Prof.dr.ir. M. Sartori | People pages: Vind een medewerker en alle contactgegevens

Tijdens een inspirerend promotie-onderzoek en postdoc aan zowel de Universiteit van Padova, Stanford University en het Universitair Medisch centrum Goettingen, verwierf Massimo Sartori fundamentele kennis in computationele neuromechanica wat hem motiveerde fundamentele vragen over mens en machine interactie te beantwoorden. Prof.dr.ir. Sartori was gast redacteur voor IEEE Transactions on Biomedical Engineering en Frontiers in Computational Neuroscience en leidde onderzoeksactiviteiten in nationale en Europese projecten. Tussen 2014 en 2018 ontving hij de NIH- OpenSim Fellowship, de NCSRR Outstanding Research Award, een Marie-Skłodowska-Curie Individual Fellowship en een ERC Starting Grant. Hij is momenteel een associate-hoogleraar aan de Universiteit van Twente en leidt een groeiende onderzoeksgroep.

Expertises

Computer Science
- Models
- Control
- Robotics
Medicine and Dentistry
- Muscle
- Electromyography
- Joint
- Exoskeleton
Engineering
- Joints (Structural Components)

Organisaties

Nevenwerkzaakheden

European UnionReviewer for EU Project COMPASS-NMD

Publicaties

Jump to: 2025 | 2024

2025

Predictive Control for Bio-Protective Robotic Exoskeletons: Closed-Loop Control of Internal Body Forces (2025)In Converging Clinical and Engineering Research on Neurorehabilitation V: Proceedings of the 6th International Conference on Neurorehabilitation (ICNR 2024), November 5–8, 2024, La Granja, Spain - Volume 1 (pp. 153-157) ( Biosystems & Biorobotic; Vol. 31). Nabipour, M., S. Sawicki, G. & Sartori, M.https://doi.org/10.1007/978-3-031-77588-8_30Towards Predictive Control of Trunk Internal Loads: Modeling Musculotendon Loads and Predicting Muscle Excitations (2025)In Converging Clinical and Engineering Research on Neurorehabilitation V: Proceedings of the 6th International Conference on Neurorehabilitation (ICNR 2024), November 5–8, 2024, La Granja, Spain - Volume 1 (pp. 164-168) ( Biosystems & Biorobotics; Vol. 31). Li, Y., Sartori, M. & Nabipour, M.https://doi.org/10.1007/978-3-031-77588-8_32Soft back exosuit controlled by neuro-mechanical modeling provides adaptive assistance while lifting unknown loads and reduces lumbosacral compression forces (2025)Wearable Technologies, 6. Article e9. Moya-Esteban, A., Refai, M. I., Sridar, S., van der Kooij, H. & Sartori, M.https://doi.org/10.1017/wtc.2025.3Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuation (2025)Wearable Technologies, 6. Nabipour, M., S. Sawicki, G. & Sartori, M.https://doi.org/10.1017/wtc.2025.1Assessing low-back loading during lifting using personalized electromyography-driven trunk models and NIOSH-based risk levels (2025)Frontiers in bioengineering and biotechnology, 13. Article 1486931. Refai, M. I., Varrecchia, T., Chini, G., Ranavolo, A. & Sartori, M.https://doi.org/10.3389/fbioe.2025.1486931Towards Wearable Electromyography for Personalized Musculoskeletal Trunk Models Using an Inverse Synergy-Based Approach (2025)IEEE Transactions on Medical Robotics and Bionics. Rook, J. W. A., Sartori, M. & Refai, M. I.https://doi.org/10.1109/TMRB.2024.3503900CEINMS-RT: an open-source framework for the continuous neuro-mechanical model-based control of wearable robots (2025)[Working paper › Preprint]. TechRxiv. Sartori, M., Refai, M. I., Avanci Gaudio, L., Cop, C. P., Simonetti, D., Damonte, F., Hambly, M., Lloyd, D., Pizzolato, C. & Durandau, G. V.https://doi.org/10.36227/techrxiv.173397962.28177284/v1

