CNN based approach for activity recognition using a wrist-worn accelerometer

Publication Type:
Conference Proceeding
Citation:
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 2438 - 2441
Issue Date:
2017-09-13
Full metadata record
Files in This Item:
Filename Description Size
08037349.pdfPublished version862.67 kB
Adobe PDF
© 2017 IEEE. In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.
Please use this identifier to cite or link to this item: