Using Histograms of Electromyography Signals to Detect Essential Tremor
Athena Cai
Port Moody Secondary
Floor Location : S 005 H

Essential tremor is a life altering condition that causes involuntary shaking which results in the loss of fine motor skills and inability to care for oneself. It is recognized as “the most common movement disorder, affecting up to 10 million people in the U.S.”. An algorithm was written in C++ code to differentiate between essential tremors and purposeful muscle movement through an Arduino UNO, an Olimex EKG/EMG shield, electrode wires, and electrode pads. The algorithm converts electromyography signals into a histogram, then uses the least square method to find the slope of the line of best fit of one half of said histogram, then evaluates whether this slope is above or below a threshold. If the slope is above, the algorithm will recognize that the muscle movement has been affected by essential tremor. This algorithm was tested on four muscles of the arm: the biceps brachii, flexor carpi radialis, extensor carpi ulnaris, and extensor carpi radialis brevis in three different activities to determine its effectiveness in detecting the three different types of essential tremor: postural, kinetic, and intentional. Each test was repeated five times by a healthy person not diagnosed with essential tremor and five times again while the same arm was shaking and mimicking essential tremor. From the trials it was concluded that the algorithm was effective in differentiating all three types of tremor from purposeful arm movement. However, the thresholds for each arm muscle requires adjustment to detect correctly. Ultimately, this algorithm provides real-time output and a novel approach to detecting essential tremor that can be easily adapted to be compatible with different types and degrees of tremor. Its functions can be used to coordinate tremor suppression and treat essential tremor in a low-risk and non-invasive fashion.