders. The platform is called LOLA2 and it is equipped with an artificial intelligence-based application to reinforce the learning of daily life activities in people with neurodevelopmental problems. LOLA2 has been integrated with an ROS-based navigation system and a user interface for healthcare professionals and their patients to interact with it. Technically, we have been able to embed all these modules into an NVIDIA Jetson Xavier board, as well as an artificial intelligence agent for online action detection (OAD). This OAD approach provides a detailed report on the degree of performance of a set of daily life activities that are being learned or reinforced by users. All the human–robot interaction process to work with users with neurodevelopmental disorders has been designed by a multidisciplinary team. Among its main features are the ability to control the robot with a joystick, a graphical user interface application that shows video tutorials with the activities to reinforce or learn, and the ability to monitor the progress of the users as they complete tasks. The main objective of the assistive robotic platform LOLA2 is to provide a system that allows therapists to track how well the users understand and perform daily tasks. This paper focuses on the technical validation of the proposed platform and its application. To do so, we have carried out a set of tests with four users with neurodevelopmental problems and special physical conditions under the supervision of the corresponding therapeutic personnel. We present detailed results of all interventions with end users, analyzing the usability, effectiveness, and limitations of the proposed technology. During its initial technical validation with real users, LOLA2 was able to detect the actions of users with disabilities with high precision. It was able to distinguish four assigned daily actions with high accuracy, but some actions were more challenging due to the physical limitations of the users. Generally, the presence of the robot in the therapy sessions received excellent feedback from medical professionals as well as patients. Overall, this study demonstrates that our developed robot is capable of assisting and monitoring people with neurodevelopmental disorders in performing their daily living tasks.This research was funded by project AIRPLANE, with reference PID2019-104323RB-C31, of Spain’s Ministry of Science and Innovation

Bis[S-benzyl 3-(furan-2-ylmethylidene)dithiocarbazato-κ2 N 3,S]copper(II): crystal structure and Hirshfeld surface analysis

The title CuII complex, [Cu(C13H11N2OS2)2], features a trans-N2S2 donor set as a result of the CuII atom being located on a crystallographic centre of inversion and being coordinated by thiolate-S and imine-N atoms derived from two dithiocarbazate anions. The resulting geometry is distorted square-planar. In the crystal, π(chelate ring)–π(furyl) [inter-centroid separation = 3.6950 (14) A ˚ and angle of inclination = 5.33 (13)˚] and phenyl-C—H...π(phenyl) interactions sustain supramolecular layers lying parallel to (102). The most prominent interactions between layers, as confirmed by an analysis of the calculated Hirshfeld surface, are phenyl-H...H(phenyl) contacts. Indications for Cu...Cg(furyl) contacts (Cu...Cg = 3.74 A ˚ ) were also found. Interaction energy calculations suggest the contacts between molecules are largely dispersive in natur

Hand gesture recognition using sEMG and deep learning

In this thesis, a study of two blooming fields in the artificial intelligence topic is carried out. The first part of the present document is about 3D object recognition methods. Object recognition in general is about providing the ability to understand what objects appears in the input data of an intelligent system. Any robot, from industrial robots to social robots, could benefit of such capability to improve its performance and carry out high level tasks. In fact, this topic has been largely studied and some object recognition methods present in the state of the art outperform humans in terms of accuracy. Nonetheless, these methods are image-based, namely, they focus in recognizing visual features. This could be a problem in some contexts as there exist objects that look alike some other, different objects. For instance, a social robot that recognizes a face in a picture, or an intelligent car that recognizes a pedestrian in a billboard. A potential solution for this issue would be involving tridimensional data so that the systems would not focus on visual features but topological features. Thus, in this thesis, a study of 3D object recognition methods is carried out. The approaches proposed in this document, which take advantage of deep learning methods, take as an input point clouds and are able to provide the correct category. We evaluated the proposals with a range of public challenges, datasets and real life data with high success. The second part of the thesis is about hand pose estimation. This is also an interesting topic that focuses in providing the hand's kinematics. A range of systems, from human computer interaction and virtual reality to social robots could benefit of such capability. For instance to interface a computer and control it with seamless hand gestures or to interact with a social robot that is able to understand human non-verbal communication methods. Thus, in the present document, hand pose estimation approaches are proposed. It is worth noting that the proposals take as an input color images and are able to provide 2D and 3D hand pose in the image plane and euclidean coordinate frames. Specifically, the hand poses are encoded in a collection of points that represents the joints in a hand, so that they can be easily reconstructed in the full hand pose. The methods are evaluated on custom and public datasets, and integrated with a robotic hand teleoperation application with great success

An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques

In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG-controlled 3D game that leverages a deep learning-based architecture for real-time gesture recognition. The 3D game experience developed in the study is focused on rehabilitation exercises, allowing individuals with certain disabilities to use low-cost sEMG sensors to control the game experience. For this purpose, we acquired a novel dataset of seven gestures using the Myo armband device, which we utilized to train the proposed deep learning model. The signals captured were used as an input of a Conv-GRU architecture to classify the gestures. Further, we ran a live system with the participation of different individuals and analyzed the neural network’s classification for hand gestures. Finally, we also evaluated our system, testing it for 20 rounds with new participants and analyzed its results in a user study.This work was supported by the Spanish Government PID2019-104818RB-I00 grant, supported with Feder funds

Sardinelle protein isolate as a novel material for oil microencapsulation: Novel alternative for fish by-products valorisation

International audienc

Towards Sketched-based Garment Design and. Animation

One of the most tedious tasks in the virtual clothing process is the garment creation phase with its direct previsualization, and animation on the body. In this paper, we propose a new approach for garment design from the outlines drawn directly on a 3D mannequin. These outlines represent the boundaries of closed regions that are quad meshed to generate the garment pieces. The garments can then be animated in a simulation system based on Finite Elements and using a rediscretization of the generated mesh into triangular elements containing a reconstructed metric of the cloth surface. This allows previewing the garment on bodies with various poses and animations