The goal of this study was to compute the relative angle of human joints such as the knee flex/extension angle using two IMUs. To do so, we utilized two 6-axis (accelerometer, gyroscope) low-cost IMUs (MPU6050, TDK-Invensense, CA, USA) that were mounted on a custom developed test apparatus that replicated the human knee motion.
In this dataset, there is a total of 45 files.
9 raw readings of the nine test trials (text files (.txt)) (e.g., raw_yaw_slow.txt)
Each trial contained 22 columns of data. Each column of data contains the following.
1) Sampled Time (ms)
2) Encoder (deg)
3,4,5,6) IMU 2 quaternion from DMP (4 values = a,b,c,d)
7,8,9,10) IMU 1 quaternion from DMP (4 values = a,b,c,d)
11,12,13) Raw Gyroscope Readings (deg/s) of IMU 2 about x,y,z (3 values)
14,15,16) Raw Accelerometer Readings (g - gravitational constant) of IMU 2 about x,y,z (3 values)
17,18,19) Raw Gyroscope Readings (deg/s) of IMU 1 about x,y,z (3 values)
20,21,22) Raw Accelerometer Readings (g - gravitational constant) of IMU 1 about x,y,z (3 values)
MATLAB scripts are used to process the raw text files. See the documentation for MATLAB scripts"README_MATLAB_Files.txt"). Additional 36 .mat files will be created. Description for these .mat files are given below.
9 raw readings of the nine test trials in MATLAB data files (.mat) (e.g., raw_yaw_slow.mat)
These files are similar to the previous raw text files but converted into MATLAB data space for easier processing in MATLAB.
These contain similar information as the previous raw text files as well as the line of when the calibration phase and data phase starts.
9 filtered readings of the nine test trails in MATLAB data files (.mat) (e.g., filtered_yaw_slow.mat)
These files contain filtered readings of the previous raw MATLAB files. Any missing data strings are removed. A 4th order Butterworth low-pass filter at 4 Hz is applied to the raw IMU data.
9 angle readings of the nine test trials in MATLAB data files (.mat) (e.g., raw_yaw_slow.mat) (e.g., angle_yaw_slow.mat)
These files contain computed angles from the previous filtered MATLAB files. Five computational algorithms (Accelerometer Inclination, Gyroscopic Integration, Complementary Filter, Kalman Filter, and Digital Motion Processing) are used. For more info on these algorithms, see https://github.com/ssong47/get_joint_angles_using_imus
9 metric readings of the nine test trials in MATLAB data files (.mat) (e.g., raw_yaw_slow.mat) (e.g., metric_yaw_slow.mat)
These files contain computed metrics such as Root-Mean-Squared-Errors (RMSE) for each computed algorithm. The RMSE is a metric for how accurately each algorithm can estimate the true rotated angle. A RMSE below 6 degrees is acceptable for quantifying human joint angles in biomechanics.
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This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
The dataset contains ~1000 RF signals in .mat format from the remote controllers (RCs) of the following drones:
- DJI (5): Inspire 1 Pro, Matrice 100, Matrice 600*, Phantom 4 Pro*, Phantom 3
- Spektrum (4): DX5e, DX6e, DX6i, JR X9303
- Futaba (1): T8FG
- Graupner (1): MC32
- HobbyKing (1): HK-T6A
- FlySky (1): FS-T6
- Turnigy (1): 9X
- Jeti Duplex (1): DC-16.
In the dataset, there are two pairs of RCs for the drones indicated by an asterisk above, making a total of 17 drone RCs. Each RF signal contains 5 million samples and spans a time period of 0.25 ms.
The scripts provided with the dataset defines a class to create drone RC objects and creates a database of objects as well as a database in table format with all the available information, such as make, model, raw RF signal, sampling frequency, etc. The scripts also include functions to visualize data and extract a few example features from the raw RF signal (e.g., transient signal start point). Instructions for using the scripts are included at the top of each script and can also be viewed by typing help scriptName in MATLAB command window.
The drone RC RF dataset was used in the following papers:
- M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
- M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference," IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
- E. Ozturk, F. Erden, and I. Guvenc, "RF-based low-SNR classification of UAVs using convolutional neural networks." arXiv preprint arXiv:2009.05519, Sept. 2020.
Other details regarding the dataset and data collection and processing can be found in the above papers and attached documentation.
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Author Contributions:
- Experiment design: O. Ozdemir and M. Ezuma
- Data collection: M. Ezuma
- Scripts: F. Erden and C. K. Anjinappa
- Documentation: F. Erden
- Supervision, revision, and funding: I. Guvenc
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Acknowledgment
This work was supported in part by NASA through the Federal Award under Grant NNX17AJ94A.
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Sugarcane vegetation on path-loss between CC2650 and CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)".
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from period 01/07/2020 to 03/11/2020.
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Millet vegetation on path-loss between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for millet crop monitoring from period 03/06/2020 to 04/10/2020.
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It contains the four biomarkers which we have selected for the instrument, in the first column we have the recordings for heart, in second we have recordings for temperature, third is for muscle activity and last column is for oxygen levels.
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This dataset has data collected from force, current, position, and inertial sensors of the NAO humanoid robot while walking on Vinyl, Gravel, Wood, Concrete, Artificial grass, and Asphalt without slope and while walking on Vinyl, Gravel and Wood with a slope of 2 degrees.
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GesHome dataset consists of 18 hand gestures from 20 non-professional subjects with various ages and occupation. The participant performed 50 times for each gesture in 5 days. Thus, GesHome consists of 18000 gesture samples in total. Using embedded accelerometer and gyroscope, we take 3-axial linear acceleration and 3-axial angular velocity with frequency equals to 25Hz. The experiments have been video-recorded to label the data manually using ELan tool.
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**Dataset will be uploaded soon - dataset is complete but uploader is currently freezing midway through status bar**
This dataset contains inertial data consisting of 1) physiotherapy exercise recordings, and 2) unlabeled other activity data recordings, each collected by smart watches worn by healthy subjects.
This dataset may be used to perform supervised classification analysis of physiotherapy exercises, or to perform out-of-distribution detection analysis with the unlabeled other activity data.
This inertial dataset consists of 20 csv files, each one corresponding to one of 20 healthy subjects. Inertial data was captured at 50 Hz.
Each record consists of an Nx10 array, where numbered columns correspond to:
0-2: Accelerometer (X/Y/Z) in G's
3-5: Magnetometer (X/Y/Z) in μT's
6-8: Gyroscope (X/Y/Z) in rad/s
9: Heart Rate in bpm.
10: Shoulder (0 = OOD (unlabeled), 1 = Left Shoulder, 2 = Right Shoulder)
11: Activity Label (0-11 as described above)
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