The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a product. There exist procedures that systematically allows measurement of these property perceptions that are performed by professional panels. However, systematic evaluations of attributes by these tasting panels, which avoid the subjective character for an individual taster, have a high economic, temporal and organizational cost. The process is only applied in a sampled way so that its result cannot be used on a sound and complete quality system. In this paper, we present a method that allows making use of a non-destructive measurement of physical–chemical properties of the target product to obtain an estimation of the sensory description given by QDA-based procedure. More concisely, we propose that through Artificial Neural Networks (ANNs), we will obtain a reliable prediction that will relate the near-infrared (NIR) spectrum of a complete set of cheese samples with a complete image of the sensory attributes that describe taste, texture, aspect, smell and other relevant sensations.

In food products marketing, ensuring to the consumer identical organoleptic properties is vital for maintaining the client fidelity. Systematically achieve it by using as sensory information the valuations of a tasting panel, is unfeasible. Routinely, to assemble a tasting panel involves organizational and economics costs, as well as the sensory fatigue and the subjectivity of the panel members. In this paper is proposed a vitualization strategy or computational model focused on food products quality control, based on cooperation and data exchange between the main agents involved in the process: quality managers, professional tasters, production managers, inspection authorities, etc. Virtualization (digitalization) is supported on a ICatador cloud platform which has intelligent algorithms embedded to predict the food sensory properties. These algorithms have Near InfraRed Spectroscopy data of the product samples as input. As a validation scenario, our virtualization approach has been applied to the ripening cheese elaboration. Thanks to advanced visualization techniques, the quality manager can immediately and systematically know the merit figure related to a product sensory quality. The ICatador solution, has two profitable aspects. Sensory analysis is performed without routinely gathering a professional tasting panel. As a huge amount of data coming from the elaboration process itself are available, the intelligent algorithms are enriched by these data for the adaptation to the product elaboration process. In this way, we will be able to fine-tune continuously the machine-learning algorithms to the particular process and use them intelligently to increase the competitiveness.

As a consequence of the huge development of IMU (Inertial Measurement Unit) sensors based on MEMS (Micro-Electromechanical Systems), innovative applications related to the analysis of human motion are now possible. In this paper, we present one of these applications: a portable platform for training in Ultrasound Imaging-based musculoskeletal (MSK) exploration in rehabilitation settings. Ultrasound Imaging (USI) in the diagnostic and treatment of MSK pathologies offers various advantages, but it is a strongly operator-dependent technique, so training and experience become of fundamental relevance for rehabilitation specialists. The key element of our platform is a replica of a real transducer (HUSP—Haptic US Probe), equipped with MEMS based IMU sensors, an embedded computing board to calculate its 3D orientation and a mouse board to obtain its relative position in the 2D plane. The sensor fusion algorithm used to resolve in real-time the 3D orientation (roll, pitch and yaw angles) of the probe from accelerometer, gyroscope and magnetometer data will be presented. Thanks to the results obtained, the integration of the probe into the learning platform allows a haptic sensation to be recreated in the rehabilitation trainee, with an attractive performance/cost ratio.

Minimally invasive percutaneous insertion procedures are widely used techniques in medicine. Their success is highly dependent on the skills of the practitioner. This paper presents a haptic simulator for training in these procedures, whose key component is a real percutaneous insertion needle with a sensory system incorporated to track its 3D location at every instant. By means of the proposed embedded vision system, the attitude (spatial orientation) and depth of insertion of a real needle are estimated. The proposal is founded on a novel depth estimation procedure based on optical flow techniques, complemented by sensory fusion techniques with the attitude calculated with data from an Inertial Measurement Unit (IMU) sensor. This procedure allows estimating the needle attitude with an accuracy of tenths of a degree and the displacement with an accuracy of millimeters. The computational algorithm runs on an embedded computer with real-time constraints for tracking the movement of a real needle. This haptic needle location data is used to reproduce the movement of a virtual needle within a simulation app. As a fundamental result, an ergonomic and realistic training simulator has been successfully constructed for healthcare professionals to acquire the mental model and motor skills necessary to practice percutaneous procedures successfully.

Background: Ultrasound is very important in medicine and teaching, but there are not many formal training programs. We also do not know much about what students think. To be good at using ultrasound, one needs to learn technical, thinking, and seeing skills. This is especially true in regional anesthesia (RA), where mistakes in reading images can cause problems. Training with simulations is a safe and good way to learn these skills. Some models are helpful for teaching how to perform procedures using ultrasound. Objective: This study aimed to evaluate the effectiveness, localization time, and success rate of traditional teaching versus a new simulation-based teaching method for RA designed by the investigators among undergraduate medical students. Methods: A prospective, randomized controlled trial was conducted at the University of Salamanca from April 2022 to January 2023. A total of 34 medical students in their fourth to sixth academic years were randomly allocated to either a simulation-based training group using the Haptic Ultrasound Probe or a traditional teaching group. The simulation approach used a realistic probe replica and a software-based ultrasound environment, whereas the traditional method comprised a theoretical lecture and curated audiovisual materials. Two days after training, participants underwent a blinded assessment requiring the identification of peripheral nerve plexuses using an ultrasound device. The primary outcome measured was the successful identification of nerves, and the secondary outcome was the time taken to complete each procedure. Data were analyzed using an intention-to-treat approach. Results: A total of 34 medical students (fourth to sixth years) were recruited to compare traditional teaching with simulation-based training in ultrasound-guided nerve localization. No statistically significant differences were found in the success rates between the groups. For the interscalene approach, the traditional teaching group achieved a 100% (17/17) success rate compared to 82% (14/17) in the simulation group (P=.07). The time to task completion was similar across most procedures. In the sciatic nerve division, the traditional teaching group was significantly faster, with a mean time of 42.4 (SD 39.5) seconds (P=.02). The regression models showed no significant interaction between the intervention type and academic year. Both teaching methods had positive educational impacts. Conclusions: Simulation-based learning effectively supports competency acquisition in RA and offers a safe, scalable alternative to traditional methods. Its integration into medical curricula may standardize training, improve skill consistency, and enhance patient safety. Further multicenter studies with larger, diverse cohorts are needed to validate these benefits and guide implementation in medical education.