The system, dubbed Synergistic Physio-Neuro Platform (SynPhNe), was developed by Dr John Heng, a senior research fellow at NTU’s School of Mechanical and Aerospace Engineering and PhD student, Banerji Subhasis.
‘While current rehabilitation systems do benefit many patients, there are also other patients who still have difficulties performing everyday activities like holding a fork or drinking from a cup, despite the usual rehab sessions,’ said Dr Heng.
‘SynPhNe works by giving real-time feedback to the patients on what is happening in their mind and in their muscles. Patients using SynPhNe know where their problems lie and can slowly work towards overcoming each problem, instead of feeling frustrated and going through a painful, expensive and prolonged trial-and-error process when their improvements are not visible.’
SynPhNe consists of patented computer software connected to a specially designed headset with neural sensors and a sensor arm glove.
These sensors provide feedback on the stress, attention, and relaxation levels of the mind and which muscles are being activated or inhibited by the patient. The software contains instructional videos for limb movements which the patient can mimic to improve his or her performance of various tasks.
Sensor information is displayed in real time via the computer screen so that the patient is aware of what is happening in his mind and body while undergoing the rehabilitation exercises.
Dr Heng said in a statement that while multi-model associative learning is known to be useful in the development of babies and in education, it is the first time that their research team is adapting it for stroke therapy. Tested on 10 patients so far, it has shown to be effective in accelerating the recovery in stroke patients.
In associative learning, a patient will find out the link between cause and effect, or intent and physical result. The patient learns what he or she wants to do and what is actually happening with their limbs. This helps the patient to self-correct movements to match intended actions.
‘For example, if a patient wants to move his wrist, but his wrist is not moving, SynPhNe will be able to show him that his mind had sent out a signal, his muscles have received it, but because supporting and opposing muscles are clenched, he will need to relax the opposing muscle in order to move his wrist,’ said Subhasis.
‘Another common problem is that the patient may feel stressed while undergoing therapy, which affects…muscle control. So by showing the stress level on the screen, SynPhNe will teach the patient how to control his breathing and posture to regain his balance and composure so that he can continue with the exercises.
‘In short, SynPhNe makes patients aware of what is happening with their bodies so they learn how to relax their mind and muscles. This helps them to re-learn simple actions like holding a pen or a cup which may be arduous tasks for stroke victims.’
Patient trials are still on-going and 10 patients have undergone the trial for 12 sessions, each lasting 90 minutes. Over a four-week period, they have all shown some improvement on the clinical scales. It was found that patients with hand control and hand weakness problems improved the most, in several cases up to 70 per cent.
As well as further patient trials, the next step for the scientists is to form a start-up company to turn the SynPhNe prototype into a portable stroke therapy kit for home use.
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