Emotion Recognition (ER) has gained significant attention due to its importance in advanced human-machine interaction and its widespread real-world applications. In recent years, research on ER systems has focused on multiple key aspects, including the development of high-quality emotional databases, the selection of robust feature representations, and the implementation of advanced classifiers leveraging AI-based techniques. Despite this progress in research, ER still faces significant challenges and gaps that must be addressed to develop accurate and reliable systems. To systematically assess these critical aspects, particularly those centered on AI-based techniques, we employed the PRISMA methodology. Thus, we include journal and conference papers that provide essential insights into key parameters required for dataset development, involving emotion modeling (categorical or dimensional), the type of speech data (natural, acted, or elicited), the most common modalities integrated with acoustic and linguistic data from speech and the technologies used. Similarly, following this methodology, we identified the key representative features that serve as critical emotional information sources in both modalities. For acoustic, this included those extracted from the time and frequency domains, while for linguistic, earlier embeddings and the most common transformer models were considered. In addition, Deep Learning (DL) and attention-based methods were analyzed for both. Given the importance of effectively combining these diverse features for improving ER, we then explore fusion techniques based on the level of abstraction. Specifically, we focus on traditional approaches, including feature-, decision-, DL-, and attention-based fusion methods. Next, we provide a comparative analysis to assess the performance of the approaches included in our study. Our findings indicate that for the most commonly used datasets in the literature: IEMOCAP and MELD, the integration of acoustic and linguistic features reached a weighted accuracy (WA) of 85.71% and 63.80%, respectively. Finally, we discuss the main challenges and propose future guidelines that could enhance the performance of ER systems using acoustic and linguistic features from speech. • Exploration of emotion modeling theories. • Analysis of key insights of Speech Emotion Recognition (SER) datasets. • Assessment of speech features across acoustic and linguistic modalities for ER. • Thorough evaluation of fusion techniques for SER. • Detailed benchmark analysis of SER approaches. • Detailed selection of research articles under systematic review approach. • Critical discussion of the current state and emerging research opportunities.

This paper presents the implementation of a localization system in a senior living residence located in a town in the province of Guadalajara, Spain. The system integrates precise location tracking using Ultra-Wideband (UWB) technology in the common areas of the residence, and symbolic positioning via millimeter-wave (mmWave) radar modules installed in private rooms. In UWB-monitored areas, users carry a wearable UWB tag, while in mmWave-radar monitored rooms, a device-free approach is adopted. The localization system is designed to be scalable, easy to deploy, and to support remote maintenance and management, facilitating long-term operation with minimal on-site intervention. This paper provides a detailed description of the devices used, the deployment process, and the overall system setup. Furthermore, by leveraging open-source tools, efficient data management has been achieved. Finally, raw data collected from the implemented system are presented graphically. These data will support future development of tools to detect behavioral anomalies early, which may indicate physical or cognitive decline among the monitored individuals. The collected data are also valuable for identifying daily behavioral patterns and routines, and for detecting deviations that could signal the onset of conditions associated with physical and/or cognitive impairment.

This work proposes the use of room-level or symbolic localisation for the analysis of the daily life of elderly people. The elderly people tend to be quite routine, so variations in behaviour patterns can be an indication of physical or cognitive impairment. Then, it is essential to develop solutions for the early detection of changes or anomalies that can be the onset of physical or cognitive impairment. In this work, the behaviour of the monitored person is modelled by the probability of staying in each room of their place of residence. The proposed system is based on the generation of a baseline behaviour, defined by groups of similar days during an initial training period. From this model, each new day is compared with the baseline behaviour and the evolution is analysed. Two databases are used for the evaluation of the proposed system: a real database and a synthetic one. The initial results are very promising, being possible to classify location-based daily activities as main and secondary routines, detecting when a day is considered unusual.

Gait analysis is the systematic study of the movement of human locomotion. It has applications in the field of health by allowing the diagnosis of diseases related to gait disturbances, such as the risk of falling or frailty in the elderly. The identification of these pathologies is carried out by analyzing different specific gait parameters, such as speed, time or length of each step, etc. Currently, there are numerous methods or technologies for gait analysis, mainly divided into non-portable systems (optical systems or pressure mapping corridors) and portable systems (inertial sensors, pressure insoles, electromyography, etc.), with different characteristics and limitations. It is important to be able to assess which technique is the most appropriate to use for each type of gait analysis required. In this work, we study and compare three different technologies for gait analysis, two of them commercial: an optical system (optitrack) and a pressure mapping system for human gait analysis (StrideWay); and a dual inertial system to be placed on the feet (IMUE-CSIC, proprietary design). We analyze the main features of each system, as well as their advantages and limitations, including the gait parameters that they can provide directly or indirectly from the recorded information. In addition, gait trials have been carried out using all three systems simultaneously. The results obtained reveal very small differences between them, being less than 0.02 seconds for gait cycle time and 0.03 meters for stride length. The advantages of using IMUs are their low cost and portability (allowing outdoor testing). Despite requiring complex post-processing algorithms, they provide a broad set of gait parameters. The optical system and the pressure mapping corridor for human gait analysis have high resolution, but their high cost and lack of portability make them difficult to use in real conditions outside the laboratory. Additionally, the pressure corridor provides more extensive results on the footprint, useful in physiotherapy or podiatry applications.

