cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11185036
title: AllGestureWiimoteX

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The original data include 10 subjects, each perform 10 gestures 10 times. The gesture acquisition device is a Nintendo Wiimote remote controller with built-in three-axis accelerometer.  Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels below). Note that data are shuffled and randomly sampled so that instances across datasets are not sychronized by dimension or subject. Time series are of different lengths. There are no missing values.

The gestures are (class label. original label - English translation):



poteg: pick-up

shake: shake

desno: one move to the right

levo: one move to the left

gor: one move to up

dol: one move to down

kroglevo:  one left circle

krogdesn: one right circle

suneknot: one move toward the screen

sunekven: one move away from the screen


This data is acceleration in x-axis dimension.

Donator: J. Guna
DOI: 10.5281/zenodo.11185107
title: AllGestureWiimoteY

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The original data include 10 subjects, each perform 10 gestures 10 times. The gesture acquisition device is a Nintendo Wiimote remote controller with built-in three-axis accelerometer.  Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels below). Note that data are shuffled and randomly sampled so that instances across datasets are not sychronized by dimension or subject. Time series are of different lengths. There are no missing values.

The gestures are (class label. original label - English translation):



poteg: pick-up

shake: shake

desno: one move to the right

levo: one move to the left

gor: one move to up

dol: one move to down

kroglevo:  one left circle

krogdesn: one right circle

suneknot: one move toward the screen

sunekven: one move away from the screen


This data is acceleration in y-axis dimension.

Donator: J. Guna
DOI: 10.5281/zenodo.11185106
title: AllGestureWiimoteY

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The original data include 10 subjects, each perform 10 gestures 10 times. The gesture acquisition device is a Nintendo Wiimote remote controller with built-in three-axis accelerometer.  Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels below). Note that data are shuffled and randomly sampled so that instances across datasets are not sychronized by dimension or subject. Time series are of different lengths. There are no missing values.

The gestures are (class label. original label - English translation):



poteg: pick-up

shake: shake

desno: one move to the right

levo: one move to the left

gor: one move to up

dol: one move to down

kroglevo:  one left circle

krogdesn: one right circle

suneknot: one move toward the screen

sunekven: one move away from the screen


This data is acceleration in y-axis dimension.

Donator: J. Guna
DOI: 10.5281/zenodo.11185135
title: AllGestureWiimoteZ

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The original data include 10 subjects, each perform 10 gestures 10 times. The gesture acquisition device is a Nintendo Wiimote remote controller with built-in three-axis accelerometer.  Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels below). Note that data are shuffled and randomly sampled so that instances across datasets are not sychronized by dimension or subject. Time series are of different lengths. There are no missing values.

The gestures are (class label. original label - English translation):



poteg: pick-up

shake: shake

desno: one move to the right

levo: one move to the left

gor: one move to up

dol: one move to down

kroglevo:  one left circle

krogdesn: one right circle

suneknot: one move toward the screen

sunekven: one move away from the screen


This data is acceleration in z-axis dimension.

Donator: J. Guna
DOI: 10.5281/zenodo.11185136
title: AllGestureWiimoteZ

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The original data include 10 subjects, each perform 10 gestures 10 times. The gesture acquisition device is a Nintendo Wiimote remote controller with built-in three-axis accelerometer.  Each subject performs a set of gestures multiple times. Classes are based on gestures (see class labels below). Note that data are shuffled and randomly sampled so that instances across datasets are not sychronized by dimension or subject. Time series are of different lengths. There are no missing values.

The gestures are (class label. original label - English translation):



poteg: pick-up

shake: shake

desno: one move to the right

levo: one move to the left

gor: one move to up

dol: one move to down

kroglevo:  one left circle

krogdesn: one right circle

suneknot: one move toward the screen

sunekven: one move away from the screen


This data is acceleration in z-axis dimension.

Donator: J. Guna
DOI: 10.5281/zenodo.11186617
title: DodgerLoopDay

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The traffic data are collected with the loop sensor installed on ramp for the 101 North freeway in Los Angeles. This location is close to Dodgers Stadium; therefore the traffic is affected by volume of visitors to the stadium. Missing values are represented with NaN.  The classes are days of the week.  - Class 1: Sunday - Class 2: Monday - Class 3: Tuesday - Class 4: Wednesday - Class 5: Thursday - Class 6: Friday - Class 7: Saturday. Data created by Ihler, Alexander, Jon Hutchins, and Padhraic Smyth (see [1][2][3]). Data edited by Chin-Chia Michael Yeh.  

[1] Ihler, Alexander, Jon Hutchins, and Padhraic Smyth. "Adaptive event detection with time-varying poisson processes." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.  

[2] “UCI Machine Learning Repository: Dodgers Loop Sensor Data Set.” UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets/dodgers+loop+sensor.  

[3] “Caltrans PeMS.” Caltrans, pems.dot.ca.gov/.

Donator: C. Yeh
DOI: 10.5281/zenodo.11186618
title: DodgerLoopDay

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The traffic data are collected with the loop sensor installed on ramp for the 101 North freeway in Los Angeles. This location is close to Dodgers Stadium; therefore the traffic is affected by volume of visitors to the stadium. Missing values are represented with NaN.  The classes are days of the week.  - Class 1: Sunday - Class 2: Monday - Class 3: Tuesday - Class 4: Wednesday - Class 5: Thursday - Class 6: Friday - Class 7: Saturday. Data created by Ihler, Alexander, Jon Hutchins, and Padhraic Smyth (see [1][2][3]). Data edited by Chin-Chia Michael Yeh.  

[1] Ihler, Alexander, Jon Hutchins, and Padhraic Smyth. "Adaptive event detection with time-varying poisson processes." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.  

[2] “UCI Machine Learning Repository: Dodgers Loop Sensor Data Set.” UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets/dodgers+loop+sensor.  

[3] “Caltrans PeMS.” Caltrans, pems.dot.ca.gov/.

Donator: C. Yeh
DOI: 10.5281/zenodo.11186627
title: DodgerLoopGame

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The traffic data are collected with the loop sensor installed on ramp for the 101 North freeway in Los Angeles. This location is close to Dodgers Stadium; therefore the traffic is affected by volume of visitors to the stadium. Missing values are represented with NaN.  - Class 1: Normal Day - Class 2: Game Day   There is nothing to infer from the order of examples in the train and test set. Missing values are represented with NaN in the text file.  Data created by Ihler, Alexander, Jon Hutchins, and Padhraic Smyth (see [1][2][3]). Data edited by Chin-Chia Michael Yeh.  

[1] Ihler, Alexander, Jon Hutchins, and Padhraic Smyth. "Adaptive event detection with time-varying poisson processes." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.  

[2] “UCI Machine Learning Repository: Dodgers Loop Sensor Data Set.” UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets/dodgers+loop+sensor.  

[3] “Caltrans PeMS.” Caltrans, pems.dot.ca.gov/.

Donator: C. Yeh
DOI: 10.5281/zenodo.11186628
title: DodgerLoopGame

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The traffic data are collected with the loop sensor installed on ramp for the 101 North freeway in Los Angeles. This location is close to Dodgers Stadium; therefore the traffic is affected by volume of visitors to the stadium. Missing values are represented with NaN.  - Class 1: Normal Day - Class 2: Game Day   There is nothing to infer from the order of examples in the train and test set. Missing values are represented with NaN in the text file.  Data created by Ihler, Alexander, Jon Hutchins, and Padhraic Smyth (see [1][2][3]). Data edited by Chin-Chia Michael Yeh.  

[1] Ihler, Alexander, Jon Hutchins, and Padhraic Smyth. "Adaptive event detection with time-varying poisson processes." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.  

[2] “UCI Machine Learning Repository: Dodgers Loop Sensor Data Set.” UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets/dodgers+loop+sensor.  

[3] “Caltrans PeMS.” Caltrans, pems.dot.ca.gov/.

Donator: C. Yeh
DOI: 10.5281/zenodo.11186646
title: DodgerLoopWeekend

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The traffic data are collected with the loop sensor installed on ramp for the 101 North freeway in Los Angeles. This location is close to Dodgers Stadium; therefore the traffic is affected by volume of visitors to the stadium. Missing values are represented with NaN.  ## DodgersLoopWeekend  - Class 1: Weekday - Class 2: Weekend  There is nothing to infer from the order of examples in the train and test set. Missing values are represented with NaN in the text file.  Data created by Ihler, Alexander, Jon Hutchins, and Padhraic Smyth (see [1][2][3]). Data edited by Chin-Chia Michael Yeh.  

[1] Ihler, Alexander, Jon Hutchins, and Padhraic Smyth. "Adaptive event detection with time-varying poisson processes." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.  

[2] “UCI Machine Learning Repository: Dodgers Loop Sensor Data Set.” UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets/dodgers+loop+sensor.  

[3] “Caltrans PeMS.” Caltrans, pems.dot.ca.gov/.

Donator: C. Yeh
DOI: 10.5281/zenodo.11186647
title: DodgerLoopWeekend

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

The traffic data are collected with the loop sensor installed on ramp for the 101 North freeway in Los Angeles. This location is close to Dodgers Stadium; therefore the traffic is affected by volume of visitors to the stadium. Missing values are represented with NaN.  ## DodgersLoopWeekend  - Class 1: Weekday - Class 2: Weekend  There is nothing to infer from the order of examples in the train and test set. Missing values are represented with NaN in the text file.  Data created by Ihler, Alexander, Jon Hutchins, and Padhraic Smyth (see [1][2][3]). Data edited by Chin-Chia Michael Yeh.  

