Comparative Techniques Using Hierarchical Modelling and Machine Learning for Procedure Recognition in Smart Hospitals


  • Shaheena Noor Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan
  • Muhammad Aamir Department of Telecommunication Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan
  • Najma Ismat Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan
  • Muhammad Imran Saleem Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan



Procedure recognition • Inside-out Vision • Machine Learning • Artificial Neural Network, 6G-enabled applications


6G is one of the key cornerstone elements of the futuristic smart system setup – the others being cloud computing, big data, wearable devices and Artificial Intelligence. Also, smart offices and homes have become even more popular than before, because of the advancement in computer vision and Machine Learning (ML) technologies. Recognition of human actions and situations are fundamental components of such systems, especially in complex environments like healthcare, for example at the dentist clinic, where we need cues such as eye movement to distinguish procedures being undertaken. In this work, we compare models based on hierarchical modelling and machine learning to identify the dental procedure. We used the objects seen while following the eye trajectories and focussed on elements including material used for treatment, equipment involved and the teeth conditions i.e. symptoms. Our experiments showed that using Artificial Neural Network (ANN) increased the accuracy of prediction compared to hierarchical modelling. Our experiments show an improvement in accuracy for each of the constituent parameters i.e., symptom (ANN: 95.58% vs. Hierarchical: 45.68%), material (ANN: 86.32% vs. Hierarchical: 45.18%) and equipment (ANN: 92.65% vs. Hierarchical: 59.39%).


Download data is not yet available.

Author Biographies

Shaheena Noor, Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Shaheena Noor graduated from Hamdard University with a BS degree in Computer Engineering in 2003, the MS degree in computer systems from NED University of Engineering & Technology in 2007, and the PhD degree in Computer Engineering from Hamdard University in 2019, respectively. She is employed as an Assistant Professor at the Department of Computer Engineering, Sir Syed University of Engineering & Technology (SSUET), Karachi, Pakistan. Object recognition, activity recognition and prediction are among her research interest.

Muhammad Aamir, Department of Telecommunication Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Muhammad Aamir was born on 3 July, 1976 in Karachi Pakistan. In 1998, he received BS Electronic Engineering degree and in 2002 his MS degree in Electronic Engineering (with specialization in Telecommunication). He accomplished his PhD in Electronic Engineering from Mehran University of Engineering & Technology. During his PhD studies, he accomplished his research work at the University of Malaga under Erasmus Mundus Scholarship. He has authored and co-authored around 50 research papers and book chapters published in various journals, books and conferences of international repute. For the past 12 Years he is a life member of Pakistan Engineering Council and professional member of IEEE. He was awarded with a grant by the Ministry of Education Spain to teach at the University of Malaga which he successfully availed in May 2012. He is also Member of two separate National Curriculum Revision Committees constituted by Higher Education Commission (HEC) for revision of Electronic Engineering Curriculum and Telecommunication Engineering Curriculum at the National Level. He was served as guest editor for special issue of Springer’s Journal with title “Wireless Personal Communication” which had published in November 2016. He is also HEC approved supervisor for Pakistani PhD candidates. He is currently employed as a Professor and Associate Dean in the Faculty of Electrical & Computer Engineering at SSUET. Moreover, he is also Editor-in-Chief of Sir Syed University Research Journal of Engineering & Technology which is HEC-Recognized Research Journal which is published bi-annually.

Najma Ismat, Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Najma Ismat is an alumni of SSUET. She has received her post graduate degree in 2018. Dr. Ismat did her undergraduate and graduate degrees in year 1998 and 2002 respectively. Her current research interests are mobility, reliability, connectivity and coverage issues in Underater Sensor Networks, Wireless Sensor Networks, and IoT.

Muhammad Imran Saleem, Department of Computer Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan

Engr. Muhammad Imran Saleem is currently working as an Assistant Professor in the Department of Computer Engineering at SSUET. He is associated with the university since January 2001. He is doing Ph.D. in Telecommunication Engineering from University of Malaga Spain. He did Masters (M.S) in Computer Engineering with specialization in Computer Network from SSUET. His thesis topic was Differentiated and Integrated services of IP packet. He did Bachelor (B.S) in Electronic Engineering from SSUET.


J. Gao, Y. Yang, P. Lin, DS. Park, ‘Computer Vision in Healthcare Applications’, J Healthc Eng. 2018;2018:5157020. 4 March 2018, doi:10.1155/2018/5157020.

C. H. Chen. ‘Series in Computer Vision’, Volume 2, Pages: 412, Computer Vision in Medical Imaging. January 2014.

A. Betancourt, P. Morerio, C.S. Regazzoni, and M. Rauterberg, ‘The Evolution of First Person Vision Methods: A Survey’, IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 5, pp. 744–760, May 2015.

M. Dimiccoli. ‘Computer Vision for Egocentric (First-Person) Vision’, In Computer Vision for Assistive Healthcare, Editors: Marco Leo, Giovanni Maria Farinella. Academic Press, 2018, Pages 183–210, ISBN 9780128134450,

S. Tian, W. Yang, J. Michael Le Grange, P. Wang, W. Huang, Z. Ye, ‘Smart healthcare: making medical care more intelligent’, Global Health Journal, Volume 3, Issue 3, 2019, Pages 62–65, ISSN 2414-6447,

G.J. Zelinsky, R.P.N. Rao, M.M.Hayhoe, and D.H.Ballard, ‘Eye Movements Reveal the Spatiotemporal Dynamics of Visual Search’, A journal of the association for Psychological Science, vol. 8, no. 6, pp. 448–453, 1997.

A.Toet, ‘Gaze directed displays as an enabling technology for attention aware systems’, Journal in Computers in Human Behavior, vol. 22, no. 4, pp. 615–647, July 2006.

