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Researchers at the Technical University of Munich (TUM) have developed a system that helps patients learn to move their paralyzed arms and hands quickly after a stroke. This requires targeted stimulation of the muscles in the forearm and the support of an exoskeleton. Twenty-four stroke patients have already tested the system at the Schön Klinik Bad Aibling.
The researchers use functional electrical stimulation (FES) to stimulate specific muscles in the forearm. This is necessary, for example, for moving fingers, grasping objects or catching a ball. However, one-sided paralysis following a stroke usually affects not only the hand, but the entire side of the body. For that reason, a scaffold also supports the entire arm up to the shoulder.
Modular system with computer game: independent training
Twenty-four stroke patients have already used the complete system, consisting of an exoskeleton for the arm and shoulder in combination with FES as part of the ReHyb research project. Half of them were patients at the Schön Klinik Bad Aibling Harthausen, which is leading the study. The researchers also used a computer game that automatically adapts to the individual player’s capabilities. It trains them to grip and move their arms shortly after a stroke by reacting to colored balls flying toward them at varying speeds on a screen. The task is to catch the balls and match them with color-coded boxes.
The secret of success: digital twin of muscle activity, muscle stimulation strength and exoskeleton
At the center of TUM Professor Sandra Hirche’s setup is a digital twin that records the individual requirements of each patient and places them in a control loop. Among other things, the researchers have to determine how well each patient can move their arm and hand. In the event of a stroke, for example, paralysis can be caused by damage to the motor area in the brain responsible for movement. However, it is impossible to predict how severely the signals transmitted from the brain to the muscles in the forearm will be impaired after the stroke. “Individual muscle strands in the forearm can be stimulated to the right extent for hands and fingers to move,” says Prof. Hirche, who holds the Chair of Information-Oriented Control at TUM. In addition to information on muscle activity in the forearm, the researchers need to know how strongly the muscles should be stimulated in conjunction with the exoskeleton assistance. “We use algorithms to bring this individual information together in a control loop,” says the control engineering expert. Consequently, the digital twin is needed to provide individualized support for the arm and hand movements of affected persons.
Schön Klinik: modular system as a home trainer
Prof. Hirche uses the phrase “intention-controlled intelligent control” to refer to the fact that patients can use this technology to move as much as they want after a stroke. Carmen Krewer, team lead of the research group at the Schön Klinik cooperation partner in Bad Aibling, enthuses: “Such a modular system with electrical stimulation and exoskeleton has never existed. It also enables stroke sufferers to continue training at home without the support of others.”
Additional material for media outlets:
- Features of the digital twin
o Muscle activity recording: The musculoskeletal system, motor control and the muscular nervous system are each affected to varying degrees by a stroke. By measuring the electrical voltage in the muscle, it is possible to determine the extent of damage to nerves that function to transmit signals from the brain to the muscles and that are ultimately responsible for moving the fingers and hands.
o Forearm muscle stimulation: For functional electrical stimulation (FES), a film with 32 electrodes is attached to the forearm. Individual fingers move depending on which electrodes are activated and the hand stretches or contracts. The threshold value at which the fingers and hand start to move can be individually adjusted.
o Exoskeleton support: An exoskeleton makes it easier for those affected to move and rotate their arm or shoulder using a spring mechanism or specific motors, for example. This support is necessary, as the disease weakens the muscles in the arms. It would also be challenging to attach electrodes to the shoulder, for example, to activate the correct muscles. The exoskeleton helps patients learn to move their hands, arms and shoulders together in a coordinated manner.
- Glove is based on the same principle as the “Exoglove”:
A study on a glove that healthy people can wear as an exoskeleton also shows how FES and exoskeletons can be successfully combined in a hybrid system. It was developed by Prof Lorenzo Masia, Director of the Munich Institute of Robotics and Machine Intelligence (MIRMI) at TUM. In healthy test subjects, the mobility of the fingers increased at least twofold compared to purely electrical stimulation with the help of the motorized glove and even threefold in the case of the thumb. See also: Toward Dexterous Hand Functional Movement: Wearable Hybrid Soft Exoglove-FES Study; H. Kavianirad, F. Missiroli, S. Endo, L. Masia, S. Hirche; 2024; https://ieeexplore.ieee.org/document/10719731
- More about the EU project ReHyb (Rehabilitation based on hybrid neuroprosthesis): https://rehyb.eu/
- Foto zum Download: https://mediatum.ub.tum.de/1773375
- VIDEO: https://youtu.be/OboVMCkM9Yk
Further information:
Prof Sandra Hirche is involved in the Munich Institute for Data Science (MDSI) and the Munich Institute of Robotics and Machine Intelligence (MIRMI). Further information on the institutes: https://www.mdsi.tum.de/en/mdsi/home/ and https://www.mirmi.tum.de/mirmi/startseite/.
Prof. Sandra Hirche
Technical University of Munich
Chair of Information-oriented Control
hirche@tum.de
Framework for Learning a Hand Intent Recognition Model from sEMG for FES-Based control; N. Das, S. Endo, H. Kavianirad, S. Hirche; 2024; https://ieeexplore.ieee.org/document/10719910
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