Detection of acute stress caused by cognitive tasks based on physiological signals

We report on the development of an automated detector of acute stress based on physiological signals. Our detector discriminates between high and low levels of acute stress accumulated by students when performing cognitive tasks on a computer. The proposed detector builds on well-known physiological signal processing principles combined with the state-of-art support vector machine (SVM) classifier. The novelty aspects here come from the design and implementation of the signal pre-processing and the feature extraction stages, which were purposely designed and fine-tuned for the specific needs of acute stress detection and from applying existing algorithms to a new problem. The proposed acute stress detector was evaluated in person-specific and person-independent experimental setups using the publicly available CLAS dataset. Each setup involved three cognitive tasks with a dissimilar crux of the matter and different complexity. The experimental results indicated a very high detection accuracy when discriminating between acute stress conditions due to significant cognitive load and conditions elicited by two typical emotion elicitation tasks. Such a functionality would also contribute towards obtaining a multi-faceted analysis on the dependence of work efficiency from personal treats, cognitive load and acute stress level.

A Wavelet Adaptive Cancellation Algorithm Based on Multi-Inertial Sensors for the Reduction of Motion Artifacts in Ambulatory ECGs

Wearable electrocardiogram (ECG) devices are universally used around the world for patients who have cardiovascular disease (CVD). At present, how to suppress motion artifacts is one of the most challenging issues in the field of physiological signal processing. In this paper, we propose an adaptive cancellation algorithm based on multi-inertial sensors to suppress motion artifacts in ambulatory ECGs. Firstly, this method collects information related to the electrode motion through multi-inertial sensors. Then, the part that is not related to the electrode motion is removed through wavelet transform, which improves the correlation of the reference input signal. In this way, the ability of the adaptive cancellation algorithm to remove motion artifacts is improved in the ambulatory ECG. Subsequent experimentation demonstrated that the wavelet adaptive cancellation algorithm based on multi-inertial sensors can effectively remove motion artifacts in ambulatory ECGs.

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

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 s window length.