Addressing one’s mental health has never been more important. The incidences of mental diseases, such as depression or anxiety disorders, have drastically increased in recent years. The longer an adequate treatment is delayed, the greater the impact on the severity of the illness which often results in long absences from work. With the development of smart devices and wearables, it is already possible to measure many physiological parameters in everyday life. In addition, monitoring people in their natural environment offers many advantages, e.g. it is not based on retrospective feelings and memories but can measure and reflect the momentary state. This conceptual paper presents an overview of possible elements of a system for automated monitoring of mental health characteristics in the home. We describe examples of typical parameters for various mental disorders and present different systems and methods to measure them. Furthermore, we show how the individual components of a system can be connected to get a holistic view of specific mental health characteristics. Finally, we also discuss challenges and limitations.
Hand Gesture Recognition in Daily Life as an Additional Tool for Unobtrusive Data Labeling in Medical Studies. Joch, Julia; Kirsten, Kristina; Arnrich, Bert (2022).
For many use cases, such as supervised machine learning, labeled data is needed. However, to collect information for labels in real-life contexts, scientists are confronted with the challenge of gathering labeled data over an extended period. Labeling this data can become problematic, as constant supervision, similar to a laboratory setting, is neither feasible nor desired. Therefore, participants of such studies have to label their data themselves via appropriate apps on a smartphone. Nevertheless, this process can become very obtrusive in daily life and might even influence the results, especially studies regarding emotions. For example, in studies where participants need to indicate their stress levels frequently, labels get missed in situations where it would be inappropriate to take the phone. Consequently, missing these labels presents a significant problem. This paper aims to provide an unobtrusive solution to labeling data in real-world studies. We recorded a dataset consisting of five gestures and data from daily life. Thereby, we provide a set of predefined gestures that can be distinguished from other everyday life activities by using accelerometer and gyroscope sensors of wearable devices on the wrist. The use of predefined hand gestures for labeling data can therefore serve as an additional tool for the labeling process. Two machine learning approaches were compared and achieved promising results with Matthews Correlation Coefficients of up to 0.789 for a Random Forest and up to 0.835 for a Convolutional Neural Network.
Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.
The mental illness Obsessive-Compulsive Disorder (OCD) is characterised by obsessive thoughts and compulsive actions. The latter can occur as repetitive activities to ensure that severe fears do not come true. A diagnosis of the disease is usually very late due to a lack of knowledge and shame of the patient. Nevertheless, early detection can significantly increase the success of therapy. With the development of new wearable sensors, it is possible to recognise human activities. Accordingly, wearables can also be used to identify recurring activities that indicate an OCD. Through this form of an automatic detection system, a diagnosis can be made earlier and thus therapy can be started sooner. Since compulsive behaviour is very individual and varies from patient to patient, this paper deals with personalised federated machine learning models. We first adapt the publicly available OPPORTUNITY dataset to simulate OCD behaviour. Secondly, we evaluate two existing personalised federated learning algorithms against baseline approaches. Finally, we propose a hybrid approach that merges the two evaluated algorithms and reaches a mean area under the precision-recall curve (AUPRC) of 0.954 across clients.