IMU人体行为识别（HAR）公开数据集

概述

基于IMU等传感器的人体行为识别（Human Activity Recognition，HAR）数据集，依照穿戴式传感器或手机传感器采集的原始数据判断用户的行为模式等状态。

名称 传感器模态 行为类别 传感器位置 频率
Sussex-Huawei Locomotion 2020 A, G, M, LA, R, P 8 4 100Hz
REALDISP A, G, M, R 33 9 50Hz
PAMAP2 T, A, G, M 13 3 100Hz
RealWorld HAR A, G, M, L, GN, SL 8 7 50Hz
Smartphone A, G 6 1 50Hz
HAPT A, G 12 1 50Hz
DSADS A, G, M 19 5 25Hz
MHEALTH A, G, M, EC 12 3 50Hz
HTC-TMD A, G, M 10 1 50Hz
USC-HAD A, G 12 1 100Hz

其中传感器模态简称对照如下：

简称 传感器模态
A Accelerometer
G Gyroscope
M Magnetic Field
R Rotation Vector
LA Linear Vector
P Pressure
T Temperature
L Light
GN GNSS (Global Navigation Satellite System)
SL Sound Level
EC Electrocardiogram

介绍

Sussex-Huawei Locomotion 2020 交通模式

属性 参数
类别 Still, Walk, Run, Bike, Car, Bus, Train, Subway
传感器位置 Torso, Bag, Hips, Torso
传感器模态 A, G, M, LA, R, P
频率 100Hz
大小 26.43GB
源 Sussex-Huawei Locomotion Challenge 2020
下载 Train/Torso Train/Bag Train/Hips Train/Hand Validation Test Test/Labels
提供者 University of Sussex
HUAWEI

如果您将该数据集用于研究，请引用以下工作：

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@article{DBLP:journals/access/GjoreskiCWMMVR18,
  author    = {Hristijan Gjoreski and
               Mathias Ciliberto and
               Lin Wang and
               Francisco Javier Ord{\'{o}}{\~{n}}ez Morales and
               Sami Mekki and
               Stefan Valentin and
               Daniel Roggen},
  title     = {The University of Sussex-Huawei Locomotion and Transportation Dataset
               for Multimodal Analytics With Mobile Devices},
  journal   = {{IEEE} Access},
  volume    = {6},
  pages     = {42592--42604},
  year      = {2018},
  url       = {https://doi.org/10.1109/ACCESS.2018.2858933},
  doi       = {10.1109/ACCESS.2018.2858933},
  timestamp = {Mon, 15 Jun 2020 16:51:57 +0200},
  biburl    = {https://dblp.org/rec/journals/access/GjoreskiCWMMVR18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/access/WangGCMVR19,
  author    = {Lin Wang and
               Hristijan Gjoreski and
               Mathias Ciliberto and
               Sami Mekki and
               Stefan Valentin and
               Daniel Roggen},
  title     = {Enabling Reproducible Research in Sensor-Based Transportation Mode
               Recognition With the Sussex-Huawei Dataset},
  journal   = {{IEEE} Access},
  volume    = {7},
  pages     = {10870--10891},
  year      = {2019},
  url       = {https://doi.org/10.1109/ACCESS.2019.2890793},
  doi       = {10.1109/ACCESS.2019.2890793},
  timestamp = {Thu, 14 Feb 2019 15:07:35 +0100},
  biburl    = {https://dblp.org/rec/journals/access/WangGCMVR19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/huc/WangGM0R18,
  author    = {Lin Wang and
               Hristijan Gjoreski and
               Kazuya Murao and
               Tsuyoshi Okita and
               Daniel Roggen},
  title     = {Summary of the Sussex-Huawei Locomotion-Transportation Recognition
               Challenge},
  booktitle = {Proceedings of the 2018 {ACM} International Joint Conference and 2018
               International Symposium on Pervasive and Ubiquitous Computing and
               Wearable Computers, UbiComp/ISWC 2018 Adjunct, Singapore, October
               08-12, 2018},
  pages     = {1521--1530},
  publisher = {{ACM}},
  year      = {2018},
  url       = {https://doi.org/10.1145/3267305.3267519},
  doi       = {10.1145/3267305.3267519},
  timestamp = {Thu, 14 Oct 2021 10:35:41 +0200},
  biburl    = {https://dblp.org/rec/conf/huc/WangGM0R18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/huc/WangGCMVR18,
  author    = {Lin Wang and
               Hristijan Gjoreski and
               Mathias Ciliberto and
               Sami Mekki and
               Stefan Valentin and
               Daniel Roggen},
  title     = {Benchmarking the {SHL} Recognition Challenge with Classical and Deep-Learning
               Pipelines},
  booktitle = {Proceedings of the 2018 {ACM} International Joint Conference and 2018
               International Symposium on Pervasive and Ubiquitous Computing and
               Wearable Computers, UbiComp/ISWC 2018 Adjunct, Singapore, October
               08-12, 2018},
  pages     = {1626--1635},
  publisher = {{ACM}},
  year      = {2018},
  url       = {https://doi.org/10.1145/3267305.3267531},
  doi       = {10.1145/3267305.3267531},
  timestamp = {Mon, 15 Jun 2020 17:10:43 +0200},
  biburl    = {https://dblp.org/rec/conf/huc/WangGCMVR18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/huc/WangGCLM0R19,
  author    = {Lin Wang and
               Hristijan Gjoreski and
               Mathias Ciliberto and
               Paula Lago and
               Kazuya Murao and
               Tsuyoshi Okita and
               Daniel Roggen},
  editor    = {Robert Harle and
               Katayoun Farrahi and
               Nicholas D. Lane},
  title     = {Summary of the Sussex-Huawei locomotion-transportation recognition
               challenge 2019},
  booktitle = {Proceedings of the 2019 {ACM} International Joint Conference on Pervasive
               and Ubiquitous Computing and Proceedings of the 2019 {ACM} International
               Symposium on Wearable Computers, UbiComp/ISWC 2019 Adjunct, London,
               UK, September 9-13, 2019},
  pages     = {849--856},
  publisher = {{ACM}},
  year      = {2019},
  url       = {https://doi.org/10.1145/3341162.3344872},
  doi       = {10.1145/3341162.3344872},
  timestamp = {Thu, 14 Oct 2021 10:35:34 +0200},
  biburl    = {https://dblp.org/rec/conf/huc/WangGCLM0R19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

