Bayesian optimization is a powerful paradigm to optimize black-box functions based on scarce and noisy data. Its data efficiency can be further improved by transfer learning from related tasks. While recent transfer models meta-learn a prior based on large amount of data, in the low-data regime methods that exploit the closed-form posterior of Gaussian processes (GPs) have an advantage. In this setting, several analytically tractable transfer-model posteriors have been proposed, but the relative advantages of these methods are not well understood. In this paper, we provide a unified view on hierarchical GP models for transfer learning, which allows us to analyze the relationship between methods. As part of the analysis, we develop a novel closed-form boosted GP transfer model that fits between existing approaches in terms of complexity. We evaluate the performance of the different approaches in large-scale experiments and highlight strengths and weaknesses of the different transfer-learning methods.

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.

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.

We present a novel sensor system for the support of nutrition monitoring. The system is based on smart table cloth equipped with a fine grained pressure textile matrix and a weight sensitive tablet. Unlike many other nutrition monitoring approaches, our system is unobtrusive, non privacy invasive and easily deployable in every day life. It allows the spotting and recognition of food intake related actions, such as cutting, scooping, stirring, etc., the identification of the plate/container on which the action is executed, and the tracking of the weight change in the containers. In other words, we can determine how many pieces are cut on the main dish plate, how many are taken from the side dish, how many sips are taken from the drink, how fast the food is being consumed and how much weight is taken overall. In addition, the distinction between different eating actions, such as cutting, scooping, poking, provides clues to the type of food taken and the way the meal is consumed. We have evaluated our system on 40 meals (5 subjects) in a real life living environment: for seven eating related actions (cutting, scooping, stirring, etc.), resulting in above 90% average recognition rate for person dependent cases, and spotting each action out of continuous data streams (average F1 score 87%).

This paper presents a mobile and unobtrusive platform that enables the accurate monitoring of physical activities in daily life, and is integrated into a healthcare system supporting out-of-hospital services. The main focus of the paper is to describe and evaluate a complete data processing chain for recognizing different activities, and estimating their intensity level. To develop a mobile platform that is more applicable in real-life scenarios than systems in previous work, an additional other activity class is introduced to deal with various everyday, household and fitness activities. Evaluation of the proposed methods is done on a dataset recorded by 9 subjects performing 16 different activities, while wearing the mobile platform consisting of three IMUs, a HR-monitor and a mobile companion unit. Another important aspect of this work is the integration of the mobile platform with an EHR, providing access for both the clinician (e.g. to enter a patient's medical record or to set up a care plan) and the patient (e.g. to watch assigned educational material). Feedback about the patient's daily progress is also given both to the patient and the clinician, preserving or even increasing the patient's motivation to follow the defined care plan, and providing valuable information on program adherence for the clinician.

This paper discusses several aspects and practical issues of physical activity recognition. Many existent activity recognition applications only include the few and well known basic activities, thus limiting the applicability of these systems. One of the main goals of this paper is to point out the importance of extending activity recognition with background activities, and to demonstrate its effects. Another practical issue of activity recognition is, that the comparison of different approaches is often not possible not only because of the lack of common datasets, but also because of the usage of different evaluation methods. This work argues, that usually subject independent validation techniques should be applied for the evaluation of activity recognition systems. The statements of this work are demonstrated on a dataset with 8 subjects and 13 different activities. Finally, an empirical study carried out within this work shows the feasibility of using more complex classifiers (required due to the extended activity recognition problem) for mobile applications.

This paper addresses two fundamental requirements of full body motion monitoring: (a) the ability to sense the input of the user and (b) the means to interpret the captured input. Appropriate technology in both areas is required for an interactive virtual reality system to provide feedback in a useful and natural way. This paper combines technologies for both areas: It develops a sensor fusion approach for capturing user input based on miniature on-body inertial and magnetic motion sensors. Furthermore, it presents work in progress to automatically generate models for motion patterns from the captured input. The technology is then used and evaluated in the context of a personalized virtual rehabilitation trainer application.