Inma Mohino-Herranz

Assessment of Mental, Emotional and Physical Stress through Analysis of Physiological Signals Using Smartphones

Determining the stress level of a subject in real time could be of special interest in certain professional activities to allow the monitoring of soldiers, pilots, emergency personnel and other professionals responsible for human lives. Assessment of current mental fitness for executing a task at hand might avoid unnecessary risks. To obtain this knowledge, two physiological measurements were recorded in this work using customized non-invasive wearable instrumentation that measures electrocardiogram (ECG) and thoracic electrical bioimpedance (TEB) signals. The relevant information from each measurement is extracted via evaluation of a reduced set of selected features. These features are primarily obtained from filtered and processed versions of the raw time measurements with calculations of certain statistical and descriptive parameters. Selection of the reduced set of features was performed using genetic algorithms, thus constraining the computational cost of the real-time implementation. Different classification approaches have been studied, but neural networks were chosen for this investigation because they represent a good tradeoff between the intelligence of the solution and computational complexity. Three different application scenarios were considered. In the first scenario, the proposed system is capable of distinguishing among different types of activity with a 21.2% probability error, for activities coded as neutral, emotional, mental and physical. In the second scenario, the proposed solution distinguishes among the three different emotional states of neutral, sadness and disgust, with a probability error of 4.8%. In the third scenario, the system is able to distinguish between low mental load and mental overload with a probability error of 32.3%. The computational cost was calculated, and the solution was implemented in commercially available Android-based smartphones. The results indicate that execution of such a monitoring solution is negligible compared to the nominal computational load of current smartphones.

Acoustic Detection of Violence in Real and Fictional Environments

Detecting violence is an important task due to the amount of people who suffer its effects daily. There is a tendency to focus the problem either in real situations or in non real ones, but both of them are useful on its own right. Until this day there has not been clear effort to try to relate both environments. In this work we try to detect violent situations on two different acoustic databases through the use of crossed information from one of them into the other. The system has been divided into three stages: feature extraction, feature selection based on genetic algorithms and classification to take a binary decision. Results focus on comparing performance loss when a database is evaluated with features selected on itself, or selection based in the other database. In general, complex classifiers tend to suffer higher losses, whereas simple classifiers, such as linear and quadratic detectors, offers less than a 10% loss in most situations.

Energy-Efficient Acoustic Violence Detector for Smart Cities

Violence detection represents an important issue to take into account in the design of intelligent algorithms for smart environments. This paper proposes an energy-efficient system capable of acoustically detecting violence. In our solution, genetic algorithms are used to select the best subset of features with a constrained computational cost. Results demonstrate the viability of the system, thanks to the low cost that some violence features require, making feasible the implementation of the proposed method in a nowadays low power microprocessor.

Distributed and collaborative sound environment information extraction in binaural hearing aids

Current research in the field of Wireless Acoustic Sensor Networks (WASN) is gradually introducing the use of sound spatial techniques in the field of binaural hearing aids, in which sound environment information must be extracted in order to tune up the main hearing aid algorithms. In binaural hearing aids, computational capability, memory and data transmission are strictly constrained, which makes the use of distributed and collaborative approaches suitable. This paper proposes solutions for the collaborative and distributed sound environment information extraction through the estimation of the different noise levels, analyzing both the performance and the computational and transmission requirements. Results demonstrate that the proposed distributed solutions highly reduce the transmission rate and the computational cost, while maintaining the accuracy in the estimations.

Synchronization for classical blind source separation algorithms in wireless acoustic sensor networks

The use of wireless acoustic sensor networks is becoming very popular since they entail many advantages. However, this type of distributed sensor networks has an important drawback for many signal processing algorithms, the synchronization problem. Broadly speaking, in those networks, signals received at the different nodes are not synchronized due to two main factors, the clock problem and the important differences in propagation delays between sources and microphones. In this work we introduce a synchronization solution for mixtures of two and three speech sources in the framework of blind source separation. This proposal of synchronization has a mixture alignment stage prior to apply the separation method. Obtained results demonstrate that this synchronization method aligns speech mixtures correctly since it improves the performance of the classical separation algorithm in terms of both speech quality and speech intelligibility.

Violence Detection in Real Environments for Smart Cities

Violence continues being an important problem in the society. Thousands of people suffer its effects every day and statistics show this number has maintained or almost increased recently. In the modern environment of smart cities there is a necessity to develop a system capable of detecting if a violent situation is taking place or not. In this paper we present an automatic acoustic violence detection system for smart cities, integrating both signal processing and pattern recognition techniques. The proposed software has been implemented in three steps: feature extraction in time and frequency domain, genetic algorithm implementation in order to select the best features, and classification to take a binary decision. Results derived from the experiments show that MFCCs are the best features for violence detection, and others like pitch or short time energy have also a good performance. In other words, features that can distinguish between voiced and unvoiced frames seem to be a good election for violence detection in real environments.

TWO-SENSOR EEG-BASED STRESS DETECTION SYSTEM

In this paper, we propose a computationally-efficient EEGbased stress detection that uses only two non-invasive electrodes. The system is designed to classify between two situations: high stress level or low stress level. A linear classifier is trained using supervised learning using a subset of features that has been selected among a larger proposed set of features, using a tailored feature selection algorithm. The proposed algorithm has been evaluated with subjects playing skill games, obtaining errors of 19.2% in the train set and 29.2% in the test set.