HTC-TMD 交通模式

属性 参数
类别 Still, Walk, Run, Bicycle, Motorcycle, Car, Bus, MRT, Train, HSR
传感器位置 未知
传感器模态 A, G, M
频率 50Hz
大小 18GB
源 HTC Research
下载 评论获取
提供者 HTC

如果您将该数据集用于研究，请引用以下工作：

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@article{DBLP:journals/pvldb/YuYWLC14,
  author    = {Meng{-}Chieh Yu and
               Tong Yu and
               Shao{-}Chen Wang and
               Chih{-}Jen Lin and
               Edward Y. Chang},
  title     = {Big Data Small Footprint: The Design of {A} Low-Power Classifier for
               Detecting Transportation Modes},
  journal   = {Proc. {VLDB} Endow.},
  volume    = {7},
  number    = {13},
  pages     = {1429--1440},
  year      = {2014},
  url       = {http://www.vldb.org/pvldb/vol7/p1429-yu.pdf},
  doi       = {10.14778/2733004.2733015},
  timestamp = {Sun, 24 May 2020 16:55:40 +0200},
  biburl    = {https://dblp.org/rec/journals/pvldb/YuYWLC14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

PAMAP2 行为模式

属性 参数
类别 Lying, Sitting, Standing, Walking, Running, Cycling, Nordic walking, Watching TV, Computer work, Car driving, Ascending stairs, Descending stairs
传感器位置 Wrist on the dominant arm, Chest, Dominant side's ankle
传感器模态 T, A, G, M
频率 100Hz
大小 656MB
源 UCI Machine Learning Repository
下载 下载 README
提供者 University of California, Irvine

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@inproceedings{DBLP:conf/iswc/ReissS12,
  author    = {Attila Reiss and
               Didier Stricker},
  title     = {Introducing a New Benchmarked Dataset for Activity Monitoring},
  booktitle = {16th International Symposium on Wearable Computers, {ISWC} 2012, Newcastle,
               United Kingdom, June 18-22, 2012},
  pages     = {108--109},
  publisher = {{IEEE} Computer Society},
  year      = {2012},
  url       = {https://doi.org/10.1109/ISWC.2012.13},
  doi       = {10.1109/ISWC.2012.13},
  timestamp = {Wed, 16 Oct 2019 14:14:52 +0200},
  biburl    = {https://dblp.org/rec/conf/iswc/ReissS12.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/petra/ReissS12,
  author    = {Attila Reiss and
               Didier Stricker},
  editor    = {Fillia Makedon},
  title     = {Creating and benchmarking a new dataset for physical activity monitoring},
  booktitle = {The 5th International Conference on PErvasive Technologies Related
               to Assistive Environments, {PETRA} 2012, Heraklion, Crete, Greece,
               June 6-9, 2012},
  pages     = {40},
  publisher = {{ACM}},
  year      = {2012},
  url       = {https://doi.org/10.1145/2413097.2413148},
  doi       = {10.1145/2413097.2413148},
  timestamp = {Tue, 06 Nov 2018 16:58:58 +0100},
  biburl    = {https://dblp.org/rec/conf/petra/ReissS12.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

