research-article

Using mobile phones to determine transportation modes

Authors:

Deborah Estrin,

Mani SrivastavaAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 6, Issue 2

Article No.: 13, Pages 1 - 27

https://doi.org/10.1145/1689239.1689243

Published: 02 March 2010 Publication History

Abstract

As mobile phones advance in functionality and capability, they are being used for more than just communication. Increasingly, these devices are being employed as instruments for introspection into habits and situations of individuals and communities. Many of the applications enabled by this new use of mobile phones rely on contextual information. The focus of this work is on one dimension of context, the transportation mode of an individual when outside. We create a convenient (no specific position and orientation setting) classification system that uses a mobile phone with a built-in GPS receiver and an accelerometer. The transportation modes identified include whether an individual is stationary, walking, running, biking, or in motorized transport. The overall classification system consists of a decision tree followed by a first-order discrete Hidden Markov Model and achieves an accuracy level of 93.6% when tested on a dataset obtained from sixteen individuals.

References

[1]

Agapie, E., Chen, G., Houston, D., Howard, E., Kim, J., Mun, M., Mondschein, A., Reddy, S., Rosario, R., Ryder, J., Steiner, A., Burke, J., Estrin, D., Hansen, M., and Rahimi, M. 2008. Seeing our signals: Combining location traces and web-based models for personal discovery. In Proceedings of the 9th Workshop on Mobile Computing Systems and Applications. ACM, New York, 6--10.

Digital Library

[2]

Anderson, I., and Muller, H. 2006. Practical activity recognition using GSM data. Technical Report CSTR-06-016, Department of Computer Science, University of Bristol.

[3]

Antonsson, E. and Mann, R. 1985. The frequency content of gait. J. Biomech. 18, 1, 39--47.

[4]

Banerjee, N., Rahmati, A., Corner, M., Rollins, S., and Zhong, L. 2007. Users and batteries: Interactions and adaptive energy management in mobile systems. In Lecture Notes in Computer Science vol. 4717. Springer-Verlag, Berlin, Germany.

Digital Library

[5]

Bao, L. and Intille, S. 2004. Activity recognition from user-annotated acceleration data. In Lecture Notes in Computer Science. Springer-Verlag, Berlin, Germany, 1--17.

[6]

BodyMedia. 2008. Sensewear wms. http://www.sensewear.com/images/WMSbrochure.pdf.

[7]

Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., and Srivastava, M. 2006. Participatory sensing. In World Sensor Web Workshop. ACM, New York, 1--5.

[8]

CarbonHero. 2008. Carbon diem - Is that knowledge now. http://carbondiem.com.

[9]

Chen, C., Anton, S., and Helal, A. 2008. A brief survey of physical activity monitoring devices. University of Florida Tech Report MPCL-08-09.

[10]

Chen, M., Sohn, T., Chmelev, D., Haehnel, D., Hightower, J., Hughes, J., LaMarca, A., Potter, F., Smith, I., and Varshavsky, A. 2006. Practical metropolitan-scale positioning for gsm phones. In Lecture Notes in Computer Science 4206, Springer-Verlag, Berlin, Germany. vol. 225.

Digital Library

[11]

Compumine. 2008. Evaluating a Classification Model. http://www.compumine.com/web/public/newsletter/20071/precision-recall.

[12]

Consolvo, S., Klasnja, P., McDonald, D., Avrahami, D., Froehlich, J., LeGrand, L., Libby, R., Mosher, K., and Landay, J. 2008a. Flowers or a robot army&quest;: Encouraging awareness & activity with personal, mobile displays. In Proceedings of the 10th International Conference on Ubiquitous Computing. ACM New York, 54--63.

Digital Library

[13]

Consolvo, S., McDonald, D., Toscos, T., Chen, M., Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., Smith, I., and Landay, J. A. 2008b. Activity sensing in the wild: A field trial of ubifit garden. In Proceedings of the 26th Annual Conference on Human Factors in Computing Systems. ACM, New York, 1797--1806.