2024

Person-specific modelling of α-motoneurons: Towards customized neurorehabilitation (2024)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Ornelas Kobayashi, R. E.https://doi.org/10.3990/1.9789036562911Optimization of a Synergy-Driven Musculoskeletal Model to Estimate Muscle Excitations and Joint Moments of the Ankle Joint at Different Walking Speeds (2024)In 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) (pp. 575-580). Article 10719730. IEEE. Damonte, F., Khanapuri, V. V., Durandau, G. & Sartori, M.https://doi.org/10.1109/BioRob60516.2024.10719730Predictive Closed-Loop Control of Musculotendon Force: Towards a Framework for Human Locomotion (2024)In 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob). Nabipour, M., S. Sawicki, G. & Sartori, M.https://doi.org/10.1109/BioRob60516.2024.10719784Towards Personalized Motor-Restoring Technologies: Characterizing Neural Data-Driven Alpha-Motoneuron Model Parameters (2024)In 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) (pp. 1581-1586). Ornelas-Kobayashi, R., Mooiweer, R. & Sartori, M.https://doi.org/10.1109/BioRob60516.2024.10719875Towards Real-Time Decoding of Motor Unit Firing Events and Resulting Muscle Activation During Human Locomotion and High-Force Contractions (2024)In 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2024 (pp. 1434-1439) (Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics). IEEE. Gogeascoechea, A., Mohamed Refai, M. I., Yavuz, U. S. & Sartori, M.https://doi.org/10.1109/BioRob60516.2024.10719929Personalized Alpha-Motoneuron Pool Models Driven by Neural Data Encode the Mechanisms Controlling Rate of Force Development (2024)IEEE transactions on neural systems and rehabilitation engineering, 32, 3699-3709. Ornelas-Kobayashi, R., Gomez-Orozco, I., Gogeascoechea, A., Asseldonk, E. V. & Sartori, M.https://doi.org/10.1109/TNSRE.2024.3467692Towards Wearable Electromyography for Personalized Musculoskeletal Trunk Models using an Inverse Synergy-based Approach (2024)[Working paper › Preprint]. bioRxiv. Rook, J. W., Sartori, M. & Refai, M. I.https://doi.org/10.1101/2024.07.23.603973Alpha to Tau: Mapping the Neuromechanical Continuum from α-Motor Neuron Firing Behavior to Joint Torque (2024)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Gogeascoechea, A.https://doi.org/10.3990/1.9789036561853Neuromuscular Modeling for Locomotion with Wearable Assistive Robots -- A primer (2024)[Working paper › Preprint]. ArXiv.org. Refai, M. I., Wang, H., Gogeascoechea, A., Ornelas Kobayashi, R., Avanci Gaudio, L., Damonte, F., Durandau, G. V., van der Kooij, H., Yavuz, U. S. & Sartori, M.https://doi.org/10.48550/arXiv.2407.14289Peak-delay method for estimating the average motor unit action potential conduction velocity (2024)[Dataset Types › Dataset]. Zenodo. Brouwer, N. P., Tabasi, A., Kingma, I., Stegeman, D. F., van Dijk, W., Moya-Esteban, A., Sartori, M. & Dieën, J. H.https://doi.org/10.5281/zenodo.11358224Predictive control of musculotendon loads across fast and slow-twitch muscles in a simulated system with parallel actuation (2024)[Working paper › Preprint]. bioRxiv. Nabipour, M., S. Sawicki, G. & Sartori, M.https://doi.org/10.1101/2024.05.14.594110Electromyography-driven musculoskeletal models with time-varying fatigue dynamics improve lumbosacral joint moments during lifting (2024)Journal of biomechanics, 164. Article 111987. Mohamed Refai, M. I., Moya-Esteban, A. & Sartori, M.https://doi.org/10.1016/j.jbiomech.2024.111987Toward a wearable gait lab for the quantitative assessment of musculoskeletal function (2024)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Simonetti, D.https://doi.org/10.3990/1.9789036558853

Onderzoeksprofielen

Verbonden aan opleidingen

Vakken collegejaar 2024/2025

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.

Vakken collegejaar 2023/2024

Lopende projecten

ROBOREACTOR: Robotic bioreactors for the longitudinal control of restorative remodelling in the human skeletal muscle

SMARTSENS

ERC Proof of Concept Grant: Smart wear for sensing the neuromusculoskeletal system during human movement in vivo

Neurological injuries such as stroke or spinal cord injury, leave 5 million people disabled worldwide annually, drastically impairing individuals' ability to move independently. The main element hampering efficacy of current neuro-rehabilitation procedures is the inability of sensing the activity of neural cells involved in the control of movement, along with the movement-generating mechanical force produced by innervated muscle-tendon units, in the intact moving human in vivo. Current technologies for sensing the neuromusculoskeletal system rely on expensive, large, and bulky sensing devices that can only be used in the highly controlled settings of research laboratories. Therefore, a wearable, rapid-to-wear system that could track function in a person’s motor neuron activity along with associated function in muscle, tendon and joint function would revolutionise current neuro-rehabilitation paradigms. SMARTSENS proposes a fully wearable, non-invasive solution to monitor a range of clinically relevant neuromuscular parameters, which currently could only be extracted in constrained laboratory settings via lengthy procedures. SMARTSENS enables measuring such information during daily life activities using a sensorised smart wear that is unobstructive and rapid to wear. This will enable continuous monitoring of the human neuromusculoskeletal system, which will disrupt current movement-measuring and diagnostic systems, by enabling causal understanding of the activity of neural and musculoskeletal structures in vivo at a resolution not considered before.

