In order to detect a person's individual momentary thermal sensation and comfort, an increased number of models are developed that involve physiological data, especially skin temperatures and features based on the heart activity. In this paper, we investigate the feasibility of Machine Learning (ML) models which include physiological data based on two data sources: (1) a smartwatch and (2) a portable chest belt device. Further, we investigate the difference between thermal sensation and comfort votes and propose a new combined ground truth label. Data were collected in lab-like studies in a single-bed hotel room. Our study focuses on the detection of cold-induced thermal discomfort by varying the room air temperature between 24 °C and 20 °C. Results show that ML based approaches lead to accuracies up to 83.1% when using a chest belt device. Using a smartwatch, accuracies drop down to 79.8%. Our investigation shows that even though Heart Rate (HR) based features improve the prediction accuracy, the highest feature importances have distal skin temperature features.

Affective computing aims to detect a person's affective state (e.g. emotion) based on observables. The link between affective states and biophysical data, collected in lab settings, has been established successfully. However, the number of realistic studies targeting affect detection in the wild is still limited. In this paper we present an exploratory field study, using physiological data of 11 healthy subjects. We aim to classify arousal, State-Trait Anxiety Inventory (STAI), stress, and valence self-reports, utilizing feature-based and convolutional neural network (CNN) methods. In addition, we extend the CNNs to multi-task CNNs, classifying all labels of interest simultaneously. Comparing the F1 score averaged over the different tasks and classifiers the CNNs reach an 1.8% higher score than the classical methods. However, the F1 scores barely exceed 45%. In the light of these results, we discuss pitfalls and challenges for physiology-based affective computing in the wild.

Seismocardiography (SCG) is a cardiac diagnostic method which evaluates vibrations of the chest via accelerometers. Current prototypical wearable SCG sensor patches are combined with electrocardiography (ECG) sensors which enables the estimation of cardiac timing intervals such as pre-ejection period (PEP). We envision a system with only SCG sensors to increase the battery run time and wearing comfort. Therefore, we present a method to predict the timing of ECG R-peak based on SCG data. The developed machine learning (ML) algorithms were evaluated with respect to algorithm complexity, body postures, and personalization schemes, on a dataset consisting of 10 subjects. Our findings show that R-peaks can be predicted with a mean error of 4:16 ms for supine, 9:43 ms for sitting, and 14:3 ms for standing posture. While personalization results only in minor improvements, using more complex ML classifier is beneficial for standing posture, which is more affected by motion artifacts.

Affect recognition aims to detect a person's affective state based on observables, with the goal to e.g. improve human-computer interaction. Long-term stress is known to have severe implications on wellbeing, which call for continuous and automated stress monitoring systems. However, the affective computing community lacks commonly used standard datasets for wearable stress detection which a) provide multimodal high-quality data, and b) include multiple affective states. Therefore, we introduce WESAD, a new publicly available dataset for wearable stress and affect detection. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects during a lab study. The following sensor modalities are included: blood volume pulse, electrocardiogram, electrodermal activity, electromyogram, respiration, body temperature, and three-axis acceleration. Moreover, the dataset bridges the gap between previous lab studies on stress and emotions, by containing three different affective states (neutral, stress, amusement). In addition, self-reports of the subjects, which were obtained using several established questionnaires, are contained in the dataset. Furthermore, a benchmark is created on the dataset, using well-known features and standard machine learning methods. Considering the three-class classification problem ( baseline vs. stress vs. amusement ), we achieved classification accuracies of up to 80%,. In the binary case ( stress vs. non-stress ), accuracies of up to 93%, were reached. Finally, we provide a detailed analysis and comparison of the two device locations ( chest vs. wrist ) as well as the different sensor modalities.