Digital Library

[14]

Constandache, I., Gaonkar, S., Sayler, M., Choudhury, R., and Cox, L. 2009. EnLoc: Energy-efficient localization for mobile phones. IEEE Computer Society Press, Los Alamitos, CA. Infocom Mini Conference

[15]

Constandache, I., Sayler, M., Gaonkar, S., Choudhury, R., and Cox, L. 2008. Energy-aware localization using mobile phones. Mobisys Poster. www.ee.duke.edu.

[16]

Crouter, S., Schneider, P., Karabulut, M., and Bassett Jr, D. 2003. Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Med. Sci. Sports Exercise 35, 8, 1455.

[17]

Denning, T., Andrew, A., Chaudhri, R., Hartung, C., Lester, J., Borriello, G., and Duncan, G. 2009. BALANCE: Towards a usable pervasive wellness application with accurate activity inference. In Proceedings of the 10th IEEE Workshop on Mobile Computing Systems and Applications (HotMobile 2009). IEEE Computer Society Press, Los Alamitos, CA.

Digital Library

[18]

Duda, R., Hart, P., and Stork, D. 2000. Pattern Clasification. Wiley, New York.

Digital Library

[19]

Esposito, F., Malerba, D., Semeraro, G., and Kay, J. 1997. A comparative analysis of methods for pruning decision trees. In Pattern Analysis and Machine Intelligence, IEEE Trans. 19. 476--491.

Digital Library

[20]

Fábián, Á., Győrbíró, N., and Hományi, G. 2008. Activity recognition system for mobile phones using the MotionBand device. In Proceedings of the 1st International Conference on Mobile Wireless Middleware, Operating Systems, and Applications. ICST, Brussels, Belgium.

Digital Library

[21]

Farringdon, J., Moore, A., Tilbury, N., Church, J., and Biemond, P. 1999. Wearable sensor badge & sensor jacket for context awareness. In Proceedings of the 3rd International Symposium on Wearable Computers. 107--113.

Digital Library

[22]

Froehlich, J., Dillahunt, T., Klasnja, P., Mankoff, J., Consolvo, S., Harrison, B., and Landay, J. 2009. UbiGreen: Investigating a mobile tool for tracking and supporting green transportation habits. Computer Human Interaction (CHI). ACM, New York.

Digital Library

[23]

Ganti, R., Jayachandran, P., Abdelzaher, T., and Stankovic, J. 2006. SATIRE: A software architecture for smart AtTIRE. In Proceedings of the 4th International Conference on Mobile Systems, Applications and Services. ACM New York, 110--123.

Digital Library

[24]

Gaonkar, S., Li, J., Choudhury, R., and Cox, L. 2008. Micro-blog: Sharing and querying content through mobile phones and social participation. In Proceedings of the 6th International Conference on Mobile Systems, Applications and Services. ACM, New York, 174--186.

Digital Library

[25]

Ghiselli, E. 1964. Theory of Psychological Measurement. McGraw-Hill, New York.

[26]

GHMM. 2008. The General Hidden Markov Model Library. http://ghmm.org.

[27]

Goertzel, G. 1958. An algorithm for the evaluation of finite trigonometric series. Amer. Math. Monthly 65, 1, 34--35.

[28]

Huynh, T. and Schiele, B. 2005. Analyzing features for activity recognition. In Proceedings Conference on Smart Objects and Ambient Intelligence. ACM, New York, 159--163.

Digital Library

[29]

ImpactSports. 2008. ePulse - Armband Heart Rate Monitor. http://impactsports.com.

[30]

Jaakkola, T., and Haussler, D. 1999. Exploiting generative models in discriminative classifiers. Adv. Neural Info. Proc. Syst. 487--493.

Digital Library

[31]

Kao, J., Lam, R., Pong, G., Talukdar, T., and Wong, J. 2008. Ecorio - Track Your Mobile Carbon Footprint. http://ecorio.org.

[32]

Karabulut, M., Crouter, S., and Bassett, D. 2005. Comparison of two waist-mounted and two ankle-mounted electronic pedometers. Europ. J. Applied Physiology 95, 4, 335--343.

[33]

Kern, N., Schiele, B., and Schmidt, A. 2003. Multi-sensor activity context detection for wearable computing. In Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, 220--234.

