Migration

Citation Author(s):
IHAO
LU
Korea Advanced Institute of Science & Technology
Submitted by:
I-Hao Lu
Last updated:
Tue, 12/03/2024 - 02:47
DOI:
10.21227/8312-b997
Data Format:
*.csv
License:
Creative Commons Attribution
16 Views
Categories:
Machine Learning
Keywords:
inertial sensors, Gait Recognition; Inertial Sensors, indoor positioning; pedestrian dead reckoning; inertial measurement unit; accelerometer; gyroscope;
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Abstract 

This dataset is collected at KAIST, Daejeon, and KAIST by ISILAB to research seamless indoor-outdoor detection. The collecting device is a Raspberry Pi 4B+ with touchscreen UI connected with a Pmod Nav module and a PmodGPS. This collection has a rough three-month time span, which mitigates the specific time-specific bias. Further, in the collection, we also swap the wiring to simulate the device bias. The dynamic calibration is not applied to the dataset; searchers may choose to apply the dataset or not. The header contains the following "timestamp, indoor, gyr.X, gyr.Y, gyr.Z, acc.X, acc.Y, acc.Z, mag.X, mag.Y, mag.Z, pressure, tempB, latitude, longitude, altitude, hdop, pdop, vdop, fix, satellites, index, snr_1, snr_2, snr_3, snr_4."  We retrieve this dataset from our database for more straightforward utilization. The label indoor is a boolean index to indicate whether the user is indoors or not, which is a manual label at collecting time. 

Instructions: 

The dataset is in CSV format under the zip file; the original files are all under migration/mirgration/*.csv files. 

The file that has been rotated to the world frame with the Madgwick AHRS algorithm and removed the background magnetic value is stored under the directory of ./migration/migration_wm/*.csv files, where the suffix means worl.dframe_magenetic_removal. This is to speed up the run time since there is no necessity to waste the same process for evaluation. 

We recommend using the Pandas library to read the files. The files have the same headers, and the world frame XYZ is mapped to the North-East-Down (NED) style. With the exact same header name, there is no need to significantly modify the code when reading two types of storage.

./migration

├── migration

│   ├── 2023-02-20-11:17:17.csv

│   ├── 2023-02-20-22:52:19.csv

│   ├── 2023-02-21-13:49:20.csv

│   ├── 2023-02-21-21:16:21.csv

│   ├── 2023-02-22-10:10:05.csv

│   ├── 2023-02-22-10:24:46.csv

│   ├── 2023-02-22-20:48:21.csv

│   ├── 2023-02-27-15:57:40.csv

│   ├── 2023-02-27-22:05:21.csv

│   ├── 2023-02-28-15:51:42.csv

│   ├── 2023-02-28-18:11:53.csv

│   ├── 2023-03-09-22:28:25.csv

│   ├── 2023-03-10-13:56:47.csv

│   ├── 2023-03-10-18:13:06.csv

│   ├── 2023-03-10-20:23:22.csv

│   ├── 2023-03-13-14:56:43.csv

│   ├── 2023-03-14-12:40:13.csv

│   ├── 2023-03-20-19:48:40.csv

│   ├── 2023-03-21-13:31:26.csv

│   ├── 2023-03-21-20:11:20.csv

│   ├── 2023-03-22-13:37:49.csv

│   ├── 2023-03-22-20:01:13.csv

│   ├── 2023-03-23-13:44:53.csv

│   ├── 2023-03-23-20:34:33.csv

│   ├── 2023-03-24-14:48:02.csv

│   ├── 2023-03-24-20:32:54.csv

│   ├── 2023-03-28-21:46:23.csv

│   ├── 2023-03-29-14:32:46.csv

│   ├── 2023-03-29-20:40:00.csv

│   ├── 2023-04-03-15:19:44.csv

│   ├── 2023-04-03-20:25:33.csv

│   ├── 2023-04-05-14:59:04.csv

│   ├── 2023-04-05-20:36:53.csv

│   ├── 2023-04-06-16:33:05.csv

│   ├── 2023-04-06-16:59:13.csv

│   ├── 2023-04-06-20:52:55.csv

│   ├── 2023-04-07-15:10:53.csv

│   ├── 2023-04-07-20:25:33.csv

│   ├── 2023-04-10-13:13:07.csv

│   ├── 2023-04-12-21:54:54.csv

│   ├── 2023-04-17-12:50:24.csv

│   ├── 2023-04-18-18:01:27.csv

│   ├── 2023-04-18-18:20:00.csv

│   ├── 2023-04-18-19:56:35.csv

│   └── 2023-04-19-13:26:28.csv

└── migration_wm

    ├── 2023-02-20-11:17:17.csv

    ├── 2023-02-20-22:52:19.csv

    ├── 2023-02-21-13:49:20.csv

    ├── 2023-02-21-21:16:21.csv

    ├── 2023-02-22-10:10:05.csv

    ├── 2023-02-22-10:24:46.csv

    ├── 2023-02-22-20:48:21.csv

    ├── 2023-02-27-15:57:40.csv

    ├── 2023-02-27-22:05:21.csv

    ├── 2023-02-28-15:51:42.csv

    ├── 2023-02-28-18:11:53.csv

    ├── 2023-03-09-22:28:25.csv

    ├── 2023-03-10-13:56:47.csv

    ├── 2023-03-10-18:13:06.csv

    ├── 2023-03-10-20:23:22.csv

    ├── 2023-03-13-14:56:43.csv

    ├── 2023-03-14-12:40:13.csv

    ├── 2023-03-20-19:48:40.csv

    ├── 2023-03-21-13:31:26.csv

    ├── 2023-03-21-20:11:20.csv

    ├── 2023-03-22-13:37:49.csv

    ├── 2023-03-22-20:01:13.csv

    ├── 2023-03-23-13:44:53.csv

    ├── 2023-03-23-20:34:33.csv

    ├── 2023-03-24-14:48:02.csv

    ├── 2023-03-24-20:32:54.csv

    ├── 2023-03-28-21:46:23.csv

    ├── 2023-03-29-14:32:46.csv

    ├── 2023-03-29-20:40:00.csv

    ├── 2023-04-03-15:19:44.csv

    ├── 2023-04-03-20:25:33.csv

    ├── 2023-04-05-14:59:04.csv

    ├── 2023-04-05-20:36:53.csv

    ├── 2023-04-06-16:33:05.csv

    ├── 2023-04-06-16:59:13.csv

    ├── 2023-04-06-20:52:55.csv

    ├── 2023-04-07-15:10:53.csv

    ├── 2023-04-07-20:25:33.csv

    ├── 2023-04-10-13:13:07.csv

    ├── 2023-04-12-21:54:54.csv

    ├── 2023-04-17-12:50:24.csv

    ├── 2023-04-18-18:01:27.csv

    ├── 2023-04-18-18:20:00.csv

    ├── 2023-04-18-19:56:35.csv

    ├── 2023-04-19-13:26:28.csv

    ├── metadata

    │   └── migration_wm.csv

    └── myutils.py

 

4 directories, 92 files

Comments

First Verseion.

Submitted by I-Hao Lu on Tue, 12/03/2024 - 02:48

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