Sensors Translate Movement Into Computer Commands
Microsoft envisions a future where simple muscle movements like the wag of a finger could control any digital device.
Get ready to ditch the peripherals. Microsoft envisions a future where simple muscle movements like the wag of a finger, or even ‘smart’ clothes, could control any digital device. What if, for example, you could play Guitar Hero just by going through the motions of strumming an air guitar, albeit with a high tech twist?
A recent patent application from Microsoft outlines a system for controlling computers with a “Wearable Electromyography-Based Controller”. In other words: a bunch of sensors that translate muscle movements into computer speak. It works because of the electrical signals created during muscle contraction. As the patent application explains:
Human skeletal muscles are made up of muscle fibers attached to bone by tendons. To contract a muscle, the brain sends an electrical signal through the nervous system to motor neurons. Motor neurons in turn transmit electrical impulses to adjoining muscle fibers, causing the muscle fibers to contract. Many motor neurons and their muscle fibers make up a muscle. During muscle contraction, some subset of these neurons and muscle fibers are activated and the sum of their electrical activity during contraction can be measured with electromyography (EMG). An EMG sensor can measure muscular electrical signals from the surface of the skin.
So in essence, your brain sends electrical signals to your strum-hand and fret-fingers telling the muscles to contract (also with electricity), which then lets you totally rock out on an imaginary guitar. These EMG sensors measure that electrical activity in your arms and translate it action. The sensors could be attached directly to a person’s skin or even built into accessories like watches, armbands, or even woven into our clothing; imagine a ballet where the dancer’s movements controlled images on a huge display, or activated some digitized set pieces. The video below explains the EMG Tech:
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