H. Kang, A.E. Alexei, M. Herbert, and T. Kanade, ‘Image Matching in Large Scale Indoor Environment’, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Egocentric Vision, 2009.

L. Sun, U. Klank, and M. Beetz, ‘EYEWATCHME 3D Hand and object tracking for inside out activity analysis’, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops, pp. 9–16, 20–25 June 2009.

A. Furnari, G. M. Farinella, ‘What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention’, International Conference on Computer Vision, 2019.

M. A. A. Al-qaness, & F. Li, ‘WiGer: WiFi-based gesture recognition system’, ISPRS Inter- national Journal of Geo-Information, 5(6), 92. 2016.

S. Wang, & G. Zhou, ‘A review on radio based activity recognition’, Digital Communications and Networks, 1(1), 20–29. 2015.

C. H. Lu, & L.C. Fu, ‘Robust location-aware activity recognition using wireless sensor network in an attentive home’, IEEE Transactions on Automation Science and Engineering, 6(4), 598–609. 2009.

Q.Sun, J. Shen, H. Qiao, X. Huang, C. Chen, & F. Hu, ‘Static human detection and scenario recognition via wearable thermal sensing system’, Computers, 6(1), 3, 2017, doi: 10.3390/computers6010003.

J. M. Henderson, & A. Hollingworth, ‘High-level scene perception’, Annual Review of Psychology, 50, 243271, 1999.

K. Rayner, T. J. Smith, G. L. Malcolm, & J. M. Henderson, ‘Eye movements and visual encoding during scene perception’, Psychological Science, 20(1), 6–10, 2009.

Y. Wang, X. Jiang, R. Cao, & X. Wang, ‘Robust indoor human activity recognition using wireless signals’, Sensor, 15(7), 17195–17208, 2015.

B. Zhou, A. Lapedriza, J. Xiao , A. Torralba, & A. Oliva, ‘Learning deep features for scene recognition using places database’, In Z. Ghahramani M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in neural information processing systems (Vol. 27, pp. 487–495). Red Hook: Curran Associates Inc. 2014.

R.S. Segundo, J.M. Montero, J.M. Pimentel, and J.M. Pardo, ‘HMM Adaptation for Improving a Human Activity Recognition System’, Algorithms, Volume 9, No. 3, 2016.

E. Kim, S. Helal, and D. Cook, ‘Human Activity Recognition and Pattern Discovery’, IEEE Pervasive Computing, Volume 9, No. 1, pp. 48–53, January, 2010.

L.C. Jatoba, U. Grossmann, C. Kunze, J. Ottenbacher and W. Stork, ‘Context-Aware Mobile Health Monitoring: Evaluation of Different Pattern Recognition Methods for Classification of Physical Activity’, 30th IEEE Annual International Conference on Engineering in Medicine and Biology Society, 2008.

D. Anguita, A. Ghio, L. Oneto, X. Parra and J.L. Reyes-Ortiz, ‘Energy Efficient Smartphone-Based Activity Recognition Using Fixed-Point Arithmetic’, Journal of University Computer Science, 2013.

U. Maurer, A. Smailagic, D. Siewiorek, and M. Deisher, ‘Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions’, Proceedings of International Workshop on Wearable and Implantable Body Sensor Networks, 2006.

K. Shaharyar, J. Ahmad, and D. Kim, ‘Depth Images- Based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM’, Journal of Electrical Engineering & Technology, Volume 11, No. 3, pp. 1921–1926, 2016.

J. Yang, ‘Toward Physical Activity Diary: Motion Recognition Using Simple Acceleration Features with Mobile Phones’, Proceedings of 1st ACM International Workshop on Interactive Multimedia for Consumer Electronic, 2009.

J.R. Kwapisz, G.M. Weiss, and S.A. Moore, ‘Activity Recognition Using Cell Phone Accelerometers’, SIGKDD Explore News Letters, Volume 12, No. 2, pp. 74–82, March, 2011 (Last Visit: 15 June 2017). [Online]. Available: https://en:wikipedia.orgwiki/Deeplearning

S. Noor, and V. Uddin, ‘Using ANN for Multi-View Activity Recognition in Indoor Environment’, International Conference on Frontiers of Information Technology, pp. 258–263, December, 2016.

R. DamaeviIius, M. Vasiljevas, J. AlkeviIius, and M. Wofniak, ‘Human Activity Recognition in AAL Environments Using Random Projections’, Computational and Mathematical Methods in Medicine, pp. 17, 2016.

[Last seen on: 13-Apr-2021]

[Last seen on: 31-Apr-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Jan-2020] (visual)

[Last seen on: 31-Jan-2020]

S. Noor, H.M. Minhas, M.I. Saleem, V. Uddin and N. Ismat, ‘Inside-out Vision for Procedure Recognition in Dental Environment’, 2020 Global Conference on Wireless & Optical Technologies (GCWOT), Malaga, Spain 6–8 October 2020, pp. 1–8, doi: 10.1109/GCWOT49901.2020.9391594

D.E. Rumelhart, G.E. Hinton, R.J. Williams, ‘Learning representations by back-propagating errors’, Nat. Int. Wkly. J. Sci., 1986, 323, pp. 533–536.

S. Noor and V. Uddin, ‘Using ANN for Multi-view Activity Recognition in Indoor Environment,’ 14th International Conference on Frontiers of Information Technology (FIT-2016), 19–21 December 2016.

S. Noor and V. Uddin, ‘Using context from inside-out vision for improved activity recognition’, IET Computer Vision, vol. 12, no. 3, pp. 276–287, March 2018.

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 31-Mar-2021]

[Last seen on: 20-Feb-2020]

[Last Seen on: 27 June 2017]






6G Enabling Technologies – Innovation 6G