[34]

Krause, A., Ihmig, M., Rankin, E., Leong, D., Gupta, S., Siewiorek, D., Smailagic, A., Deisher, M., and Sengupta, U. 2005. Trading off prediction accuracy and power consumption for context-aware wearable computing. In Proceedings of the 9th IEEE International Symposium on Wearable Computers. IEEE Computer Society Press, Los Alamitos, CA. 20, 25.

Digital Library

[35]

LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J., Sohn, T., Howard, J., Hughes, J., Potter, F., Tabert, J., Powledge, P., Borriello, G., and Schilib, B. 2005. Place lab: Device positioning using radio beacons in the wild. In Proceedings of PERVASIVE. Vol. 3468. Springer-Verlag, Berlin, Germany, 7.

Digital Library

[36]

Lester, J., Choudhury, T., and Borriello, G. 2006. A practical approach to recognizing physical activities. In Lecture Notes in Computer Science, vol. 3968, Springer-Verlag, Berlin, Germany, 1.

Digital Library

[37]

Lester, J., Choudhury, T., Kern, N., Borriello, G., and Hannaford, B. 2005. A hybrid discriminative/generative approach for modeling human activities. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2005).

Digital Library

[38]

Lester, J., Hurvitz, P., Chaudhri, R., Hartung, C., and Borriello, G. 2008. MobileSense - Sensing modes of transportation in studies of the built environment. In Proceedings of the International Workshop on Urban, Community, and Social Applications of Networked Sensing Systems. ACM, New York.

[39]

Li, K., Sohn, T., Huang, S., and Griswold, W. 2008. Peopletones: A system for the detection and notification of buddy proximity on mobile phones. In Proceedings of the International Conference on Mobile Systems, Applications, and Services. ACM, New York.

Digital Library

[40]

Liao, L., Patterson, D., Fox, D., and Kautz, H. 2007. Learning and inferring transportation routines. Artif. Intell. 171, 5-6, 311--331.

Digital Library

[41]

Marmasse, N., Schmandt, C., and Spectre, D. 2004. WatchMe: communication and awareness between members of a closely-knit group. In Lecture Notes in Computer Science. Springer-Verlag, Berlin, Germany, 214--231.

[42]

Martinez, A. and Kak, A. 2001. PCA versus LDA. Patt. Anal. Mach. Intell. IEEE Trans. 23, 2, 228--233.

Digital Library

[43]

Miluzzo, E., Lane, N., Eisenman, S., and Campbell, A. 2007. Cenceme-injecting sensing presence into social networking applications. In Lecture Notes in Computer Science. vol. 4793. Springer-Verlag, Berlin, Germany, 1.

Digital Library

[44]

Miluzzo, E., Lane, N., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S., Zheng, X., and Campbell, A. 2008. Sensing meets mobile social networks: The design implementation and evaluation of the cenceMe application. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems. ACM, New York. 337--350.

Digital Library

[45]

Mohan, P., Padmanabhan, V., and Ramjee, R. 2008. Trafficsense: Rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of the Conference on Embedded Networked Sensor Systems (SenSys). ACM, New York, 323--336.

Digital Library

[46]

Mun, M., Estrin, D., Burke, J., and Hansen, M. 2008. Parsimonious mobility classification using GSM and WiFi traces. In Proceedings of the 5th Workshop on Embedded Networked Sensors (HotEmNets).

[47]

Mun, M., Reddy, S., Samanta, V., Shilton, K., Howard, E., Le, D., Ramanthan, N., Burke, J., Estrin, D., Hansen, M., and Srivastava, M. 2009a. CycleSense: Sensing and mapping for better biking. In Proceedings of the Active Living Conference.

[48]

Mun, M., Reddy, S., Shilton, K., Yau, N., Boda, P., Burke, J., Estrin, D., Hansen, M., Howard, E., and West, R. 2009b. PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In Proceedings of the International Conference on Mobile Systems, Applications, and Services. ACM, New York, 55--68.

Digital Library

[49]

Nike. 2008. Apple Computer, Inc., Nike+ iPod Sport Kit. http://apple.com/nike.

[50]

Nokia. 2008. Nokia n95. http://www.nseries.com.

[51]

Nokia-Research. 2009. Nokia simple context service. https://alpha.nokoscope.com/eb2/.

[52]

Omron. 2008a. Omron hj-150. http://www.omronhealthcare.com/product/detail.asp?p=1129&t=187.

[53]

Omron. 2008b. Omron hj-720itc. http://www.omronhealthcare.com/product/1132-187-fitness-diagnostics-pocket-pedometer-with-pc-software-hj-720itc.

[54]

Patterson, D., Liao, L., Fox, D., and Kautz, H. 2003. Inferring high-level behavior from low-level sensors. In Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, 73--89.

[55]

Peterson, N., Estrin, D., and Burke, J. 2009. Project footstep - Linking physical activity with location. http://urban.cens.ucla.edu/projects/footstep.

[56]

Pham, N., and Abdelzaher, T. 2008. Robust dynamic human activity recognition based on relative energy allocation. In Lecture Notes in Computer Science, vol. 5067, Springer-Verlag, Berlin, Germany, 525--530.

Digital Library

[57]

Randell, C., and Muller, H. 2000. Context awareness by analysing accelerometer data. In Proceedings of the 4th International Symposium on Wearable Computers. IEEE Computer Society Press, Los Alamitos, CA, 175--176.

Digital Library

[58]

Ravi, N., Scott, J., Han, L., and Iftode, L. 2008. Context-aware battery management for mobile phones. In Proceedings of the 6th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom2008). IEEE Computer Society Press, Los Alamitos, CA, 224--233.

Digital Library

[59]

Reddy, S., Samanta, V., Burke, J., Estrin, D., and Srivastava, M. 2009a. Walkability - Safe Walkable Streets in Modern Cities. http://urban.cens.ucla.edu/projects/walkability.

[60]

Reddy, S., Shilton, K., Burke, J., Estrin, D., Hansen, M., and Srivastava, M. 2009b. Using context annotated mobility profiles to recruit data collectors in participatory sensing. In Proceedings of the Symposium on Location and Context Awareness (LOCA). Springer-Verlag, Berlin, Germany.

Digital Library

[61]

Saponas, T., Lester, J., Froehlich, J., Fogarty, J., and Landay, J. 2008. iLearn on the iPhone: Real-time human activity classification on commodity mobile phones. University of Washington CSE Tech Report UW-CSE-08-04-02.

[62]

Schneider, P., Crouter, S., and Bassett, D. 2004. Pedometer measures of free-living physical activity: comparison of 13 models. Med. Sci. Sports Exerc 36, 2, 331--335.

[63]

Skyhook. 2009. Skyhook Wireless: Coverage. http://www.skyhookwireless.com/howitworks/coverage.php.

[64]

Sohn, T., Varshavsky, A., LaMarca, A., Chen, M., Choudhury, T., Smith, I., Consolvo, S., Hightower, J., Griswold, W., and de Lara, E. 2006. Mobility detection using everyday GSM traces. In Lecture Notes in Computer Science, vol. 4206, Springer-Verlag, Berlin, Germany, 212.

Digital Library

[65]

Sun, M., and Hill, J. 1993. A method for measuring mechanical work and work efficiency during human activities. J. Biomech. 26, 3, 229--241.

[66]

Tapia, E., Intille, S., and Larson, K. 2004. Activity recognition in the home using simple and sbiquitous sensors. In Lecture Notes in Computer Science. Springer-Verlag, Berlin, Germany, 158--175.

[67]

Welbourne, E., Lester, J., LaMarca, A., and Borriello, G. 2005. Mobile context inference using low-cost sensors. In Proceedings of the 1st International Symposium on Location and Context Awareness. Springer-Verlag, Berlin, Germany, 254--263.

Digital Library

[68]

Wigle. 2009. WiGLE - Wireless Geographic Logging Engine. http://wigle.net/gps/gps/Map/onlinemap2/.

[69]

Winter, D. 1990. Biomechanics and Motor Control of Human Movement. Wiley, New York.

[70]

Witten, I. and Frank, E. 2005. Data mining: Practical machine learning tools and techniques. Morgan-Kaufmann, San Francisco, CA.

Digital Library

[71]

Yu, L. and Liu, H. 2003. Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of the 20th International Conference on Machine Learning. 856--864.

[72]

Zheng, Y., Li, Q., Chen, Y., Xie, X., and Ma, W. 2008. Understanding mobility based on GPS data. In Ubiquitous Computing. ACM New York, 312--321.

Digital Library

Cited By

Molina-Campoverde JRivera-Campoverde NMolina Campoverde PBermeo Naula A(2024)Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPSSensors10.3390/s2412388424:12(3884)Online publication date: 15-Jun-2024
https://doi.org/10.3390/s24123884
Li GXu RShi TDeng XLiu YDi DZhao CLiu G(2024)Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal CharacteristicsISPRS International Journal of Geo-Information10.3390/ijgi1309031413:9(314)Online publication date: 30-Aug-2024
https://doi.org/10.3390/ijgi13090314
Van Der Donckt JVan Der Donckt JVan Hoecke SKostakos VKay JHoang T(2024)Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data ModalitiesCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678463(597-602)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678463
Show More Cited By

Index Terms

Using mobile phones to determine transportation modes
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Recommendations

Transportation mode detection using mobile phones and GIS information
GIS '11: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile ...
Personal customisation of mobile phones: a case study
NordiCHI '06: Proceedings of the 4th Nordic conference on Human-computer interaction: changing roles

Mobile phones are highly personal, customisable mobile computing devices that allow users to precisely control how they interact with the device and their environment. This paper examines the process of customisation and seeks to identify how this ...
Use of mobile phones by male and female Greek students

Mobile technology is a continuously growing domain, and research activities regarding its use are quite intensive. A questionnaire regarding the use of mobile devices was developed and distributed to 416 students in a Greek University. There were completed ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 6, Issue 2

February 2010

270 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/1689239

Issue’s Table of Contents

Copyright © 2010 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 02 March 2010

Accepted: 01 June 2009

Revised: 01 April 2009

Received: 01 December 2008

Published in TOSN Volume 6, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Division of Computer and Network Systems

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

653
Total Citations
View Citations
5,950
Total Downloads

Downloads (Last 12 months)200
Downloads (Last 6 weeks)26

Reflects downloads up to 20 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Molina-Campoverde JRivera-Campoverde NMolina Campoverde PBermeo Naula A(2024)Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPSSensors10.3390/s2412388424:12(3884)Online publication date: 15-Jun-2024
https://doi.org/10.3390/s24123884
Li GXu RShi TDeng XLiu YDi DZhao CLiu G(2024)Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal CharacteristicsISPRS International Journal of Geo-Information10.3390/ijgi1309031413:9(314)Online publication date: 30-Aug-2024
https://doi.org/10.3390/ijgi13090314
Van Der Donckt JVan Der Donckt JVan Hoecke SKostakos VKay JHoang T(2024)Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data ModalitiesCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678463(597-602)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678463
Phan HNguyen TNguyen KVo NHa ALe NKostakos VKay JHoang T(2024)Enhanced Transportation and Locomotion Mode Recognition through Difference and Variance Analysis in Inertial Sensing DataCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678461(585-590)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678461
Musleh MMokbel M(2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656470
Song XSun HDong YPei YWang S(2024)Deep Learning-Based Wearable Human Activity Recognition: Model and Performance AnalysisProceedings of the 2024 8th International Conference on Control Engineering and Artificial Intelligence10.1145/3640824.3640830(30-36)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3640824.3640830
Teng SD'Alfonso SKostakos V(2024)A Tool for Capturing Smartphone Screen TextProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642347(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642347
Aziz AYoukee NAhmed FTasnim SFahim-Ul-Islam MChakrabarty A(2024)Enhancing Transparency in Transport Mode Detection: An Interpretable Ensemble Model Classifier2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS)10.1109/iCACCESS61735.2024.10499511(1-6)Online publication date: 8-Mar-2024
https://doi.org/10.1109/iCACCESS61735.2024.10499511
Nguyen NZubair MZuniga ATarkoma SHui PLee HPerrault SAmmar MFlores HNurmi P(2024)The Price is Right? The Economic Value of Sharing SensorsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333007111:3(3468-3482)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3330071
Liu YWijewickrema SBester CO’Leary SBailey J(2024)Time Series Representation Learning with Supervised Contrastive Temporal Transformer2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650516(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650516
Show More Cited By

View Options

Get Access

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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents