The ForceLoss study aims to develop personalised modeling and simulation procedures to enable the differential diagnosis for the loss of muscle force, namely dynapenia. Dynapenia can be caused by diffuse or selective sarcopenia, lack of activation, or improper motor control. Each of these causes requires different interventions, but a reliable differential diagnosis is currently impossible. While instrumental methods can provide information on each of these possible causes, it is left to the experience of the single clinician to integrate such information into a complete diagnostic picture. But an accurate diagnosis for dynapenia is important in a number of pathologies, including neurological diseases, age-related frailty, diabetes, and orthopaedic conditions. The hypothesis is that the use of a mechanistic, subject-specific model of maximum isometric knee extension, informed by a number of instrumental information can provide a robust differential diagnosis of dynapenia.

In this preliminary study, on healthy volunteers, the investigators will develop and optimize (i) the experimental protocol and (ii) the modeling and simulation framework, assessing both feasibility and reliability of the proposed procedures. Medical imaging, electromyography (EMG) and dynamometry data will be collected and combined to inform a personalised musculoskeletal model of each participant. Biomechanical computer simulations of a Maximal Voluntary Isometric Contraction (MVIC) task will then be performed. To validate the proposed approach, the models' estimates will be compared to in vivo dynamometry measurements and experimental EMG data.

Inclusion Criteria:

• Body mass index (BMI) between 15 and 30 kg / m²

Exclusion Criteria:

• Neurological, rheumatic or tumoral diseases;
• Pathologies or physical conditions incompatible with the use of magnetic resonance imaging and electrostimulation (i.e., active and passive implanted biomedical devices, epilepsy, severe venous insufficiency in the lower limbs, pregnancy);
• Previous interventions or traumas to the joints of the lower limb.

We plan to share a database of normative experimental data representative of a healthy adult population. The dataset will include the force profiles and the EMG data recorded during the MVIC test, and may include Magnetic Resonance Imaging data. All data will be irreversibly anonymized.

The (anonymized) database of normative values will be made available to the wider biomechanical community upon study completion

Not yet defined