[1] Ihler, Alexander, Jon Hutchins, and Padhraic Smyth. "Adaptive event detection with time-varying poisson processes." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2006.  

[2] “UCI Machine Learning Repository: Dodgers Loop Sensor Data Set.” UCI Machine Learning Repository, archive.ics.uci.edu/ml/datasets/dodgers+loop+sensor.  

[3] “Caltrans PeMS.” Caltrans, pems.dot.ca.gov/.

Donator: C. Yeh
DOI: 10.5281/zenodo.11197478
title: GestureMidAirD1

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Data contain 3D hand trajectories collected with Leap Motion device. There are 13 subjects, each performs 26 interface-command gestures. 

Each gesture is encoded as a sequence of 3D points, representing the position of the dominant-hand forefinger. There are 26 classes corresponding to unique gestures. See Fig. 1 of [2] for the list of gestures and their visualisation.

- Class 1: arc3Dleft- Class 2: arc3Dright- Class 3: caret- Class 4: check- Class 5: circle- Class 6: curly-bracket-left- Class 7: curly-bracket-right- Class 8: delete- Class 9: left-swipe- Class 10: pigtail- Class 11: poly3Dxyz- Class 12: poly3Dxzy- Class 13: poly3Dyxz- Class 14: poly3Dyzx- Class 15: poly3Dzxy- Class 16: poly3Dzyx- Class 17: rectangle- Class 18: right-swipe- Class 19: spiral- Class 20: square-bracket-left- Class 21: square-bracket-right- Class 22: star- Class 23: triangle- Class 24: v- Class 25: x- Class 26: zig-zag 

We make three datasets out of these data, one for each dimension. We follow the original paper (see [2]) and use data of 8 subjects for training and 5 subjects for testing. Data of a same subjects does not appear in both train and test set, so resampling may introduce bias.

Data created by Fabio M. Caputo et al. (see [1], [2]). Data edited by Hoang Anh Dau.

[1] Github repo: https://github.com/giach68/gesturesCodes[2] Caputo, Fabio M., et al. Comparing 3D trajectories for simple mid-air gesture recognition. Computers & Graphics. 73 (2018): 17-25. 

Donator: H. A. Dau
DOI: 10.5281/zenodo.11197477
title: GestureMidAirD1

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Data contain 3D hand trajectories collected with Leap Motion device. There are 13 subjects, each performs 26 interface-command gestures. 

Each gesture is encoded as a sequence of 3D points, representing the position of the dominant-hand forefinger. There are 26 classes corresponding to unique gestures. See Fig. 1 of [2] for the list of gestures and their visualisation.

- Class 1: arc3Dleft- Class 2: arc3Dright- Class 3: caret- Class 4: check- Class 5: circle- Class 6: curly-bracket-left- Class 7: curly-bracket-right- Class 8: delete- Class 9: left-swipe- Class 10: pigtail- Class 11: poly3Dxyz- Class 12: poly3Dxzy- Class 13: poly3Dyxz- Class 14: poly3Dyzx- Class 15: poly3Dzxy- Class 16: poly3Dzyx- Class 17: rectangle- Class 18: right-swipe- Class 19: spiral- Class 20: square-bracket-left- Class 21: square-bracket-right- Class 22: star- Class 23: triangle- Class 24: v- Class 25: x- Class 26: zig-zag 

We make three datasets out of these data, one for each dimension. We follow the original paper (see [2]) and use data of 8 subjects for training and 5 subjects for testing. Data of a same subjects does not appear in both train and test set, so resampling may introduce bias.

Data created by Fabio M. Caputo et al. (see [1], [2]). Data edited by Hoang Anh Dau.

[1] Github repo: https://github.com/giach68/gesturesCodes[2] Caputo, Fabio M., et al. Comparing 3D trajectories for simple mid-air gesture recognition. Computers & Graphics. 73 (2018): 17-25. 

Donator: H. A. Dau
DOI: 10.5281/zenodo.11197489
title: GestureMidAirD2

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Data contain 3D hand trajectories collected with Leap Motion device. There are 13 subjects, each performs 26 interface-command gestures. 

Each gesture is encoded as a sequence of 3D points, representing the position of the dominant-hand forefinger. There are 26 classes corresponding to unique gestures. See Fig. 1 of [2] for the list of gestures and their visualisation.