INTERACT: Modelling the neuromusculoskeletal system across spatiotemporal scales for a new paradigm of humanmachine motor interaction

INTERACT

Ieder jaar worden er miljoenen mensen door neurologisch letsel zoals een beroerte of ruggenmergletsel uitgeschakeld. Voor deze personen is herstel nog niet optimaal. De impact van de neurorevalidatiemachines van vandaag de dag wordt belemmerd door een beperkte kennis van hun fysieke interactie met het menselijk lichaam. Motorisch herstel kan alleen worden bereikt als positieve neuromusculaire aanpassingen gedurende een langere tijd worden gestuurd. Als we dergelijke aanpassing zouden kunnen voorspellen en beheersen om in de toekomst een positieve verandering teweeg te brengen, zou een nieuw tijdperk in revalidatie-robotica beginnen. INTERACT zal deze uitdaging aangaan door elektrische stimulatie van het ruggenmerg en de robot- exoskeletten te combineren met een nieuwe klasse van voorspellende multischaal modellen van het neuromusculaire systeem. Hierdoor kunnen robots autonoom de elektro-mechanische stimuli ontdekken die nodig zijn om de motorische functie in de loop van de tijd te herstellen. INTERACT zal fundamentele vragen over bewegingsneuromechanica via nieuwe beginselen van interactie tussen mens en machine beantwoorden, en heeft hiermee een brede impact op bio-engineering en robotica.

S.W.A.G.: Soft wearable assistive garments for human empowerment

Horizon Europe (CL4-Digital Emerging): Soft wearable assistive garments for human empowerment

Soft robotics has become one of the fastest growing fields over the last decade, and the development of technologies related to the associated modelling, sensing, actuation and control challenges has flourished as part of the field’s impetus. Soft robots have been demonstrated in diverse applications such as wearable devices, mobile or locomotive robots, as well as soft manipulators. Soft lower extremity exoskeletons ( “soft wearable robotics (SWRs)) are one of the most challenging research topics, and require multidisciplinary approaches involving diverse fields such as neuroscience, biomechanics, robot control, ergonomics and other fields. SWAG aims to explore a fundamentally new approach to engineering soft structures that omit fully rigid materials for inflatable ones made from high-strength fabrics and films when manufacturing human-assistive exoskeletal devices that target strainprone segments of the human body (i.e. lower body and core). Such soft wearable adaptive garments with actuation capabilities offer higher variable stiffness and force-to-weight ratios compared to other existing methods, and simultaneously entirely conform to each joint’s intricate kinematics. Because of this, new design approaches can be used as building blocks to realise complete assistance for multi-degree-of-freedom joints, such as the ankle or hip, by adapting flexible and conforming motions achieved by continuum robot designs. SWAG’s advances will demonstrated in 4 different application scenarios. The project brings together 13 partners from 5 EU countries and the UK. The partners consist of an interdisciplinary combination of leading academics with very strong track records in their respective fields. They are supported by RTOs with demonstrated capabilities of developing and validating application-driven solutions, as well as two commercial partners aiming to lead the exploitation of SWAG’s outcomes.

MyoStride: portable HDEMG-driven digital human twins in the clinical practice

In the Netherlands, 265,000 patients suffer every year from neurological or neuromuscular disorders, such as a stroke, spinal cord injuries or cerebral palsy. These conditions have a major impact on mobility due to, among other things, reduced muscle functions in the lower leg. Rehabilitation therefore focuses mainly on restoring these muscle functions and preventing further deterioration. However, the precise effectiveness of treatment cannot be properly measured. Practitioners can only rely on their observations, but have no insight into the activity of muscle groups. This leads to suboptimal and inefficient care. As a result, rehabilitation often takes longer than necessary and recovery is not optimal. The consortium wants to change this with the development of a wearable device to measure the activity of the muscle groups in the lower leg during rehabilitation. The basis for this is a prototype technology of a 'smart sock': the MyoStride. This was developed by TMSi, the Sint Maartenskliniek and the University of Twente in a previous ERDF project into a validated prototype at TRL level 5. They want to further develop and demonstrate this technology in this project, together with Moveshelf. The goal is to realize a new prototype that is, among other things, completely wireless, can be automatically calibrated and is integrated with the practitioner's IT systems. The developments are taking place in phases in the R&D departments of the SMEs involved and are tested in a practical setting in the Sint Maartenskliniek. In the first phase, the emphasis is on collecting additional data on the use of the technology in patients with a stroke, spinal cord injury, cerebral palsy or muscle disease. The developers use this data to improve the technology and test its suitability for these different patient groups. In the second phase, the new MyoStride will be used in at least 50 patients from these groups. The intended result is a proven and scalable technology, ready for medical certification. The intended technology makes it possible to accurately monitor the muscle activity of patients during a rehabilitation process in both rehabilitation clinics and physiotherapy practices. This gives the practitioners concrete insight into (the lack of) progress, so that they can adjust the treatments accordingly. Moreover, this ensures a much better transfer of data between the clinic and the physiotherapist. It is expected that the technology can prevent approximately 500,000 rehabilitation treatments annually and help save €20 million in healthcare costs.