Home-based physical therapies are effective if the prescribed exercises are correctly executed and patients adhere to these routines. This is specially important for older adults who can easily forget the guidelines from therapists. Inertial Measurement Units (IMUs) are commonly used for tracking exercise execution giving information of patients’ motion data. In this work, we propose the use of Machine Learning techniques to recognize which exercise is being carried out and to assess if the recognized exercise is properly executed by using data from four IMUs placed on the person limbs. To the best of our knowledge, both tasks have never been addressed together as a unique complex task before. However, their combination is needed for the complete characterization of the performance of physical therapies. We evaluate the performance of six machine learning classifiers in three contexts: recognition and evaluation in a single classifier, recognition of correct exercises, excluding the wrongly performed exercises, and a two-stage approach that first recognizes the exercise and then evaluates it. We apply our proposal to a set of 8 exercises of the upper-and lower-limbs designed for maintaining elderly people health status. To do so, the motion of 30 volunteers were monitored with 4 IMUs. We obtain accuracies of 88 . 4 % and the 91 . 4 % in the two initial scenarios. In the third one, the recognition provides an accuracy of 96 . 2 %, whereas the exercise evaluation varies between 93 . 6 % and 100 . 0 %. This work proves the feasibility of IMUs for a complete monitoring of physical therapies in which we can get information of which exercise is being performed and its quality, as a basis for designing virtual coaches.

This work proposes a system that allows estimating and monitoring daily routine changes in a sensorized home through Machine Learning and Affinity Propagation clustering techniques. Older adults often have low-activity and rather routine lives, which means that these routines can be an indicator of their physical and cognitive state in order to lead an independent life and healthy ageing. Therefore, it is important to be able to generate precise routines, as well as to monitor them, to trigger alarms in case of significant variations. This proposal defines routines based on the time spent in each of the monitored rooms. The daily time in each room is estimated trough a Bluetooth Low Energy-based indoor localization system. The localization is obtained through the Bluetooth received signal strength, which is processed with different supervised algorithms and fused with the acceleration measured by the mobile receiver, obtaining an accuracy above 96 %. From these data, the sample has been synthetically expanded to generate four different routines, on which the proposed algorithm based on Principal Component Analysis and Affinity Propagation clustering has been tested, obtaining very promising results.

The main limitation of using inertial measurement units (IMUs) for inertial navigation systems (INS) is the accumulated error in the heading angle estimation. Different approaches have been proposed to reduce the position error caused by the heading drift, but accurate biomechanical models have not been explored in INS. A precise leg characterization method is needed to develop INS-oriented biomechanical models. The main goal of this work is to evaluate an IMU-based method to locate the leg joint centers and axes, and to estimate the leg segment lengths. These leg parameters are required for the implementation of lower-limb biomechanical models. Four different versions of the method are implemented and compared using a stereo- photogrammetric system as gold standard. The method validation is conducted in four volunteers by doing five exercises. The calibration exercises involve five different leg movements. The proposed method shows an average accuracy of 2 cm and 3 cm over 21cm and 54 cm in joint centers and axes determination, respectively. In the estimation of the leg segment lengths, the average accuracy of the proposed method is 2 cm over 39 cm. The proposed method brings promising results improving the estimations of the state-of-the-art methods.

Acoustic local positioning systems (ALPSs) are an interesting alternative for indoor positioning due to certain advantages over other approaches, including their relatively high accuracy, low cost, and room-level signal propagation. Centimeter-level or fine-grained indoor positioning can be an asset for robot navigation, guiding a person to, for instance, a particular piece in a museum or to a specific product in a shop, targeted advertising, or augmented reality. In airborne system applications, acoustic positioning can be based on using opportunistic signals or sounds produced by the person or object to be located (e.g., noise from appliances or the speech from a speaker) or from encoded emission beacons (or anchors) specifically designed for this purpose. This work presents a review of the different challenges that designers of systems based on encoded emission beacons must address in order to achieve suitable performance. At low-level processing, the waveform design (coding and modulation) and the processing of the received signal are key factors to address such drawbacks as multipath propagation, multiple-access interference, near-far effect, or Doppler shifting. With regards to high-level system design, the issues to be addressed are related to the distribution of beacons, ease of deployment, and calibration and positioning algorithms, including the possible fusion of information obtained from maps and onboard sensors. Apart from theoretical discussions, this work also includes the description of an ALPS that was implemented, installed in a large area and tested for mobile robot navigation. In addition to practical interest for real applications, airborne ALPSs can also be used as an excellent platform to test complex algorithms (taking advantage of the low sampling frequency required), which can be subsequently adapted for other positioning systems, such as underwater acoustic systems or ultrawideband radio-frequency (UWB RF) systems.

This work proposes an Android-based platform to warn the medical staff of the onset of allergy reactions during a provocation test in a hospital. The portable system carries out the analysis of the heart rate variability for the early detection of allergic reactions in patients undergoing allergy provocation tests at hospitals. The proposal is composed of an ECG (electrocardiogram) acquisition system and an Android device (Smartphone, Tablet) that monitors and evaluates the results in real time, increasing the safety of allergic tests. At present, food and drug allergic tests are a major problem for patients because of their long duration and intrusion. However, the authors have designed an algorithm for detecting allergy reactions that have focused on reducing the time of the tests and the number of doses. This algorithm runs on an Android platform, and it is able to provide alarms for the medical staff if there is an allergy reaction. The proposed monitoring system is very suitable for the health monitoring during the provocation tests.

This paper describes the design, measurement methodology, and experimental validation of an electronic system for monitoring the electrical discontinuity in rails of double-track railway lines in order to detect the rail breakage. The electronic system is based on an emitter node and on the two receiver nodes. The three nodes, together with the four rails to be analyzed, constitute the electrical circuits, whose currents are measured in both the emission and reception nodes. These nodes can communicate through a data network between them, as well as with a remote center that controls and synchronizes the electronic systems along the railway line. The electronic subsystem associated with the emitter node transmits an encoded signal to the rails, modulated by an 800-Hz carrier. A set of eight currents are measured, one per rail at each end of the section defined between the emitter and the receiver, with a maximum length of 7 km. The evaluation of electrical discontinuities in each section is carried out by means of the relative imbalances that can appear between the eight measured currents, according to possible breakages. The method of measurement is characterized by a high immunity to noise. The results presented here are based on a railway line prototype, which implements the electrical propagation models and coupling between the four rails in the double track. This model allows the proposed electronic system and measurement methodology to be successfully verified.

The current method to detect allergies obliges the patient to stay within a room under the observation of the nurse during the allergy tests. Such tests last up to 4 hours. The authors have developed an allergy detection system based on the heart rate variability, which is monitored through electrocardiogram sensors. The detector is based on the correlation between variations of the heart rate and allergic reactions. However, heart rate variations are also due to the patient's physical activity, being necessary to discriminate them. The pocket navigation system will be used to enhance the performance of the allergy detection algorithm, as it is able to identify different physical activities. Additionally, the patient can walk out of the testing room because a 3D localization solution is possible. The experiments described in this article show that it is possible identifying the most important patient's physical activities to avoid false allergic reaction alarms. Moreover, the patient is allowed to move within the hospital during the allergy tests and can be located in case of a severe allergic reaction occurs. The experiments show also long duration walks to assess that our unaided inertial-based navigation system is suited to this particular medical application.

This paper describes the hands on workshop experience using a robot kit DepecaBot, as a part of a global effort of teaching robotics as a way of improving skills for students in field of computer science, electronics, and telecommunications. The Robotics Week has been taking place at the University of Alcala since 2000. This Robotics Week comprises different activities: sumo fighting robots, autonomous race robots, conferences, mini-conferences and workshops. In this framework, the DepecaBot hands on workshop is held in parallel with other working activities. In this paper, the bases, creation, evolution, development, and assessments of this hands on workshop are shown as a part of a continuous effort. These efforts try to improve skills, motivation, by making things by using robotics. Participants in the workshop make a complete working robot, from the scratch. A small competition takes place in the end of the workshop in order to test the acquired skills.

