Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors
- 19 February 2021
- journal article
- research article
- Published by Springer Nature in Neural Processing Letters
- Vol. 53 (3), 1795-1809
- https://doi.org/10.1007/s11063-021-10448-3
Abstract
No abstract availableKeywords
This publication has 41 references indexed in Scilit:
- A robust human activity recognition system using smartphone sensors and deep learningFuture Generation Computer Systems, 2018
- Activity Recognition Invariant to Sensor Orientation with Wearable Motion SensorsSensors, 2017
- Human activity recognition with inertial sensors using a deep learning approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Integrating Features for Accelerometer-based Activity RecognitionProcedia Computer Science, 2016
- Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity RecognitionSensors, 2016
- Weighting Efficient Accuracy and Minimum Sensitivity for Evolving Multi-Class ClassifiersNeural Processing Letters, 2011
- Adaptive least squares support vector machines filter for hand tremor canceling in microsurgeryInternational Journal of Machine Learning and Cybernetics, 2011
- Human Activity Recognition from Accelerometer Data Using a Wearable DeviceLecture Notes in Computer Science, 2011
- A Study on the Relationships of Classifier Performance MetricsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- A Practical Approach to Recognizing Physical ActivitiesLecture Notes in Computer Science, 2006