- Class 1: arc3Dleft- Class 2: arc3Dright- Class 3: caret- Class 4: check- Class 5: circle- Class 6: curly-bracket-left- Class 7: curly-bracket-right- Class 8: delete- Class 9: left-swipe- Class 10: pigtail- Class 11: poly3Dxyz- Class 12: poly3Dxzy- Class 13: poly3Dyxz- Class 14: poly3Dyzx- Class 15: poly3Dzxy- Class 16: poly3Dzyx- Class 17: rectangle- Class 18: right-swipe- Class 19: spiral- Class 20: square-bracket-left- Class 21: square-bracket-right- Class 22: star- Class 23: triangle- Class 24: v- Class 25: x- Class 26: zig-zag 

We make three datasets out of these data, one for each dimension. We follow the original paper (see [2]) and use data of 8 subjects for training and 5 subjects for testing. Data of a same subjects does not appear in both train and test set, so resampling may introduce bias.

Data created by Fabio M. Caputo et al. (see [1], [2]). Data edited by Hoang Anh Dau.

[1] Github repo: https://github.com/giach68/gesturesCodes[2] Caputo, Fabio M., et al. Comparing 3D trajectories for simple mid-air gesture recognition. Computers & Graphics. 73 (2018): 17-25. 

Donator: H. A. Dau
DOI: 10.5281/zenodo.11197490
title: GestureMidAirD2

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Data contain 3D hand trajectories collected with Leap Motion device. There are 13 subjects, each performs 26 interface-command gestures. 

Each gesture is encoded as a sequence of 3D points, representing the position of the dominant-hand forefinger. There are 26 classes corresponding to unique gestures. See Fig. 1 of [2] for the list of gestures and their visualisation.

- Class 1: arc3Dleft- Class 2: arc3Dright- Class 3: caret- Class 4: check- Class 5: circle- Class 6: curly-bracket-left- Class 7: curly-bracket-right- Class 8: delete- Class 9: left-swipe- Class 10: pigtail- Class 11: poly3Dxyz- Class 12: poly3Dxzy- Class 13: poly3Dyxz- Class 14: poly3Dyzx- Class 15: poly3Dzxy- Class 16: poly3Dzyx- Class 17: rectangle- Class 18: right-swipe- Class 19: spiral- Class 20: square-bracket-left- Class 21: square-bracket-right- Class 22: star- Class 23: triangle- Class 24: v- Class 25: x- Class 26: zig-zag 

We make three datasets out of these data, one for each dimension. We follow the original paper (see [2]) and use data of 8 subjects for training and 5 subjects for testing. Data of a same subjects does not appear in both train and test set, so resampling may introduce bias.

Data created by Fabio M. Caputo et al. (see [1], [2]). Data edited by Hoang Anh Dau.

[1] Github repo: https://github.com/giach68/gesturesCodes[2] Caputo, Fabio M., et al. Comparing 3D trajectories for simple mid-air gesture recognition. Computers & Graphics. 73 (2018): 17-25. 

Donator: H. A. Dau
DOI: 10.5281/zenodo.11197503
title: GestureMidAirD3

description: This dataset is part of the UCR Archive maintained by University of Southampton researchers. Please cite a relevant or the latest full archive release if you use the datasets. See http://www.timeseriesclassification.com/.

Data contain 3D hand trajectories collected with Leap Motion device. There are 13 subjects, each performs 26 interface-command gestures. 

Each gesture is encoded as a sequence of 3D points, representing the position of the dominant-hand forefinger. There are 26 classes corresponding to unique gestures. See Fig. 1 of [2] for the list of gestures and their visualisation.

- Class 1: arc3Dleft- Class 2: arc3Dright- Class 3: caret- Class 4: check- Class 5: circle- Class 6: curly-bracket-left- Class 7: curly-bracket-right- Class 8: delete- Class 9: left-swipe- Class 10: pigtail- Class 11: poly3Dxyz- Class 12: poly3Dxzy- Class 13: poly3Dyxz- Class 14: poly3Dyzx- Class 15: poly3Dzxy- Class 16: poly3Dzyx- Class 17: rectangle- Class 18: right-swipe- Class 19: spiral- Class 20: square-bracket-left- Class 21: square-bracket-right- Class 22: star- Class 23: triangle- Class 24: v- Class 25: x- Class 26: zig-zag 

We make three datasets out of these data, one for each dimension. We follow the original paper (see [2]) and use data of 8 subjects for training and 5 subjects for testing. Data of a same subjects does not appear in both train and test set, so resampling may introduce bias.

Data created by Fabio M. Caputo et al. (see [1], [2]). Data edited by Hoang Anh Dau.

[1] Github repo: https://github.com/giach68/gesturesCodes[2] Caputo, Fabio M., et al. Comparing 3D trajectories for simple mid-air gesture recognition. Computers & Graphics. 73 (2018): 17-25. 

Donator: H. A. Dau