The development of Local Positioning Systems (LPSs), that allow user positioning in areas where solutions based on GNSS (Global Navigation Satellite System) fail (as indoor environments), has been an active research field in the last years. In LPSs based on Ultra-Wideband (UWB), near-far effect is a significant issue that severely constrains the system performance. In this work, we present a novel UWB LPS based on CDMA (Code Division Multiple Access) and Loosely Synchronized sequences and we compare their performance with an UWB LPS based on TDMA (Time Division Multiple Access), which is a multiple access scheme commonly used to cope with near-far effect. The first approach provides the benefits of simultaneous access, thus minimizing hardware requirements and near-far effect.

The current stage of railway transportation systems must deal with increased safety and reliability issues. A key point is improving occupancy track circuit performance and providing them with redundancy, higher noise immunity, and the capability to acquire additional information about the track section involved. This work proposes a novel track circuit based on the encoding of the electrical transmissions with Kasami codes. Track circuit emitters send signals coded with a known sequence that can be identified by the corresponding receivers using correlation techniques; these processes increase immunity to noise and changes in environmental conditions. An appropriate selection of orthogonal sequences for encoding, as well as different carrier frequencies for transmissions, allow simultaneous emissions and receptions without cross interference.

In this paper it is proposed a new method for generating multilevel Complementary Sets of Sequences (CSS) based on the existence of Hadamard matrices of odd order. Then it is generated a family of M2Multilevel Loosely Synchronized (MLS) codes from a multilevel CSS. These codes have inherent advantages that make interesting their use in UWB-based local positioning systems.

On current railway systems, it is becoming ever more necessary to install safety elements to avoid accidents. One of the causes that can provoke serious accidents is the existence of obstacles on the tracks, either fixed or mobile. In this paper, a multisensory system that can inform the monitoring system about the existence of obstacles is proposed. The system for obstacle detection consists of two emitting and receiving barriers, which are placed on opposing sides of the railway, respectively, and use infrared and ultrasonic sensors, thus establishing different optical and acoustic links between them. The interruption of one or several links should produce an alarm. However, even without the existence of objects, degradation of links could occur due to atmospheric attenuation, solar radiation, etc., also producing an activation of the alarm system. Since detection is based on the lack of radiation in the detectors, the use of complementary sensors for the same task is justified. Since the minimum size of an object for which an alarm is required to be generated is 50 × 50 × 50 cm, in some situations, several links are interrupted; however, alarms should not be generated. Typical cases are the flight of leaves or the movement of small animals in the scanned area. To avoid alarm activation in such situations, this paper proposes the combined use of diverse techniques of data fusion, based on fuzzy logic and the Dempster-Shafer theory of evidence, to validate the existence of objects, providing a highly reliable detection system.

Ultra-wideband (UWB) technology is currently considered the most attractive solution to develop a Local Positioning System. In fact, the IEEE 802.15.4a standard includes a link (MAC) and physical (PHY) layer with indoor positioning capabilities. This paper presents a study of the typical modulations schemes used with UWB and its adaptation to ultrasonic frequencies. Moreover, several tests of the modulation schemes in the ultrasonic frequency range are shown, in order to apply these techniques in a local positioning system. The main goal is to implement the algorithms in FPGAs using UWB radio technology. Therefore, the use of ultrasonic frequencies can be considered as a previous step of the future work.

The current development of railway transport gives a significant importance to the design of suitable and reliable safety systems. Among them, the common track circuit can be improved to enhance its performance, by providing redundancy, immunity to noise and more information from the track circuit. This work proposes a novel track circuit based on the encoding of the electrical transmissions with Kasami codes. Every node emits its corresponding code that can be detected at next nodes by correlation techniques. The suitable selection of orthogonal sequences, as well as different carrier frequencies for emissions, allow to have simultaneous emissions and receptions without cross interferences.

In this work the specific software tools designed to manage a high performance data acquisition board to be used in active multi-sensory systems is presented. Specifically, the developments which has been carried out allow to monitor the coded emission/reception of two sensory barriers for obstacle detection on railways, based on infrared (IR) and ultrasonic (US) sensors. Additionally, it has been developed the tools to detect the coded emissions. As a result of that, it can be detected the existence of obstacles between both barriers, based on the lack of signal at the receiving barrier. The developed software can be considered the previous step to implement the hardware prototype. Initially it has been validated the feasibility of the algorithm, optimizing the processing time to obtain the detection results in quasi-real time.

In local positioning systems (LPS) active beacons are placed in the environment, so the accurate coordinates of these beacons are necessary for positioning algorithms. Such coordinates or positions can be obtained by means of hand-made measurements (a slow and less flexible method), or by using calibration algorithms (i.e., automatic determination of the beacon coordinates from pre-determined measurements). In this work a new method is presented to estimate the optimal position of the test-points, necessary to calibrate an ultrasonic LPS. The method has been developed for both, spherical and hyperbolic trilateration. The results from simulated and real data shows the improvement obtained when using in the calibration these optimal test-points instead of randomly selected ones.

In this work, the use of the Generalized Principal Components Analysis (G-PCA) to improve the segmentation of moving objects in image sequences is proposed. In order to obtain this improvement, the noise components in the image derivatives are reduced, and afterwards, a method based on linear algebra is used to make the segmentation. Furthermore this work presents diverse tests to compare the results reached with and without the noise reduction in the image derivatives, and using the nonlinear minimization of an error function. A remarkable improvement in the segmentation quality and the processing time can be observed in every experiment when using the proposed method.

This work proposes a data fusion technique to validate the detection of objects in railway environments. The system for obstacle detection is based on an infrared emitting barrier and another of receivers placed on opposing side in the railway, establishing different optical links between them. The interruption of one or several links in the sensorial system should produce an alarm. Since the detection is based on the radiation lack in the detectors, channel degradation can produce a similar situation as if there was one obstacle. Since the interruption of the links is the necessary condition, but not sufficient, for the existence of an object, this work proposes the fusion of the sensors information by means of the Dempster-Shafer evidential theory to obtain a certainty value of the existence of objects supposing a risk for the railway.

The determination of the relative position among mobile objects or members of a robot team is a useful information when the systems are not restricted to a particular environment and they cannot depend on an external infrastructure to obtain their location information. The solution for this kind of problems cannot be divided and treated separately for each object. Therefore it is necessary to implement mechanisms that allow to simultaneously determine the spatial relations among objects not knowing their absolute location in the environment. After that, a positioning algorithm can be computed whose complexity depends on the number of observations made and the precision required in the system. In this work, the implementation of a relative positioning system is described where only acoustic emissions are made in order to obtain information about the other objects. These data are obtained by using simultaneous round-trip-time-of-flight measurements. Finally, using this information, the object positions are computed with a multidimensional scaling technique, and a closer solution is achieved with least-square algorithms.

Signal coding and pulse compression techniques have been widely used in the design of high performance airborne sonar as a mean to increase their robustness to noise and enhance their spatial resolution. These systems are still today in continuous development, with the appearance of new encoding schemes and processing algorithms which not only improve the previous features, but also provide the system with additional properties. All these improvements are achieved at the expense of increasing the computational complexity of the signal processing tasks, what put into risk the real-time operation capability of the system. This work presents the hardware implementation in a single FPGA of all the stages forming the detection module of an advanced sonar system which is based on the emission of complementary sets of sequences. This implementation is able to carry out all the data processing in the time interval between two successive firings, also obtaining a portable module that makes possible its incorporation into mobile robots

This paper presents a reflector recognition and localization technique in three-dimensional (3-D) environments, using only times-of-flight (TOFs) data obtained from ultrasonic transducers. The recognition and localization technique is based on the principal component analysis applied to the TOF vectors originating from a sensor that contains two emitting transducers and several receivers. The two emitters simultaneously transmit two coded pulses that are detected later on and discriminated by the receivers, after being reflected in the environment. The proposed technique allows for the possibility of not only recognizing the reflectors, but also estimating approximately its localization referred to the sensor. This technique has been tested with three types of reflectors in 3-D environments: planes, edges, and corners. The achieved results are very satisfactory for reflectors located in the range 50-350 cm.

The detection of the passage of railway units by certain points of the railway line (mainly in the junctions or bifurcations of the routes), of reliable way, is a functionality of great importance for the safe management of the circulation. Most of the existing systems to that end bases of its operation on the detection of the disturbance that undergoes a magnetic flux (generated by means of coils) to the passage of the wheel of the train. With the system proposed here, we make this detection with the coils located next to the rail of the route, but without additional electronics located in this place. All the electronic systems are concentrated in a point that can be to a considerable distance from the rail. This makes easier the tasks of maintenance of the equipment, in addition to protecting it from the noises and aggressions of the environment. In order to compensate the attenuation of signal in the wires and the external noise, we propose a codification of the signal sent to the emitting coil by means of pairs of Golay complementary sequences.

Recent sonar modules applied to autonomous robots usually consist of several transducers, associated geometrically and electronically to improve performances and results, compared to systems based on only one isolated transducer. Nevertheless, in order to take advantage of these associations, it is necessary to develop new algorithms to consider suitably all the information given by times-of-flight (TOFs) from transducers. This work describes a new method to analyze these TOFs, so they can be used to obtain a heuristic map of the environment. A new operation mode is also proposed to allow the system adaptation to different environmental conditions in run-time.