Driver somnolence remains a major challenge for road safety, not only for its detection, but especially for forecasting when drowsiness will impair driving performance. To address this matter, various physiological signals and facial images are employed to identify signs of sleepiness. However, predicting the driver’s drowsiness condition within a few minutes earlier is more complex than classifying their current status. This study introduces a novel forecasting method based on BiLSTM (Bidirectional Long-Short-Term Memory) to predict when a driver will reach a predefined drowsiness threshold within a seven-minute window. A set of non-intrusive sensors, including force-sensing resistors (FSR) and vehicle measurements (Telemetry data), alongside physiological data (EEG, ECG, EMG), is employed to detect and forecast the upcoming drowsy events. Moreover, a combination of drowsiness detectors based on regression models and a ResNet architecture was implemented to evaluate the performance of these models. This multimodal database was collected from 30 volunteer drivers in a controlled virtual driving environment using a driving simulator in three different scenarios. The results of this study allow evaluation of whether the performance of the BiLSTM model is enhanced when compared to non-intrusive sensor data. In comparison to existing classification-based approaches, the proposed BiLSTM forecasting model demonstrated superior predictive outcomes, reducing classification error rates and improving accuracy in forecasting drowsiness events. This improvement highlights the advantage of integrating regression-based detection with time-series forecasting, thereby enhancing the reliability of driver monitoring systems. Furthermore, the best regression model achieved a test accuracy of 0.964, while the best-performing forecasting model scored 0.86 on the same metric. Notably, the entirely non-intrusive FSR alternative achieves a promising detection accuracy of 0.905. These findings demonstrate the feasibility of using time-series data, non-intrusive sensors, and a forecasting technique to predict upcoming drowsiness events, enabling a practical alternative for continuously monitoring the drowsiness status of drivers.

Gait, a complex process unique to everyone, has been extensively studied in fields such as medicine, rehabilitation, and biomechanics. Gait laboratories, like those at Teletón centers, play a crucial role in analyzing and diagnosing human movement patterns during locomotion. This report focuses on standardizing data from different gait laboratories. This initiative addresses the lack of uniformity, which hinders the comparison and exchange of information between different rehabilitation centers. The proposal is part of the Movement Analysis Network project, an initiative by Politecnico di Milano, Oritel, and Universidad de Concepción, promoting collaboration, research, and continuous improvement in movement analysis. The proposed methodology particularly focuses on standardizing data acquired from BTS GaitLab and Vicon Systems. To achieve an effective comparison between data from these two systems, it is essential to define uniform labels and automate the processes that allow for the comparison of the information contained in the files. For this purpose, our script takes C3D and EMT files as input and generates table-formatted files separated by study type as output. Then, those files are used as input for a NoSQL database. This database facilitates further data comparison. This work enables the creation of standardized databases, which can be used to improve the effectiveness and reliability of diagnoses and treatments related to gait pathologies. In a future work, machine learning techniques could be used for gait pattern classification, creating new opportunities for more precise and personalized medical care, thereby positively impacting patients' quality of life by tailoring treatments to their specific needs.

"Emotion recognition is an expanding field with applications in psychology and neuroscience. Pupillometry has proven to be an effective technique for assessing emotional responses due to its high temporal resolution and its ability to reflect autonomic nervous system activity. This study explores how pupil characteristics contribute to identifying emotional states in response to visual stimuli. First, a clustering analysis based on affective dimensions (Approach, Commitment, and Identity) was performed. Then, a segmentation based exclusively on pupillary characteristics was applied, allowing for the identification of significant differences in parameters such as dilation amplitude, contraction latency, and pupil variability. For more robust statistical results, we use generalized linear mixed models (GLMMs). The results confirm that pupillometry is an effective tool for distinguishing emotional states, reinforcing its usefulness as a non-invasive method for emotional assessment. Pupillom-etry alone shows a significant relationship to the emotional dimensions of Commitment and Attention. Additionally, the importance of controlling external factors such as lighting and eye movement is highlighted to improve analysis accuracy."