RealWorld HAR 行为模式

属性 参数
类别 Walking, Running, Sitting, Standing, Lying, Stairs up, Stairs down, Jumping
传感器位置 Chest, Forearm, Head, Shin, Thigh, Upper arm, Waist
传感器模态 A, G, M, L, GN, SL
频率 50Hz
大小 3.5GB
源 Human Activity Recognition
下载 下载
提供者 University of Mannheim

如果您将该数据集用于研究，请引用以下工作：

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  @inproceedings{sztyler2016onbody,
     title={On-body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition},
     author={Sztyler, Timo and Stuckenschmidt, Heiner},
     booktitle={2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)},
     pages={1--9},
     year={2016},
     eventdate={2016-03-14/2016-03-18},
     venue={Sydney, Australia},
     publisher={IEEE Computer Society},
     doi={10.1109/PERCOM.2016.7456521},
     note={http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7456521}
}

Smartphone 行为模式

属性 参数
类别 Walking, Walking upstairs, Walking downstairs, Sitting, Standing, Laying
传感器位置 Waist
传感器模态 A, G
频率 50Hz
大小 58.2MB
源 UCI Machine Learning Repository
下载 下载
提供者 University of California, Irvine

如果您将该数据集用于研究，请引用以下工作：

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@inproceedings{DBLP:conf/esann/AnguitaGOPR13,
  author    = {Davide Anguita and
               Alessandro Ghio and
               Luca Oneto and
               Xavier Parra and
               Jorge Luis Reyes{-}Ortiz},
  title     = {A Public Domain Dataset for Human Activity Recognition using Smartphones},
  booktitle = {21st European Symposium on Artificial Neural Networks, {ESANN} 2013,
               Bruges, Belgium, April 24-26, 2013},
  year      = {2013},
  url       = {http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-84.pdf},
  timestamp = {Thu, 15 Jul 2021 17:38:03 +0200},
  biburl    = {https://dblp.org/rec/conf/esann/AnguitaGOPR13.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

HAPT 行为模式

属性 参数
类别 Standing, Sitting, Lying, Walking, Walking downstairs, Walking upstairs, Stand-to-sit, Sit-to-stand, Sit-to-lie, Lie-to-sit, Stand-to-lie, Lie-to-stand
传感器位置 未知
传感器模态 A, G
频率 50Hz
大小 75.9MB
源 UCI Machine Learning Repository
下载 下载
提供者 University of California, Irvine

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BibTex 
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@article{DBLP:journals/ijon/Reyes-OrtizOSPA16,
  author    = {Jorge Luis Reyes{-}Ortiz and
               Luca Oneto and
               Albert Sam{\`{a}} and
               Xavier Parra and
               Davide Anguita},
  title     = {Transition-Aware Human Activity Recognition Using Smartphones},
  journal   = {Neurocomputing},
  volume    = {171},
  pages     = {754--767},
  year      = {2016},
  url       = {https://doi.org/10.1016/j.neucom.2015.07.085},
  doi       = {10.1016/j.neucom.2015.07.085},
  timestamp = {Sat, 24 Nov 2018 11:58:30 +0100},
  biburl    = {https://dblp.org/rec/journals/ijon/Reyes-OrtizOSPA16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

MHEALTH 行为模式

属性 参数
类别 Standing still (1 min), Sitting and relaxing (1 min), Lying down (1 min), Walking (1 min), Climbing stairs (1 min), Waist bends forward (20x), Frontal elevation of arms (20x), Knees bending (crouching) (20x), Cycling (1 min), Jogging (1 min), Running (1 min), Jump front & back (20x)
传感器位置 Chest, Left ankle, Right ankle
传感器模态 A, G, M, EC
频率 50Hz
大小 72.1MB
源 UCI Machine Learning Repository
下载 下载
提供者 University of California, Irvine

