TY - GEN
T1 - A system for accelerometer-based gesture classification using artificial neural networks
AU - Stephenson, Robert M.
AU - Naik, Ganesh R.
AU - Chai, Rifai
PY - 2017/9/13
Y1 - 2017/9/13
N2 - 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%.
AB - 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%.
UR - http://www.scopus.com/inward/record.url?scp=85032174695&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2017.8037779
DO - 10.1109/EMBC.2017.8037779
M3 - Conference contribution
C2 - 29060820
AN - SCOPUS:85032174695
SN - 9781509028108
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4187
EP - 4190
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2017)
PB - Institute of Electrical and Electronics Engineers Inc.
CY - South Korea
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -