Proprioception (or afferent feedback), is known to play a central role in the regulation of muscle activity. Capturing proprioceptive information is hardly achievable in humans.
Due to the difficulty in recording sensory signals, and the relative ease to harvest their motor counterparts, traditional modeling approaches often neglect the afferent information, focusing only on the description of muscular activity, and assuming the motor output as the net result of sensorimotor integration.
A more complete information about the effects of sensory alteration on motor output will improve the accuracy and reliability of our models.
The goal of this project is to enhance the understanding of the somatosensory system in humans and its objective is determining the contribution of sensory information on motor command. By delivering controlled stimuli to specific somatosensory receptors and evaluating the changes in motor drive, PN2-3A aims at creating a dataset of high quality baseline and perturbed neuromechanical data and isolating the individual and compound effects of afferent information on motor output.
The first perturbation paradigm (Blood Flow Restriction - BFR), is implemented and has been successfully tested on tens of healthy volunteers, the first study of the influence of BFR is published and two are on the way!
The next perturbations (mechanical - vibration and electrical stimulation) have been approved by the Ethical Committee of the University of Stuttgart and are being tested as we write.
Pervasive Simulation And Visualization Under Resource- And Time-Constraints, nickname PerSiVal, revolves around the idea of combining continuum-biomechanics with novel human-machine-interaction concepts, and distributed systems knowledge, to bring a type of simulation that is slow by nature, into a real-time application. Preliminary results of the project show this to be possible. By using different types of neural network (NN) surrogates, real-time evaluation of the surface deformation of the biceps has already been achieved on both a non-high-end PC as well as the augmented reality headset HoloLens 2. Further development of this project could give rise to opportunities to integrate muscle recruitment strategies based on motor-unit-pools into the model, which in turn would allow these simulations to be directly informed by data recorded in the NML.
- A full description of the project is available at the SimTech website:https://www.simtech.uni-stuttgart.de/exc/research/pn/pn7/pn7-1/
- PerSiVal was awarded the prize for best poster at the SimTech Status Seminar 2021:https://www.imsb.uni-stuttgart.de/institute/news/news/SimTech-Status-Seminar-2021-Best-Poster-Award/
Morphological and architectural measures of the muscle such as muscle volume and length, fascicle length and pennation angle are important characteristics as they are related to the muscle’s force-generating capacities and functionality. The “Gold Standard” for obtaining these characteristics are Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI) which is an MRI based technique for fiber tractography. However, MRI comes with drawbacks such as being expensive and not portable, long acquisition times and other limitations such as no metal implants. Ultrasound however overcomes these limitations by enabling shorter acquisition times, being less expensive, portable and not having to consider metal parts.
Usually ultrasound collects high resolution 2D images of tissue. 3D Freehand Ultrasound is an established technique where the transducer scans along the longitudinal axis of a muscle and collects cross-sectional images. Since the transducer is equipped with optical markers (or any type of position tracking device), the position and orientation of the transducer is known for each image frame. By applying a series of coordinate transformations to the image pixel coordinates, a 3D volume of the muscle can be reconstructed.
From this volume, muscle fibers can be enhanced by using a Multiscale-Vessel-Enhancement Filter (MVEF) and by applying 3D line detection algorithms, fascicle directions can be computed.
This method enables fast fiber reconstruction in 3D. The volumes can then serve as input geometries for e.g. FE simulation models.
With our current setup in the Neuromechanics lab, we are able to simultaneously measure the electrical activity of the biceps brachii muscle (BB) with a surface electromyography system, the stiffness of the BB by shear wave elastography and the resulting elbow joint torque during isometric elbow flexion.
Our objective is to investigate the mechanics of BB in relation to elbow joint position and function using shear-wave elastography (SWE). We hypothesized that SWE can detect the changes in mechanical properties of BB, in a passive state for different muscle lengths and during isometric ramp contractions at different activity levels.
We aim to develop SWE as an index of individual muscle force which can be used to improve musculoskeletal models. Future work will investigate other muscles e.g. from the lower limbs.
Muscle tension or pain is clearly noticeable, but we know little about its associated objective changes in the muscle. Ultrasound shear-wave elastography measures muscle elasticity or stiffness and shows a surprisingly inhomogeneous distribution of stiffness in active muscles.
Funded by Hochschulen für Angewandte Wissenschaften (HAW), and in cooperation with the Hochschule Fruthwagen (HFU), this project focuses on the relationship between the mechanical properties of active motor units and their state of activation, also in relation to aging and arthritic muscle pain.
More information can be found at the page of the Hochschule Furthwagen: https://www.hs-furtwangen.de/aktuelles/detail/2262-innovativ-muskelverspannungen-messen/
In cooperation with AAlto University (prof. Ivan Vujaklija).
The main theme of research is the influence of somatosensory integration on motor control. Albeit motor commands can be easily detected and robustly interpreted, sensory feedback (representing over 90% of the information traveling through the nervous system), is still elusive. A better understanding of how the central nervous system perceives the sensory information and how the latter influences motor responses, can have extraordinary consequences on our understanding of the human body, and in the design of unprecedented dexterous human machine interfaces, like orthoses or prostheses.
The project was kicked-off in February 2022.
In cooperation with the University of Genova (prof. Marco Testa).
Fundamental challenge of an aging society is maintaining its members active and independent. Strategic milestones for succeeding in this task, are sustainable programs of physical activity, and an effective policy of short and long-term rehabilitation. If properly used, those translate into longer, healthier, and happier lives, and reduce health-associated costs for individuals and the society.
A punctual evaluation of the efficacy of those policies is however difficult, with the holistic instruments of rehabilitation, and a more systematic approach is necessary.
Mathematical modelling is widely used in domains such as physics, construction and mechanical engineering, economics and social sciences. In the last decades, new models emerged that faithfully describe human neuromechanics (neurophysiology and biomechanics), revealing unprecedented opportunities for understanding the basic functions of the human body and how those can benefit from external interventions.
The aim of the project is to promote a deep and constructive dialog between the domains of
neuromechanics (experimental and in silico) and rehabilitation, by means of 2 main actions:
- The organization of a Summer School and a round table on Neuromechanics and Rehabilitation
- The creation of a series of public outreach initiatives to raise awareness on healthy ageing and systematic evaluation of rehabilitation/training outcomes
Ancillary initiatives (e.g., the publication of the Proceedings of the School) will be used to maximize the impact of the main actions.
This project leverages on the 7th goal of the University of Stuttgart „Emphatic commitment to sustainable development“, and the new born initiative „Campus green“ of the University of Genova.
The project was kicked-off in January 2022.