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Detection, Estimation, and Modulation Theory

Este reporte hace una importante contribucion a nuestra comprension de la violencia y su impacto sobre las sociedades. Exhibe los diferentes rostros de la violencia, desde el sufrimiento "invisible" de los individuos mas vulnerables de la sociedad, a la notria tragedia de las sociedades en conflicto. El mismo mejora nuestro analisis de los factores que conducen a la violencia y las posibles respuestas de diferentes sectores de la sociedad. Al hacer esto, este documento nos recuerda que la proteccion y la seguridad no ocurren en forma espontanea, sino que son el resultado de un consenso colectivo y de politicas publicas dirigidas a tal fin.

The World Report on violence and health

Monitoring stress with a wrist device using context.

Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges

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.

https://www.mdpi.com/1424-8220/15/10/25607/pdf

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

Activity and emotion recognition based on physiological signal processing in health care applications is a relevant research field, with promising future and relevant applications, such as health at work or preventive care. This paper carries out a deep analysis of features proposed to extract information from the electrocardiogram, thoracic electrical bioimpedance, and electrodermal activity signals. The activities analyzed are: neutral, emotional, mental and physical. A total number of 533 features are tested for activity recognition, performing a comprehensive study taking into consideration the prediction accuracy, feature calculation, window length, and type of classifier. Feature selection to know the most relevant features from the complete set is implemented using a genetic algorithm, with a different number of features. This study has allowed us to determine the best number of features to obtain a good error probability avoiding over-fitting, and the best subset of features among those proposed in the literature. The lowest error probability that is obtained is 22.2%, with 40 features, a least squares error classifier, and 40 seconds window length.

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study.

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.

/pdf/energy-efficient-acoustic-violence-detector-for-smart-cities-55bza9x3qb.pdf

Energy-Efficient Acoustic Violence Detector 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.

Violence Detection in Real Environments for Smart Cities

Assessing emotional state is an emerging application field boosting research activities on the topic of analysis of non-invasive biosignals to find effective markers to accurately determine the emotional state in real-time. Nowadays using wearable sensors, electrocardiogram and thoracic impedance measurements can be recorded, facilitating analyzing cardiac and respiratory functions directly and autonomic nervous system function indirectly. Such analysis allows distinguishing between different emotional states: neutral, sadness, and disgust. This work was specifically focused on the proposal of a k-fold approach for selecting features while training the classifier that reduces the loss of generalization. The performance of the proposed algorithm used as the selection criterion was compared to the commonly used standard error function. The proposed k-fold approach outperforms the conventional method with 4% hit success rate improvement, reaching an accuracy near to 78%. Moreover, the proposed selection criterion method allows the classifier to produce the best performance using a lower number of features at lower computational cost. A reduced number of features reduces the risk of overfitting while a lower computational cost contributes to implementing real-time systems using wearable electronics.

https://www.mdpi.com/1424-8220/20/1/309/pdf

Inma Mohino-Herranz

Papers

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

Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study.

Energy-Efficient Acoustic Violence Detector for Smart Cities

Violence Detection in Real Environments for Smart Cities

A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors.