| mmalekzadeh/motion-sense |
189 |
|
0 |
0 |
over 4 years ago |
0 |
|
2 |
mit |
Jupyter Notebook |
| MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope) (PMC Journal) (IoTDI'19) |
| wadpac/GGIR |
133 |
|
3 |
5 |
3 months ago |
74 |
January 27, 2026 |
25 |
apache-2.0 |
R |
| Code corresponding to R package GGIR |
| wisdal/Deep-Learning-for-Sensor-based-Human-Activity-Recognition |
52 |
|
0 |
0 |
about 8 years ago |
0 |
|
1 |
|
Jupyter Notebook |
| Application of Deep Learning to Human Activity Recognition using accelerometer and gyroscope sensors data |
| nivdul/actitracker-cassandra-spark |
43 |
|
0 |
0 |
over 9 years ago |
0 |
|
3 |
|
Java |
| Activity recognition using Spark, Cassandra and MLlib |
| UdiBhaskar/Human-Activity-Recognition--Using-Deep-NN |
22 |
|
0 |
0 |
about 6 years ago |
0 |
|
2 |
|
Jupyter Notebook |
| Human Activity Recognition Using Deep Learning |
| aiff22/HAR |
19 |
|
0 |
0 |
about 9 years ago |
0 |
|
2 |
|
Python |
| mmalekzadeh/dana |
16 |
|
0 |
0 |
over 4 years ago |
0 |
|
2 |
mit |
Jupyter Notebook |
| DANA: Dimension-Adaptive Neural Architecture (UbiComp'21)( ACM IMWUT) |
| yatharthsharma/Activity-Recognition |
14 |
|
0 |
0 |
over 10 years ago |
0 |
|
0 |
|
Python |
| This project focuses on detecting user activities (Walking/Running) using smart phone's accelerometer |
| lulujianjie/robust-single-accelerometer-based-activity-recognition-using-modified-recurrence-plot |
13 |
|
0 |
0 |
about 5 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Yifeng-He/Human-Activity-Recognition-from-Accelerometer-Data-using-Ensemble-Learning |
8 |
|
0 |
0 |
over 9 years ago |
0 |
|
0 |
|
Python |
| This project aims to classify the human activities using ensemble learning method. In this project, we compared the recognition accuracy among different classifiers, visualized the data using seaborn library and t-SNE, and tuned the hyperparameters using grid search and k-fold cross-validation. |
