Update README.md

README.md

@@ -1,14 +1,30 @@
+---
+license: cc-by-4.0
+tags:
+- imu
+- wifi
+- localization
+pretty_name: imu_wifine
+size_categories:
+- 1M<n<10M
+---
 # IMUWiFine: End-to-End Sequential Indoor Localization
+**Paper:** [End-to-End Sequential Indoor Localization Using Smartphone Inertial Sensors and WiFi](https://ieeexplore.ieee.org/abstract/document/9708854)
 
+**GitHub:** [https://github.com/IS2AI/IMUWiFine](https://github.com/IS2AI/IMUWiFine)
 
-**Dataset Information:**
+**Description:** The IMUWiFine dataset comprises IMU and WiFi RSSI data readings recorded in sequential order with a fine spatiotemporal resolution. The
+dataset was collected on the fourth, fifth, and sixth floors of the C4 building at the Nazarbayev University campus. The total covered area is over 9, 000 m2 throughout the three
+floors.
 
-The dataset used for training and evaluation is available separately at [ISSAI](https://issai.nu.edu.kz/imuwifine) and consists of train, test and validation folders.  Specific file paths must be set within the `train.py` script.
-The following github repository provides the source code for the paper "End-to-End Sequential Indoor Localization Using Smartphone Inertial Sensors and WiFi" (Nurpeiissov et al., 2022). The code implements an end-to-end sequential indoor localization architecture using a PyTorch-based model. This architecture takes as input WiFi Received Signal Strength Indicators (RSSI) and Inertial Measurement Unit (IMU) readings from a smartphone and outputs estimated (x, y, z) coordinates. The model architecture comprises a stack of ReLU, LSTM, and regression layers. The dataset, available separately, is divided into training, validation, and testing sets.  The code allows for training the model and automatic evaluation on the testing set.
-**Repository:** https://github.com/IS2AI/IMUWiFine
+|Specifications| Train | Valid | Test |Total
+|-|-|-|-|-|
+Number of trajectories | 60 | 30 | 30 | 120|
+Number of samples| 3.1M| 1.2M| 1.1M| 5.4M|
+Total length (km)| 8.0 |3.1 |3.0 |14.2|
+Total duration (hours)| 5.5| 2.5| 2.4| 10.4|
 
 **Citation:**
-
 ```bibtex
 @INPROCEEDINGS{9708854,
   author={Nurpeiissov, Mukhamet and Kuzdeuov, Askat and Assylkhanov, Aslan and Khassanov, Yerbolat and Varol, Huseyin Atakan},
@@ -17,4 +33,4 @@ The following github repository provides the source code for the paper "End-to-E
   year={2022},
   pages={566-571},
   doi={10.1109/SII52469.2022.9708854}}
-```
+```