Project Area D
Physiological and behavioural modeling and condition monitoring
In project area D, sensor technologies and empatho-kinaesthetic procedures are researched on various diseases and internal states. Important aspects in this project area are the research into medical and psychological body function models for the transformation of the sensor-based EmpkinS measured variables into clinically relevant parameters and the research into new forms of diagnosis and therapy that are based on these parameters and the EmpkinS body function models. The overriding goal is to assign a medical or psychological relevance to the sensory parameters. In order to achieve this, the sub-projects focus on different (patho-) physiological states. These states are characteristic of the disease models examined and include prototypically disturbed body functions, whereby the applicability of the different methods is to be demonstrated.
The aspects considered in the project area are limited, fine motor hand function in rheumatic diseases (D01), facial expressions, posture and movement in depressed patients (D02) and, in the case of D03, stress-associated, pathophysiological changes in the skin, cardiopulmonary function, facial expressions and in general body movement as well as the detection of micro-fasciculation as a stress reaction. Furthermore, gross motor skills and cardiovascular changes are addressed in D04 as a measure of sleep and movement disorders in Parkinson’s disease, whileD05 is devoted to the investigation of motor skills and cardiopulmonary function for the monitoring of palliative patients.
In this way, the perspective transferability of the EmpkinS methodology is evaluated using a broad spectrum of body functions selected as an example: from fine motor skills to gross motor skills and cardiovascular function to the evaluation of psychological functions such as depression and stress reactions. At the same time, different areas of application of medicine are being researched, from diagnostics (D01, D03–D05) to intervention (D02, D04) to prognosis, care and supply support (D04, D05). In the sub-projects it is demonstrated that the empatho-kinaesthetic parameters can depict functional disorders and are therefore a measure of (patho-) physiological processes.
Sub Projects
Under standardized conditions, a comprehensive data set on the current disease status of patients (N = 150) with rheumatoid arthritis (RA) and psoriatic arthritis (PsA) will be collected in D01 in combination with a comprehensive clinical test battery of hand function as well as comparative data from a healthy cohort (N = 75). The parallel acquisition of hand function using state-of-the-art motion capture sensor technology provides a critical basis for the experimental evaluation and integrated data interpretation of the novel sensor data (subprojects A01, A02 and A03) acquired during the acquisition of hand function using EmpkinS.
The aim of the D02 project is the establishment of empathokinesthetic sensor technology and methods of machine learning as a means for the automatic detection and modification of depression-associated facial expressions, posture, and movement. The aim is to clarify to what extent, with the help of kinesthetic-related modifications influence depressogenic information processing and/or depressive symptoms. First, we will record facial expressions, body posture, and movement relevant to depression with the help of currently available technologies (e.g., RGB and depth cameras, wired EMG, established emotion recognition software) and use them as input parameters for new machine learning models to automatically detect depression-associated affect expressions. Secondly, a fully automated biofeedback paradigm is to be implemented and validated using the project results available up to that point. More ways of real-time feedback of depression-relevant kinaesthesia are investigated. Thirdly, we will research possibilities of mobile use of the biofeedback approach developed up to then.
The aim of this sub-project is to make stress measurable in a contact-free way using empathokinesthetic sensor technology. Due to its effects on human health and performance, research into stress has a high priority, but is hampered by costly and mostly invasive traditional recording methods. In D03, empathokinesthetic sensory modalities are being researched in laboratory experimentally induced, acute stress situations, which make it possible to make stress measurable through contactless recording of macro- and micro-movements in collaboration with A02, A04 and A05.
In D04, innovative, non-contact EmpkinS sensor technology using machine learning algorithms and multimodal reference diagnostics is evaluated using the example of Parkinson’s-associated sleep disorder patterns. For this purpose, body function parameters of sleep are technically validated with wearable sensor technology and non-contact EmpkinS sensor technology in comparison to classical poly-sonography and correlated to clinical scales. In an algorithmic approach, multiparametric sleep parameters and sleep patterns are then evaluated in correlation to movement, cardiovascular and sleep phase regulation disorders.
For the first time,D05 is investigating movement as a biomarker, i.e. objectifiable, measurable and quantifiable parameters that have diagnostic and predictive significance for the state of health, well-being and prognosis of palliative patients. These biomarkers are examined with patients in the laboratory and living lab of the palliative care unit. Contactless sensor technology allows scientific access to the last phase of life for the first time. Similarly,D05 is researching the sociological challenges of medical technology innovations in palliative care as a prototype for all health care sectors.