A system for accelerometer-based gesture classification using artificial neural networks

Robert M. Stephenson, Ganesh R. Naik, Rifai Chai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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%.

Original languageEnglish
Title of host publication2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)
Subtitle of host publicationSmarter Technology for a Healthier World, EMBC 2017
Place of PublicationSouth Korea
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4187-4190
Number of pages4
ISBN (Electronic)9781509028092
ISBN (Print)9781509028108
DOIs
Publication statusPublished - 13 Sep 2017
Externally publishedYes
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of
Duration: 11 Jul 201715 Jul 2017

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
CountryKorea, Republic of
CityJeju Island
Period11/07/1715/07/17

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  • Cite this

    Stephenson, R. M., Naik, G. R., & Chai, R. (2017). A system for accelerometer-based gesture classification using artificial neural networks. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017): Smarter Technology for a Healthier World, EMBC 2017 (pp. 4187-4190). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8037779