research-article

USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors

Authors:

Alexander A. SawchukAuthors Info & Claims

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Pages 1036 - 1043

https://doi.org/10.1145/2370216.2370438

Published: 05 September 2012 Publication History

Abstract

Many ubiquitous computing applications involve human activity recognition based on wearable sensors. Although this problem has been studied for a decade, there are a limited number of publicly available datasets to use as standard benchmarks to compare the performance of activity models and recognition algorithms. In this paper, we describe the freely available USC human activity dataset (USC-HAD), consisting of well-defined low-level daily activities intended as a benchmark for algorithm comparison particularly for healthcare scenarios. We briefly review some existing publicly available datasets and compare them with USC-HAD. We describe the wearable sensors used and details of dataset construction. We use high-precision well-calibrated sensing hardware such that the collected data is accurate, reliable, and easy to interpret. The goal is to make the dataset and research based on it repeatable and extendible by others.

References

[1]

Caltech 101 Dataset. http://www.vision.caltech.edu/Image_Datasets/Caltech101/.

[2]

MotionNode IMU platform. http://www.motionnode.com/.

[3]

UC Irvine Machine Learning Repository. http://archive.ics.uci.edu/ml/.

[4]

USC-HAD Dataset. http://sipi.usc.edu/HAD/.

[5]

S. Bamberg, A. Benbasat, D. Scarborough, D. Krebs, and J. Paradiso. Gait analysis using a shoe-integrated wireless sensor system. In IEEE Transactions on Information Technology in Biomedicine, 2006.

Digital Library

[6]

L. Bao and S. Intille. Activity Recognition from User-Annotated Acceleration Data. In International Conference on Pervasive Computing, pages 1--17, Vienna, Austria, 2004.

[7]

T. Choudhury and et al. The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Computing, 7(2):32--41, April 2008.

Digital Library

[8]

T. Choudhury, M. Philipose, D. Wyatt, and J. Lester. Towards activity databases: Using sensors and statistical models to summarize people's lives. IEEE Data Engineering Bulletin, 29(1):49--58, 2006.

[9]

J. C. F. Ichikawa and R. Grignani. Wheres the phone? a study of mobile phone location in public spaces. In Proceedings of Mobility 2005 Conference on Mobile Technology, Applications, and Systems., pages 797--804, 2005.

[10]

J. Farringdon, A. J. Moore, N. Tilbury, J. Church, and P. Biemond. Wearable sensor badge and sensor jacket for context awareness. In International Symposium on Wearable Computers, San Francisco, CA, USA, 1999.

Digital Library

[11]

E. Hoque, R. Dickerson, and J. Stankovic. Monitoring body positions and movements during sleep using WISPs. In Wireless Health, pages 44--53, San Diego, CA, USA, 2010.

Digital Library

[12]

D. Huynh. Human activity recognitionwith wearable sensors. Ph.D. Thesis, 2008.

[13]

J. Lester, T. Choudhury, and G. Borriello. A practical approach to recognizing physical activities. In International Conference on Pervasive Computing, pages 1--16, Dublin, Ireland, 2006.

Digital Library

[14]

J. Lester, T. Choudhury, N. Kern, G. Borriello, and B. Hannaford. A hybrid discriminative/generative approach for modeling human activities. In International Joint Conference on Artificial Intelligence (IJCAI), pages 766--772, Edinburgh, Scotland, 2005.

Digital Library

[15]

M. Li and et al. Multimodal physical activity recognition by fusing temporal and cepstral information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(4):369--380, 2010.

[16]

Q. Li, J. A. Stankovic, M. A. Hanson, A. T. Barth, J. Lach, and G. Zhou. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In International Workshop on Wearable and Implantable Body Sensor Networks, pages 138--143, Berkeley, CA, USA, 2009.

Digital Library

[17]

U. Maurer, A. Rowe, A. Smailagic, and D. Siewiorek. eWatch: A wearable sensor and notification platform. In International Workshop on Wearable and Implantable Body Sensor Networks, Cambridge, MA, USA, 2006.

Digital Library

[18]

D. Paul and J. Baker. The design for the wall street journal-based csr corpus. In Proceedings of the workshop on Speech and Natural Language, pages 357--362, Stroudsburg, PA, USA, 1992.

Digital Library

[19]

D. Roggen and et al. Collecting complex activity data sets in highly rich networked sensor environments. In International Conference on Networked Sensing Systems (INSS), Kassel, Germany, 2010.

[20]

E. Tapia, S. Intille, L. Lopez, and K. Larson. The design of a portable kit of wireless sensors for naturalistic data collection. In International Conference on Pervasive Computing, pages 117--134, Dublin, Ireland, 2006.

Digital Library

[21]

F. Torre, J. Hodgins, J. Montano, S. Valcarcel, R. Forcada, and J. Macey. Guide to the carnegie mellon university multimodal activity (CMU-MMAC) database. In Technical report CMU-RI-TR-08-22, Robotics Institute, Carnegie Mellon University, 2009.

[22]

P. Turaga, R. Chellappa, V. Subrahmanian, and O. Udrea. Machine Recognition of Human Activities: A Survey. IEEE Transactions on Circuits and Systems for Video Technology, 18(11):1473--1488, September 2008.

Digital Library

[23]

A. Yang, R. Jarafi, S. Sastry, and R. Bajcsy. Distributed recognition of human actions using wearable motion sensor networks. In Journal of Ambient Intelligence and Smart Environments (JAISE), 2009.

Digital Library

[24]

M. Zhang and A. A. Sawchuk. A customizable framework of body area sensor network for rehabilitation. In International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), pages 1--6, Bratislava, Slovak Republic, November 2009.

[25]

M. Zhang and A. A. Sawchuk. A feature selection-based framework for human activity recognition using wearable multimodal sensors. In International Conference on Body Area Networks (BodyNets), Beijing, China, November 2011.

Digital Library

[26]

M. Zhang and A. A. Sawchuk. Manifold learning and recognition of human activity using body-area sensors. In IEEE International Conference on Machine Learning and Applications (ICMLA), pages 7--13, Honolulu, Hawaii, USA, December 2011.

Digital Library

[27]

M. Zhang and A. A. Sawchuk. Motion primitive-based human activity recognition using a bag-of-features approach. In ACM SIGHIT International Health Informatics Symposium (IHI), pages 631--640, Miami, Florida, USA, January 2012.

Digital Library

Cited By

Ko JKim SSul JKim S(2025)Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity RecognitionSensors10.3390/s2504118425:4(1184)Online publication date: 14-Feb-2025
https://doi.org/10.3390/s25041184
Lee S(2025)Real Steps or Not: Auto-Walker Detection in Move-to-Earn ApplicationsSensors10.3390/s2504100225:4(1002)Online publication date: 7-Feb-2025
https://doi.org/10.3390/s25041002
Cao MWan JGu X(2025)CLEAR: Multimodal Human Activity Recognition via Contrastive Learning Based Feature Extraction RefinementSensors10.3390/s2503089625:3(896)Online publication date: 1-Feb-2025
https://doi.org/10.3390/s25030896
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Index Terms

USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

September 2012

1268 pages

ISBN:9781450312240

DOI:10.1145/2370216

General Chair:
Anind K. Dey
Carnegie Mellon University
,
Program Chairs:
Hao-Hua Chu
National Taiwan University, Taiwan
,
Gillian Hayes
University of California, Irvine

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 September 2012

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Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing

September 5 - 8, 2012

Pennsylvania, Pittsburgh

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

Ko JKim SSul JKim S(2025)Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity RecognitionSensors10.3390/s2504118425:4(1184)Online publication date: 14-Feb-2025
https://doi.org/10.3390/s25041184
Lee S(2025)Real Steps or Not: Auto-Walker Detection in Move-to-Earn ApplicationsSensors10.3390/s2504100225:4(1002)Online publication date: 7-Feb-2025
https://doi.org/10.3390/s25041002
Cao MWan JGu X(2025)CLEAR: Multimodal Human Activity Recognition via Contrastive Learning Based Feature Extraction RefinementSensors10.3390/s2503089625:3(896)Online publication date: 1-Feb-2025
https://doi.org/10.3390/s25030896
Guo XKim YNing XMin S(2025)Enhancing the Transformer Model with a Convolutional Feature Extractor Block and Vector-Based Relative Position Embedding for Human Activity RecognitionSensors10.3390/s2502030125:2(301)Online publication date: 7-Jan-2025
https://doi.org/10.3390/s25020301
Shehzad SBuriro AUllah SAhmad T(2025)Position-Agnostic Smartphone Placement Detection for Improved Reliability in Human Activity RecognitionIntelligenza Artificiale: The international journal of the AIxIA10.1177/17248035241312104Online publication date: 4-Feb-2025
https://doi.org/10.1177/17248035241312104
Chen ZZou HWang LWang BZhang FMa SPan YLi J(2025)Eff-WHAR: A Lightweight Design for Efficient Wearable Sensor-Based Human Activity RecognitionIEEE Sensors Journal10.1109/JSEN.2024.350996125:2(3935-3948)Online publication date: 15-Jan-2025
https://doi.org/10.1109/JSEN.2024.3509961
Chen XZhou XSun MWang H(2025)Temporal Contrastive Learning for Sensor-Based Human Activity Recognition: A Self-Supervised ApproachIEEE Sensors Journal10.1109/JSEN.2024.349193325:1(1839-1850)Online publication date: 1-Jan-2025
https://doi.org/10.1109/JSEN.2024.3491933
Teng QLi WHu GShu YLiu Y(2025)Innovative Dual-Decoupling CNN With Layer-Wise Temporal-Spatial Attention for Sensor-Based Human Activity RecognitionIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.348852829:2(1035-1048)Online publication date: Feb-2025
https://doi.org/10.1109/JBHI.2024.3488528
Zhang QWei MLiu JLi HXu R(2025)FedFP: personalized federated learning based on feature extraction guidance and parameter decouplingEngineering Research Express10.1088/2631-8695/adabb87:1(015238)Online publication date: 30-Jan-2025
https://doi.org/10.1088/2631-8695/adabb8
Yu XAl-qaness M(2025)ASK-HAR: Attention-Based Multi-Core Selective Kernel Convolution Network for Human Activity RecognitionMeasurement10.1016/j.measurement.2024.115981242(115981)Online publication date: Jan-2025
https://doi.org/10.1016/j.measurement.2024.115981
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