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README.md

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-# IMUWiFine: End-to-End Sequential Indoor Localization Dataset Card
+# IMUWiFine: End-to-End Sequential Indoor Localization
 
-**Repository:** [Link to the repository will be inserted here]
+**Repository:** [Link to the repository itself -  This needs to be added manually]
 
+**Summary:**
 
-**Abstract Summary:**  This work presents an end-to-end sequential indoor localization architecture using smartphone inertial measurement unit (IMU) data and WiFi received signal strength indicators (RSSI).  The architecture, detailed in the associated paper, uses a stack of ReLU, LSTM, and regression layers to process the sensor data and output estimated (x, y, z) coordinates.
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-**Description Summary:** This repository contains the source code for the paper "End-to-End Sequential Indoor Localization Using Smartphone Inertial Sensors and WiFi." The code implements the proposed architecture using PyTorch and requires a dataset (available separately at ISSAI) consisting of training, validation, and testing sets. The code processes IMU and WiFi RSSI data to estimate 3D indoor location.
+This 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.
 
 **Dataset Information:**
 
-The dataset is not included in this repository and needs to be downloaded separately from ISSAI ([https://issai.nu.edu.kz/imuwifine](https://issai.nu.edu.kz/imuwifine)). It consists of three folders: `train`, `test`, and `validation`.
+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.
+
+**Model Architecture:**
+
+The model utilizes a sequential architecture combining ReLU, LSTM, and regression layers to process the combined IMU and WiFi data for indoor localization.
+
 
 **Citation:**