Unlike in high-stakes decision-making contexts like medicine or law enforcement, where tabular data is prevalent and commonly available, stock market analysis relies on transparent access to the associated longitudinal data. Similar to the development in different domains, researchers are also attempting to increase predictive performance through the use of artificial intelligence (AI) in the detection of anomalies in time series, thereby reducing the risk of erroneous decisions by human end users. However, the low interpretability of the underlying AI models, if not properly addressed, can also lead to problematic outcomes. If end users cannot detect erroneous reasoning within an AI model’s anomaly detection process, they either tend not to use the system due to their low confidence, or they tend to put too much trust into the system due to not being able to question its outputs. To mitigate both of these problems, researchers have developed Explainable AI (XAI) methods that aim to make AI models scrutable and understandable to human end users. A majority of these methods, though, are intended for use on tabular data.

This thesis project reviews existing XAI methods for time series data in a systematic literature review (SLR). Based on these insights and interviews to elicit requirements for the project, the student develops an AI-based anomaly detection system for stock market data that utilizes a selection of XAI methods found in the SLR. The student evaluates their developed system by conducting a series of expert interviews. These will be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).

Modern smartwatches and smart rings increasingly rely on photoplethysmography (PPG) algorithms to measure vital signs such as heart rate, sleep patterns and stress levels. These sensors are particularly attractive because they are non-invasive and inexpensive, which makes them suitable for integration into everyday life. In recent years, advances in signal processing and machine learning have significantly improved the ability to extract meaningful insights from raw PPG data. For example, the first commercially available smartwatches are now able to measure blood pressure, offering an exciting solution for health monitoring. However, most commercial consumer devices do not allow researchers and individuals to access the raw PPG signal, significantly limiting the potential for innovation.

The aim of this thesis is to first define the potential of PPG-based sensors through a systematic literature review. Subsequently, a prototype will be developed that enables the streaming of raw PPG data in real time.

Note: A PPG sensor is provided for technical implementation.

Smart camera-based monitoring systems are capable of tracking and monitoring human activities. In recent years, systems have been developed that enable contactless monitoring of vital signs such as heart rate and oxygen saturation, capture human behavior such as posture and eye activity, and are able to detect human emotions. In contrast to other monitoring systems, cameras have the advantage of being low-cost and unobtrusive, which facilitates their implementation in different environments. Despite the technological capabilities of these systems, their use in the real world is currently limited. 

Therefore, the aim of this Bachelor thesis is to identify use cases and their potential in real-life scenarios. In order to successfully complete this Bachelor thesis, use cases will first be identified through a systematic literature review and then their potential will be explored through an online survey (e.g. LimeSurvey).

In order to properly utilize performance improvements through the adoption of artificial intelligence (AI) models, a number of conditions must be met. Since modern deep learning systems are opaque and inscrutable to human users, problems of mistrust and corresponding non-use can arise. But even if the adoption of AI technology into clinical practice is not hindered by such barriers, problems may arise due to an attitude of overconfidence and overreliance on AI results. The explainable AI (XAI) community strives to develop methods that help to create an appropriate level of trust in AI systems. Such methods are particularly important in the medical application context, as incorrect diagnostic and prognostic decisions can have significant negative consequences for the patients concerned. We intend to research XAI in the context of medical decision support systems. This includes developing an understanding of the application of XAI to different data types and diseases, and whether there has been experimental evaluation of the impact of XAI in AI-based decision support.

To develop a better understanding of XAI in the context of medical decision support systems, a systematic literature review (SLR) is carried out in this Bachelor’s thesis. To collect additional data and enhance the knowledge about XAI use cases in medical practice, the student conducts a series of expert interviews for requirements elicitation. 

Notifications from instant messaging applications can interrupt employees' productive time. While there are different ways to influence the notification behavior of instant messengers, such as turning off the application or muting notifications for certain periods of time, these measures require self-discipline and/or often result in missing notifications when not in flow. We have developed an adaptive instant messaging blocker that aims to solve this problem by recognizing the user's flow state at predefined intervals, based on their physiological data and using machine learning methods. As soon as a flow state is recognized, the “do not disturb” status is automatically activated for the duration of the flow state.

We conducted interviews with knowledge workers to evaluate the developed system. Therefore, a qualitative analysis (with MAXQDA) is to be carried out in this Master's thesis in order to evaluate the system on the basis of the interviews conducted.