A system based on Artificial Intelligence (AI) is being created to support upper limb rehabilitation after stroke.
The technology uses electromyography (EMG) to help monitor movements of the fingers, hand or whole arm during rehab, giving insight into muscle movements and whether the tasks are being performed correctly.
The wearable device comprises a bracelet measuring the output of the nerves, muscles and the nerve cells that control them, and is placed on the forearm and used to measure different types of muscle groups in real time.
The system has been created by Lithuanian researchers who previously created the BiomacVR system and a games-based app to support stroke rehab, and now takes that further through the BiomacEMG.
“Our new methodology with EMG elements is particularly important for proper exercise design during a rehabilitation programme,” says Aušra Adomavičienė, a researcher at the Faculty of Medicine at Vilnius University (VU MF).
“With this new technology, you can see exactly which muscle is working and at what capacity, how it reacts to the load and how quickly it recovers.
“This information allows the specialist to work with the patient not blindly, but knowing exactly which muscles are working well, which are overworked, or which are not even tired.”
In her opinion, such technology is not only a great help to the rehabilitation specialist, but it is equally essential for the patient, who, with such technology, can continue the exercises at home, see the progress, and feel safe and secure, without doubts about whether they are doing it right.
“This system is important for patients with musculoskeletal disorders or diseases that require the restoration of lost movement and mobility function, which is directly caused by reduced or lost muscle function,” says Adomavičienė.
The adoption of AI marks yet more use of the technology, the use of which is growing strongly in healthcare and particularly stroke care and rehab.
“I think that after 20 years, patients and professionals involved in rehabilitation will not even be able to imagine that during functional assessment, testing and programme execution, they were guided by the subjective, hand-held instruments that we now use on a daily basis,” says Adomavičienė.
She believes in the future computerised and intelligent systems based on AI strategies will not only be able to identify in detail the problems experienced by the patient, but after the assessment, they will be able to analyse, systematise and provide feedback.
Rytis Maskeliūnas, a researcher at Kaunas University of Technology who also worked on the study, agrees.
“Future research should also look at the development of individualised treatment plans and the adaptation of the algorithm to respond to a wider range of possible actions, taking into account the individual needs of the patient,” he said.