如果您将该数据集用于研究，请引用以下工作：

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@inproceedings{DBLP:conf/iwaal/BanosGHDPRSV14,
  author    = {Oresti Ba{\~{n}}os and
               Rafael Garc{\'{\i}}a and
               Juan Antonio Holgado Terriza and
               Miguel Damas and
               H{\'{e}}ctor Pomares and
               Ignacio Rojas Ruiz and
               Alejandro Saez and
               Claudia Villalonga},
  editor    = {Leandro Pecchia and
               Liming Luke Chen and
               Chris D. Nugent and
               Jos{\'{e}} Bravo},
  title     = {mHealthDroid: {A} Novel Framework for Agile Development of Mobile
               Health Applications},
  booktitle = {Ambient Assisted Living and Daily Activities - 6th International Work-Conference,
               {IWAAL} 2014, Belfast, UK, December 2-5, 2014. Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {8868},
  pages     = {91--98},
  publisher = {Springer},
  year      = {2014},
  url       = {https://doi.org/10.1007/978-3-319-13105-4\_14},
  doi       = {10.1007/978-3-319-13105-4\_14},
  timestamp = {Sun, 25 Jul 2021 11:43:38 +0200},
  biburl    = {https://dblp.org/rec/conf/iwaal/BanosGHDPRSV14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{Banos2015,
	author = {Banos, Oresti and Villalonga, Claudia and Garcia, Rafael and Saez, Alejandro and Damas, Miguel and Holgado-Terriza, Juan A. and Lee, Sungyong and Pomares, Hector and Rojas, Ignacio},
	da = {2015/08/13},
	doi = {10.1186/1475-925X-14-S2-S6},
	id = {Banos2015},
	isbn = {1475-925X},
	journal = {BioMedical Engineering OnLine},
	number = {2},
	pages = {S6},
	title = {Design, implementation and validation of a novel open framework for agile development of mobile health applications},
	ty = {JOUR},
	url = {https://doi.org/10.1186/1475-925X-14-S2-S6},
	volume = {14},
	year = {2015}
}

USC-HAD 行为模式

属性 参数
类别 Walking forward, Walking left, Walking right, Walking upstairs, Walking downstairs, Running forward, Jumping, Sitting, Standing, Sleeping, Elevator up, Elevator down
传感器位置 Front right hip
传感器模态 A, G
频率 100Hz
大小 42.5MB
源 The USC-SIPI Human Activity Dataset
下载 下载
提供者 University of Southern California

如果您将该数据集用于研究，请引用以下工作：

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@inproceedings{mi12:ubicomp-sagaware,
	author = {Mi Zhang and Alexander A. Sawchuk},
	title = {USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors},
	booktitle = {ACM International Conference on Ubiquitous Computing (Ubicomp) Workshop on Situation, Activity and Goal Awareness (SAGAware)},
	pages = {},
	address = {Pittsburgh, Pennsylvania, USA},
	month = {September},
	year = {2012}
}

DSADS 运动模式

属性 参数
类别 Sitting, Standing, Lying on back, Lying on right side, Ascending stairs, Descending stairs, Standing in an elevator still, Moving around in an elevator,Walking in a parking lot, Walking on a treadmill with a speed of 4 km/h in flat, Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions), Running on a treadmill with a speed of 8 km/h, Exercising on a stepper, Exercising on a cross trainer, Cycling on an exercise bike in horizontal, Cycling on an exercise bike in vertical positions, Rowing, Jumping, Playing basketball
传感器位置 Torso, Right arm, Left arm, Right leg, Left leg
传感器模态 A, G, M
频率 25Hz
大小 163MB
源 UCI Machine Learning Repository
下载 下载
提供者 University of California, Irvine

