Update README.md

README.md

@@ -1,3 +1,110 @@
 ---
 license: mit
+task_categories:
+- audio-classification
+tags:
+- medical
 ---
+Acoustic Waveform Airway and Respiratory Examination (AWARE/PTEase) Dataset 
+===================================================================================================
+
+Guidelines
+===================================================================================================
+AWARE/PTEase is a smartphone-based sensing system designed to examine human airway's internal physiological conditions. AWARE/PTEase probes the airway with acoustic pulses through mouth, and collect the airway's reflections for analysis. Please refer to our paper[1,2] for more details.
+
+This dataset includes the raw and pre-processed acoustic data, arranged in 3 parts:
+
+- "aware_raw": Raw .wav files
+	
+	- Subfolders are named by the subjects' ID (from 1 to 382)
+	
+	- In each subfolder, there are three "cali_X.wav" files and multiple "test_X.wav" files
+		
+		- "cali_1.wav" : Calibration step 1 - long tube calibration - consists of 12 pairs of received pulses. Each pair contains one wide-band (0-16Hz) pulse and one narrow-band (0-200Hz) pulse. The narrow-band pulses are deperacated and only the wide-band pulses are used. Transmitting period for each pair is 0.4 sec.
+		
+		- "cali_2.wav" : Calibration step 2 - standard tube close-end calibration - is the same as above except the calibrations are conducted in a different setting.
+		
+		- "cali_3.wav" : Calibration step 3 - standard tube open-end calibration - is the same as above except the calibrations are conducted in a different setting.
+		
+		- "test_X.wav" : Test number X - the subject's X-th attempt of conducting the test - follows the protocol of "Nasal breathing -> Inhale -> Exhale -> Inhale -> Exhale -> Inhale -> Exhale". Each segment contains 25 pulses collected at an interval of 0.2 sec. NOTE: Some of the samples have 50 pulses for the "Nasal breathing" phase.
+
+
+- "aware_segmented": Segmented and aligned acoustic pulses
+
+	- "aware_segmented.csv" and "headers.txt"
+
+		- Column 1-46 : Basic subject information, including demographics, clinical measurements and labels. See "headers.txt".
+			- Column 1 : Subject ID
+			- Column 2-10 : Demographics
+			- Column 11 : Disease label
+			- Column 12-22 : Impulse Oscillometry Measurements
+			- Column 23-31 : Spirometry measurements
+			- Column 32-45 : Spirometry measurements in post-bronchodilator test
+			- Column 46 : Test number
+
+		- Column 47-1012846 : Acoustic signals (16-bit PCM, sample rate = 48000). Arranged by different phases. Each calibration phase contains 12 measurements and each testing phase contains 25 measurements. Each measurement last for 0.1 sec (4800 sample points).
+			- Column 47-57646 : Phase "Calibration #1". Flattend from [12,4800] by row-major order.
+			- Column 57647-115246 : Phase "Calibration #2". Flattend from [12,4800] by row-major order.
+			- Column 115247-172846 : Phase "Calibration #3". Flattend from [12,4800] by row-major order.
+			- Column 172847-292846 : Phase "Nasal breathing". Flattend from [25,4800] by row-major order.
+			- Column 292847-412846 : Phase "Inhale #1". Flattend from [25,4800] by row-major order.
+			- Column 412847-532846 : Phase "Inhale #2". Flattend from [25,4800] by row-major order.
+			- Column 532847-652846 : Phase "Inhale #3". Flattend from [25,4800] by row-major order.
+			- Column 652847-772846 : Phase "Exhale #1". Flattend from [25,4800] by row-major order.
+			- Column 772847-892846 : Phase "Exhale #2". Flattend from [25,4800] by row-major order.
+			- Column 892847-1012846 : Phase "Exhale #3". Flattend from [25,4800] by row-major order.
+
+	- "aware_segmented.pkl"
+
+		- Python pandas.DataFrame saved as pickle file. Same contents as "aware_segmented.csv", but without flattening.
+
+		- To load the data, use the following commends: 
+        
+            ``` python
+            import pandas as pd  
+            df = pd.read_pickle("aware_segmented.pkl") 
+            ```
+
+
+- "aware_csa": Processed airway cross-sectional area (CSA) curves infered from the acoustic signals
+
+	- NOTE: Not all raw samples are used for airway CSA reconsturctions. Low-quality data is discarded, thus the sample size of this part is smaller than above
+
+	- "aware_csa.csv" and "headers.txt"
+		
+		Column 1-46 : Basic subject information. Same as "aware_segmented.csv". See "headers.txt".
+		
+		Column 47-130: Reconstructed airway cross-sectional area at different depth from mouth.
+
+
+For more information about this dataset please contact: eric.yin@pitt.edu
+
+Citation
+===================================================================================================
+Use of this dataset in publications must be acknowledged by referencing the following publication: 
+
+```
+@inproceedings{yin2023ptease,
+    title={PTEase: Objective Airway Examination for Pulmonary Telemedicine using Commodity Smartphones},
+    author={Yin, Xiangyu and Huang, Kai and Forno, Erick and Chen, Wei and Huang, Heng and Gao, Wei},
+    booktitle={Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services},
+    pages={110--123},
+    year={2023},
+    url = {https://doi.org/10.1145/3581791.3596854},
+    doi = {10.1145/3581791.3596854}
+}
+```
+
+Other Related Publications:
+===================================================================================================
+```
+@inproceedings{yin2022out,
+    title={Out-Clinic Pulmonary Disease Evaluation via Acoustic Sensing and Multi-Task Learning on Commodity Smartphones},
+    author={Yin, Xiangyu and Huang, Kai and Forno, Erick and Chen, Wei and Huang, Heng and Gao, Wei},
+    booktitle={Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems},
+    pages={1182--1188},
+    year={2022},
+    url = {https://doi.org/10.1145/3560905.3568437},
+    doi = {10.1145/3560905.3568437}
+}
+```