A System for Accelerometer-Based Gesture Classification Using Artificial Neural Networks

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 2017, pp. 4187 - 4190 (4)
Issue Date:
2017-07-11
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A great many people suffer from neurological movement disorders that render typical hardware interface devices ineffective. A need exists for a universal interface device that can be trained to accept a wide range of inputs across varying types and severities of movement disorders. In this regard, this paper details the design, testing and optimization of an accelerometer-based gesture identification system. A Bluetooth-enabled IMU mounted on the wrist provides hand motion trajectory information to a local terminal. Several techniques are applied to decrease the intra-class variance and reduce classifier complexity including filtering, segmentation and temporal scaling. Datasets consisted of 520 training samples, 260 validation samples and a further 520 testing samples. A multi-layer feed forward artificial neural network (ML-FFNN) was used to classify the input space into 26 different classes. Initial system accuracy, using arbitrary hyperparameters was 77.69% with final optimized accuracy at 99.42%.
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