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@article{DBLP:journals/pr/AltunBT10,
  author    = {Kerem Altun and
               Billur Barshan and
               Orkun Tun{\c{c}}el},
  title     = {Comparative study on classifying human activities with miniature inertial
               and magnetic sensors},
  journal   = {Pattern Recognit.},
  volume    = {43},
  number    = {10},
  pages     = {3605--3620},
  year      = {2010},
  url       = {https://doi.org/10.1016/j.patcog.2010.04.019},
  doi       = {10.1016/j.patcog.2010.04.019},
  timestamp = {Mon, 24 Feb 2020 08:29:59 +0100},
  biburl    = {https://dblp.org/rec/journals/pr/AltunBT10.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/cj/BarshanY14,
  author    = {Billur Barshan and
               Murat Cihan Y{\"{u}}ksek},
  title     = {Recognizing Daily and Sports Activities in Two Open Source Machine
               Learning Environments Using Body-Worn Sensor Units},
  journal   = {Comput. J.},
  volume    = {57},
  number    = {11},
  pages     = {1649--1667},
  year      = {2014},
  url       = {https://doi.org/10.1093/comjnl/bxt075},
  doi       = {10.1093/comjnl/bxt075},
  timestamp = {Sat, 19 Oct 2019 19:10:54 +0200},
  biburl    = {https://dblp.org/rec/journals/cj/BarshanY14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/icpr/AltunB10,
  author    = {Kerem Altun and
               Billur Barshan},
  editor    = {Albert Ali Salah and
               Theo Gevers and
               Nicu Sebe and
               Alessandro Vinciarelli},
  title     = {Human Activity Recognition Using Inertial/Magnetic Sensor Units},
  booktitle = {Human Behavior Understanding, First International Workshop, {HBU}
               2010, Istanbul, Turkey, August 22, 2010. Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {6219},
  pages     = {38--51},
  publisher = {Springer},
  year      = {2010},
  url       = {https://doi.org/10.1007/978-3-642-14715-9\_5},
  doi       = {10.1007/978-3-642-14715-9\_5},
  timestamp = {Sat, 19 Oct 2019 20:00:36 +0200},
  biburl    = {https://dblp.org/rec/conf/icpr/AltunB10.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

REALDISP 运动模式

属性 参数
类别 Walking, Jogging, Running, Jump up, Jump front & back, Jump sideways, Jump leg/arms open/closed, Jump rope, Trunk twist (arms outstretched), Trunk twist (elbows bent), Waist bends forward, Waist rotation, Waist bends (reach foot with opposite hand), Reach heels backwards, Lateral bend (10 to the left + 10 to the right), Lateral bend with arm up (10 to the left + 10 to the right), Repetitive forward stretching, Upper trunk and lower body opposite twist, Lateral elevation of arms, Frontal elevation of arms, Frontal hand claps, Frontal crossing of arms, Shoulders high-amplitude rotation, Shoulders low-amplitude rotation, Arms inner rotation, Knees (alternating) to the breast, Heels (alternating) to the backside, Knees bending (crouching), Knees (alternating) bending forward, Rotation on the knees, Rowing, Elliptical bike, Cycling
传感器位置 Right lower arm (RLA), Right upper arm (RUA), Back (BACK), Left upper arm (LUA), Left lower arm (LLA), Right calf (RC), Right thigh (RT), Left thigh (LT), Left calf (LC)
传感器模态 A, G, M, R
频率 50Hz
大小 2.5GB
源 UCI Machine Learning Repository
下载 下载
提供者 University of California, Irvine

如果您将该数据集用于研究，请引用以下工作：

BibTex 
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@article{DBLP:journals/sensors/BanosTDPR14,
  author    = {Oresti Ba{\~{n}}os and
               M{\'{a}}t{\'{e}} Attila T{\'{o}}th and
               Miguel Damas and
               H{\'{e}}ctor Pomares and
               Ignacio Rojas},
  title     = {Dealing with the Effects of Sensor Displacement in Wearable Activity
               Recognition},
  journal   = {Sensors},
  volume    = {14},
  number    = {6},
  pages     = {9995--10023},
  year      = {2014},
  url       = {https://doi.org/10.3390/s140609995},
  doi       = {10.3390/s140609995},
  timestamp = {Wed, 14 Nov 2018 10:47:49 +0100},
  biburl    = {https://dblp.org/rec/journals/sensors/BanosTDPR14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/huc/BanosDPRTA12,
  author    = {Oresti Ba{\~{n}}os and
               Miguel Damas and
               H{\'{e}}ctor Pomares and
               Ignacio Rojas and
               M{\'{a}}t{\'{e}} Attila T{\'{o}}th and
               Oliver Amft},
  editor    = {Anind K. Dey and
               Hao{-}Hua Chu and
               Gillian R. Hayes},
  title     = {A benchmark dataset to evaluate sensor displacement in activity recognition},
  booktitle = {The 2012 {ACM} Conference on Ubiquitous Computing, Ubicomp '12, Pittsburgh,
               PA, USA, September 5-8, 2012},
  pages     = {1026--1035},
  publisher = {{ACM}},
  year      = {2012},
  url       = {https://doi.org/10.1145/2370216.2370437},
  doi       = {10.1145/2370216.2370437},
  timestamp = {Tue, 29 Dec 2020 18:39:29 +0100},
  biburl    = {https://dblp.org/rec/conf/huc/BanosDPRTA12.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

其他

欢迎大家评论补充其他相关数据集，我会陆续继续补充。