Muscle Strain Sensor Developed

For most athletes "no pain, no gain" is a philosophy to live by. However, too much pain can be bad, as excessive muscle fatigue could lead to serious injury. The difficulty is that athletes rely on their own perception of muscle fatigue when training, and often it is too late by the time they feel sore.

A new piece of kit has been devised by Mohamed Al-Mulla, a PhD researcher at Essex University’s School of Computer Science and Electronic Engineering. The ‘iSense’ allows athletes to train without risking injuring their muscles due to overexertion. The iSense works by predicting and detecting the status of muscles during training. Sensors measure small electrical signals muscles produce when they contract. If the device detects that there is too much strain on a muscle, it will warn the user.

Localised muscle fatigue can be beneficial in promoting muscle growth or potentially harmful, causing strain and injury. Often it’s a fine line and timing is crucial, said Al-Mulla. ‘What I want to do is build a bridge between the brain and the muscle,’ he added. ‘When you go the gym, why do you do 10 reps of something? Why not more? Why not less? It’s quite arbitrary when you think about it. I train myself and sometimes I don’t even feel like doing three but I know I can do more’ he said.

To acquire input data, the system uses surface-electromyography (sEMG) electrodes to detect electrical signals as muscles contract and a goniometer to measure kinematics as muscle fatigue can manifest itself as small oscillations or vibrations. Signals from the muscles are then amplified and converted from an analogue-to-digital stream using a commercially available platform called Sunspot, produced by Sun Microsystems.

In order to test and calibrate the system, Al-Mulla and his team asked five male gym users to perform a resistance exercise, in this case seated bicep curls, while hooked up to the sensors and amplifier. They were then asked to exert force for as long as possible.Analysing data, the team found that the onset of fatigue occurred in several distinct phases - what Al-Mulla calls ‘transition to fatigue’.

‘You can scale it from one to 10, so the closer to 10 you are, the closer to fatigue you are and the faster you will get there. Once you detect the onset of transition to fatigue then you can calibrate it with a timer, so I can tell the user, for example, “in 30 seconds you will stop”.’

The next step will be to make the components more discrete and portable, and Al-Mulla is already working on plasters that can be placed on the relevant muscle then communicate wirelessly with an iPhone.

Wearable computing has been applied in various fields, in particular within the healthcare sector to monitor the health and the welfare of patients, where movements and behaviours are studied.

In keeping with this, Al-Mulla said his system can also be applied in occupational health and ergonomics, in particular where there is a risk of work-related musculoskeletal disorders such as repetitive strain injury.


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