Research Program

Project area A researches different wave and radio-based sensor technologies for the remote detection of motion parameters of the human body. The targeted new sensor concepts achieve a precision, measuring rate, dynamics and fine-grained resolution in all dimensions (6D pose, 3D body surface and 3D speed with the respective time course of all values) that are at least one order of magnitude better than the current state of the art are.

Coherently phase-sensitive, wave-based sensors such as radar, laser or coherent radio location systems are particularly suitable for measuring both macroscopic and microscopic movement processes remotely with maximum precision, as they are able to evaluate Doppler and micro-Doppler signal phases.

The EmpkinS sensor technologies are selected on the basis that as many different motion parameters as possible can be recorded with the highest possible quality. Since a single sensor cannot optimally record all conceivable parameters equally, complementary sensor technologies are being researched. They cover different types of detection areas (e.g. the entire body shell (sub project A01) or areas of interest such as the face, neck or arm, leg or chest area (A03, A04). Moreover different orders of magnitude of movement (e.g. movements of the limbs (A02) or microfasciculations (A05) e.g. on the face) and / or different areas of application (e.g. measuring vital parameters or the mobility of limbs) are recorded. Depending on the required input variables of the biomechanical neuro- and psychomotor models or depending on the diagnostic question, the different sensor technologies are used individually or in combination.

Project area B researches innovative methods and solutions for efficient sensor signal processing and transfer. All parts of the processing chain such as the sampling of the high-dimensional, continuous, physical signals, the efficient source coding of the scanned sensor values, the energy-saving mobile sensor data transfer and the visualization are addressed.

Specifically, the compressive sampling of 6D radar data is being researched in sub project B03 in order to shorten the recording time of empatho-kinaesthetic radar sensors. For this purpose, the common sparseness of the location and motion data is used and the radar test signals are optimized and adapted with regard to the compressive scanning. In B01 an efficient source coding method for multimodal body shell camera data is explored, which, similar to the mp3 format for audio data, enables efficient compressed transmission and storage of the human body shell data including their motion. For this purpose, the spatial correlation of the depth information and the temporal coherence of the movement should be used in order to reduce the data volume by at least two orders of magnitude. Innovative protocols and algorithms for energy-efficient reference signal and electromyography (EMG) sensor data transmission are researched in B02. In order to meet the application-related, stringent requirements for energy efficiency, the joint design of reference and data signals as well as the integration of concepts for local energy generation are developed. Finally, B04 explores the visualization of motion sequences based on a biomechanical model. Derived states of the biomechanical model make the sensor signals easier to interpret for medical professionals, whereby visualizations should also be generated for patients that support communication. Important research aspects are the spatial registration and deformation of the multimodal sensor data and the biomechanical model as well as the integrated representation of these components.

Project area C explores approaches to processing, modeling and interpretation of biomechanical data. This is an integral part of EmpkinS in order to obtain meaningful movement information on the basis of the large number of available measurement data, which originate from innovative measurement principles and therefore entail completely new requirements, and thus to be able to carry out further analyzes.

In detail, musculoskeletal human models are improved in C01 by personalization using machine learning approaches. This will help to distinguish individual differences in motion characteristics from those that are caused, for example, by neurological or psychological conditions in general. In C02, musculoskeletal human models are used to clean up / filter the measurement data by relating them to the kinematic / dynamic motion possibilities of the human musculoskeletal system. The aim of C03 is then to use the (improved) models and (adjusted) measurement data to investigate a new model of postural control of walking. This model uses measurements of the empatho-kinaesthetic sensory system and a sensorimotor-enhanced musculoskeletal human model to characterize the components of dynamic balance control and to advance research into balance regulation. Finally, in C04, the analysis and prediction of biomechanical simulation models are improved by integrating empatho-kinaesthetic sensor data. This is achieved by integrating measurement data (position, orientation, speed, acceleration, force, muscle activity) into the mathematical formulation of a motion process as an optimal control problem. The selection of the model approaches is based on the requirement to research and provide the highest possible quality analysis options for the consideration of EmpkinS.

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 microfasciculation 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, while D05 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.

Project area E researches the ethical, legal and social issues in the context of EmpkinS and develops orientation marks for its future societal design. In the sense of an “ethics in and by design” approach, the aim is not only to critically accompany technological development, but also to sound out and implement specific design and decision paths. When researching the normative core concepts, the focus is on the concepts of human dignity, informational self-determination, justice and solidarity in times of big data and research and applications driven by algorithms. Furthermore, it is examined which social attitudes and values are affected by EmpkinS and what effects this has on the social evaluation of EmpkinS as well as the weighting of normative criteria.