Name
Abstract | ||
|---|---|---|
Novel features for joint classification of gait and device modes are proposed and multiple machine learning methods are adopted to jointly classify the modes. The classification accuracy as well as the F1 score of two standard classification algorithms, $K$ -nearest neighbor (KNN) and Gaussian process (GP), are evaluated and compared against a proposed neural network (NN)-based classifier. The pr... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIM.2019.2958005 | IEEE Transactions on Instrumentation and Measurement |
Keywords | DocType | Volume |
Feature extraction,Hidden Markov models,Smart phones,Sensors,Legged locomotion,Machine learning,Machine learning algorithms | Journal | 69 |
Issue | ISSN | Citations |
8 | 0018-9456 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Parinaz Kasebzadeh | 1 | 0 | 0.34 |
| Kamiar Radnosrati | 2 | 0 | 0.68 |
| Gustaf Hendeby | 3 | 216 | 21.37 |
| Fredrik Gustafsson | 4 | 2287 | 281.33 |