Abstract
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.
Highlights
Activity can be defined as the state or quality of being active, which implies that the activity can be emotional, intellectual, physical, etc
Selecting a subset of features results mandatory for many activity recognition application. Taking all this into account, the present paper aims at assessing the utility of features extracted from ECG, Thoracic Electrical Bioimpedance (TEB), and Electrodermal Activity (EDA) in activity recognition systems
ECG signal is used in some papers such as [23], where the obtained results suggest that positive emotions lead to alterations in Heart Rate Variability (HRV), which may be beneficial in some illness treatment [19,31,32]
Summary
Activity can be defined as the state or quality of being active, which implies that the activity can be emotional, intellectual, physical, etc. The present work aims at deeply studying several features found in the literature to characterize the signals of electrocardiogram, thoracic bioimpedance and electrodermal activity, whose objective is to recognize four different activities: emotional, mental, physical and neutral activity (resting). In this study several parameters have been analyzed: (a) the physiological sensing mode (ECG, TEB and EDA), (b) the window length, (c) the features extracted from each signal, (d) the number of features to obtain the best results, and (e) the type of classifier. It is possible to find numerous works in which these signals are used to detect stress, emotions, and activity in the literature. TEB is used in some papers, though it is less useful than ECG and EDA signals.
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