In wearable human-machine interfaces, sensors play an essential role, in particular in recognizing hand-movement intent. This thesis presents and evaluates alternative and complementary methods to the gold-standard surface electromyography, such as force-, electro-impedance- and sono-myography, as well as inertial tracking. It also introduces different pipelines involving features and machine learning methods and explores multimodal sensing by fusing different modalities to improve muscular intent detection.
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In wearable human-machine interfaces, sensors play an essential role, in particular in recognizing hand-movement intent. This thesis presents and evaluates alternative and complementary methods to the gold-standard surface electromyography, such as force-, electro-impedance- and sono-myography, as well as inertial tracking. It also introduces different pipelines involving features and machine learning methods and explores multimodal sensing by fusing different modalities to improve muscular inte...
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