Name
Abstract | ||
|---|---|---|
To better accurately estimate the calories burnt during popular walking workouts, it is essential to detect the environment under which these workouts are conducted. To our best knowledge, no gait analysis studies have been done so far for such detection. This research addresses this problem by recognizing walking workouts under different environments based on the foot-mounted inertial sensor. Our objective is to recognize ten different workout activities including walking and brisk-walking under flat surface, ascending/descending staircase and upward/downward slope with no stairs. Our algorithm first identifies the extended foot-flat phase, then uses it as a boundary to extract key important features. Decision Tree, Random Forest and K-Nearest Neighbor machine learning algorithms are evaluated to decide which one works the best along with our algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WF-IoT.2019.8767285 | 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) |
Keywords | Field | DocType |
Gait analysis,activity recognition,environment detection,walking workouts,machine learning algorithms | Decision tree,Activity recognition,Computer science,Computer network,Gait analysis,Artificial intelligence,Inertial measurement unit,Random forest,Machine learning,Stairs | Conference |
ISBN | Citations | PageRank |
978-1-5386-4981-7 | 0 | 0.34 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fanuel Wahjudi | 1 | 0 | 0.34 |
| Fuchun Joseph Lin | 2 | 96 | 20.75 |