research-article

Nonintrusive Occupant Identification by Sensing Body Shape and Movement

Authors:

Driss Benhaddou,

Omprakash Gnawali,

Jaspal SubhlokAuthors Info & Claims

BuildSys '16: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

Pages 1 - 10

https://doi.org/10.1145/2993422.2993429

Published: 16 November 2016 Publication History

Abstract

The ability to identify people has numerous applications including in smart buildings where the building can be customized to the needs of its occupants or for other applications such as in assisted living and customer behavior analysis in commercial settings. There are different methods used for occupant identification. Some are intrusive such as using cameras or microphone and others require the users to carry mobile gadgets to be identified. In this paper, we present a nonintrusive method to identify people by sensing their body shape and movement. Such information is derived from using ultrasonic sensors to measure the height and width as the occupant walks through the instrumental doorway. In fact, height and width are not unique to every occupant, but extracting a set of features from the variations in height and width makes identification possible. In this study, our system senses a stream of height and width data, recognizes the walking event when a person walks through the door, extracts features that capture a person's movement as well as physical shape. These features are fed to our clustering algorithm that associates each occupant with a distinct cluster. We deployed our system for 1 month. We found out that our approach achieves 95% accuracy with 20 occupants suggesting the suitability of our approach in commercial building settings. In addition, we found out that using girth to distinguish between occupants is more successful than using height.

Supplementary Material

MOV File (p1.mov)

Download
528.14 MB

References

[1]

F. Adib, C.-Y. Hsu, H. Mao, D. Katabi, and F. Durand. Capturing the human figure through a wall. ACM Transactions on Graphics (TOG), 34(6):219, 2015.

Digital Library

[2]

F. Adib, Z. Kabelac, D. Katabi, and R. C. Miller. 3d tracking via body radio reflections. In 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14), pages 317--329, 2014.

Digital Library

[3]

I. Al-Naimi, C. B. Wong, P. Moore, and X. Chen. Advanced approach for indoor identification and tracking using smart floor and pyroelectric infrared sensors. In Information and Communication Systems (ICICS), 2014 5th International Conference on, pages 1--6, April 2014.

[4]

P. Bolliger. Redpin-adaptive, zero-configuration indoor localization through user collaboration. In MELT'08, 2008.

Digital Library

[5]

G. Conte, M. De Marchi, A. A. Nacci, V. Rana, and D. Sciuto. Bluesentinel: a first approach using ibeacon for an energy efficient occupancy detection system. In BuildSys'14, 2014.

Digital Library

[6]

C. DeLoney. Person identification and gender recognition from footstep sound using modulation analysis. 2008.

[7]

J. Doak. An evaluation of feature selection methods and their application to computer security. University of California, Computer Science, 1992.

[8]

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226--231, 1996.

Digital Library

[9]

J. T. Geiger, M. Kneißl, B. W. Schuller, and G. Rigoll. Acoustic gait-based person identification using hidden markov models. In Proceedings of the 2014 Workshop on Mapping Personality Traits Challenge and Workshop, pages 25--30. ACM, 2014.

Digital Library

[10]

A. Ghodsi. Dimensionality reduction a short tutorial. Department of Statistics and Actuarial Science, Univ. of Waterloo, Ontario, Canada, 2006.

[11]

T. W. Hnat, E. Griffiths, R. Dawson, and K. Whitehouse. Doorjamb: unobtrusive room-level tracking of people in homes using doorway sensors. In SenSys'12, 2012.

Digital Library

[12]

A. K. Hrechak and J. A. McHugh. Automated fingerprint recognition using structural matching. Pattern Recognition, 23(8):893--904, 1990.

Digital Library

[13]

Y. Jiang, X. Pan, K. Li, Q. Lv, R. P. Dick, M. Hannigan, and L. Shang. Ariel: Automatic wi-fi based room fingerprinting for indoor localization. In Ubicomp'12, 2012.

Digital Library

[14]

A. Lanitis, C. J. Taylor, and T. F. Cootes. Automatic face identification system using flexible appearance models. Image and vision computing, 13(5):393--401, 1995.

[15]

H. Liu and L. Yu. Toward integrating feature selection algorithms for classification and clustering. Knowledge and Data Engineering, IEEE Transactions on, 17(4):491--502, 2005.

Digital Library

[16]

C. D. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA, 2008.

Digital Library

[17]

C. L. Ogden, N. C. for Health Statistics (US), et al. Mean body weight, height, and body mass index: United States 1960-2002. Department of Health and Human Services, 2004.

[18]

R. J. Orr and G. D. Abowd. The smart floor: a mechanism for natural user identification and tracking. In CHI'00 extended abstracts on Human factors in computing systems, pages 275--276. ACM, 2000.

Digital Library

[19]

S. Pan, A. Chen, and P. Zhang. Securitas: user identification through rgb-nir camera pair on mobile devices. In SPSM'13.

Digital Library

[20]

S. Pan, N. Wang, Y. Qian, I. Velibeyoglu, H. Y. Noh, and P. Zhang. Indoor person identification through footstep induced structural vibration. In HotMobile'15, 2015.

Digital Library

[21]

N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location-support system. In MobiCom'13, 2000.

Digital Library

[22]

J. Ranjan, Y. Yao, and K. Whitehouse. An rf doormat for tracking people's room locations. In Ubicomp'13, 2013.

Digital Library

[23]

V. Srinivasan, J. Stankovic, and K. Whitehouse. Using height sensors for biometric identification in multi-resident homes. In Pervasive computing. Springer, 2010.

Digital Library

[24]

K. P. Subbu and N. Thomas. A location aware personalized smart control system. In IPSN'14, 2014.

[25]

C.-l. Tisse, L. Martin, L. Torres, M. Robert, et al. Person identification technique using human iris recognition. In Proc. Vision Interface, pages 294--299, 2002.

[26]

Y. Zeng, P. H. Pathak, and P. Mohapatra. Wiwho: Wifi-based person identification in smart spaces. In 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pages 1--12. IEEE, 2016.

Digital Library

[27]

J. Zhang, B. Wei, W. Hu, S. S. Kanhere, and A. Tan. Human identification using wifi signal. In 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pages 1--2. IEEE, 2016.

Cited By

Nikseresht FCampbell B(2023)FTM-Sense: Robust Sensor-free Occupancy Sensing Leveraging WiFi Fine Time MeasurementProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623741(140-148)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623741
Neu CGibson JLunardi RLeesakul NMorisset C(2023)A Blockchain-Based Architecture to Manage User Privacy Preferences on Smart Shared Spaces PrivatelyData Privacy Management, Cryptocurrencies and Blockchain Technology10.1007/978-3-031-25734-6_9(136-150)Online publication date: 24-Feb-2023
https://doi.org/10.1007/978-3-031-25734-6_9
Monti LTse RTang SMirri SDelnevo GManiezzo VSalomoni P(2022)Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus ContextSensors10.3390/s2210369222:10(3692)Online publication date: 12-May-2022
https://doi.org/10.3390/s22103692
Show More Cited By

Recommendations

Sonicdoor: scaling person identification with ultrasonic sensors by novel modeling of shape, behavior and walking patterns
BuildSys '17: Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments

Non-intrusive occupant identification enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current non-intrusive identification techniques do not scale beyond 20 people whereas commercial buildings can have ...
Smart Home Occupant Identification via Sensor Fusion Across On-Object Devices
Special Issue on BuildSys'17

Occupant identification proves crucial in many smart home applications such as automated home control and activity recognition. Previous solutions are limited in terms of deployment costs, identification accuracy, or usability. We propose SenseTribute, ...
Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors

Research concern has been directed toward automatic identification and tracking of people within the home environment to support smart home services such as care services for elderly and disadvantaged people to enable and prolong their independent ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

BuildSys '16: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

November 2016

273 pages

ISBN:9781450342643

DOI:10.1145/2993422

Copyright © 2016 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 the author(s) 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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

BuildSys '16

Sponsor:

BuildSys '16: The 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

November 16 - 17, 2016

CA, Palo Alto, USA

Acceptance Rates

Overall Acceptance Rate 148 of 500 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

28
Total Citations
View Citations
329
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Nikseresht FCampbell B(2023)FTM-Sense: Robust Sensor-free Occupancy Sensing Leveraging WiFi Fine Time MeasurementProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623741(140-148)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623741
Neu CGibson JLunardi RLeesakul NMorisset C(2023)A Blockchain-Based Architecture to Manage User Privacy Preferences on Smart Shared Spaces PrivatelyData Privacy Management, Cryptocurrencies and Blockchain Technology10.1007/978-3-031-25734-6_9(136-150)Online publication date: 24-Feb-2023
https://doi.org/10.1007/978-3-031-25734-6_9
Monti LTse RTang SMirri SDelnevo GManiezzo VSalomoni P(2022)Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus ContextSensors10.3390/s2210369222:10(3692)Online publication date: 12-May-2022
https://doi.org/10.3390/s22103692
Billah MSaoda NSobral VRouth TWang WCampbell BOrtiz J(2022)SolarWalkProceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3563357.3564073(178-187)Online publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1145/3563357.3564073
Fagert JMirshekari MZhang PNoh H(2022)Recursive Sparse Representation for Identifying Multiple Concurrent Occupants Using Floor Vibration SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172296:1(1-33)Online publication date: 29-Mar-2022
https://dl.acm.org/doi/10.1145/3517229
Hasan RHasan R(2022)X-Fidence: Post-Pandemic Wellness By Density Monitoring with Privacy Preservation2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC49033.2022.9700586(578-583)Online publication date: 8-Jan-2022
https://doi.org/10.1109/CCNC49033.2022.9700586
Navarro RRuiz AMolina FRomero MChaparro JAlises DLopez J(2022)Indoor occupancy estimation for smart utilities: A novel approach based on depth sensorsBuilding and Environment10.1016/j.buildenv.2022.109406222(109406)Online publication date: Aug-2022
https://doi.org/10.1016/j.buildenv.2022.109406
Le TVo MKieu THwang ERho SBaik S(2020)Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart BuildingSensors10.3390/s2009266820:9(2668)Online publication date: 7-May-2020
https://doi.org/10.3390/s20092668
Trivedi DBadarla V(2019)Occupancy detection systems for indoor environments: A survey of approaches and methodsIndoor and Built Environment10.1177/1420326X1987562129:8(1053-1069)Online publication date: 16-Sep-2019
https://doi.org/10.1177/1420326X19875621
Djenouri DLaidi RDjenouri YBalasingham I(2019)Machine Learning for Smart Building ApplicationsACM Computing Surveys10.1145/331195052:2(1-36)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3311950
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents