Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units
By Billur Barshan, Murat Cihan Yüksek. Computer/law journal, 2014
359 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/67216c49d031134a6554d050ab53440014263c20
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jindong Wang
- Yiqiang Chen
- Philip S. Yu
- Xiaohui Peng
- Lisha Hu
- Sreenivasan Ramasamy Ramamurthy
- Nirmalya Roy
- Charu C. Aggarwal
In Computer Vision and Pattern Recognition
- Jindong Wang
- Sreenivasan Ramasamy Ramamurthy
- Nirmalya Roy
- Yiqiang Chen
- Philip S. Yu
- Xiaohui Peng
- Lisha Hu
- Charu C. Aggarwal
In Signal Processing
- Philip S. Yu
- Jindong Wang
- Yiqiang Chen
- Xiaohui Peng
- Lisha Hu
- Sreenivasan Ramasamy Ramamurthy
- Nirmalya Roy
- Charu C. Aggarwal
Citations in the corpus
- Recent trends in machine learning for human activity recognition—A survey (2018, methodology): “In HAR systems designed using shallow learning, the frequently used feature heuristics are dependent on the domain knowledge of the researcher and the performance of the machine learning techniques is highly dependent on the data representation (Bengio, 2013). The commonly used features are time dom”
- Stratified Transfer Learning for Cross-domain Activity Recognition (2017, background): “UCI daily and sports dataset (DSADS) [44] consists of 19 activities collected from 8 subjects wearing body-worn sensors on 5 body parts.”