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Using Accelerometer Signals to Classify Prehensile Hand Movements

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dc.contributor.advisor Durić, Zoran Sheth, Nirav
dc.creator Sheth, Nirav 2019-04-30 2019-07-02T00:24:16Z 2019-07-02T00:24:16Z
dc.description.abstract Prehensile movement is crucial for activities of daily living (ADLs) such as grooming and self-care. In humans, the hand is the primary device utilized for prehensile movements. Recognizing the relationship between a hand's prehensile patterns and accelerometer data can be instrumental in developing assisted and enhanced functional hand devices. An accelerometer measures the linear acceleration acting on the part of the body where the sensor is placed. This thesis demonstrates the usefulness of several features, based on accelerometer data, towards recognizing prehensile movement of the hand while performing 47 movements, grips and neutral rest. Particular emphasis is given to measuring the use- fulness of the features to identify movement transitions. A random forest classifier is used to recognize motion onset and offset as well as various phases of movement. The results showed that the accelerometer-based features were effective in recognizing motion onset and offset and moments of transitions. However, they were not as effective in recognizing various phases of prehensile movement.
dc.language.iso en en_US
dc.subject accelerometer en_US
dc.subject prehensile patterns en_US
dc.subject random forest en_US
dc.subject hand movements en_US
dc.title Using Accelerometer Signals to Classify Prehensile Hand Movements en_US
dc.type Thesis en_US Master of Science in Computer Science en_US Master's en_US Computer Science en_US George Mason University en_US

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