Experienced in the use of computational intelligence techniques for the analysis and modelling of large video datasets for finding patterns and extracting knowledge. My current research focuses on the interface between embedded computer vision, machine learning and neuroscience; mainly to understand and model the biological vision system on parallel architectures like Field Programmable Gate Arrays (FPGA). I have developed several neural network and image processing algorithms for FPGA and ARM processors. I have also published over 40 peer-reviewed academic papers including journals on various real-time image processing algorithms for security surveillance and biologically plausible neural network models.
Dr Appiah holds a BSc in Computer Science from KNUST, completed an MSc in Computer Science at University of Oxford and an MSc in Electronic Engineering at Royal Institute of Technology (KTH) in Sweden. He joined the Computer Vision Group at Durham University under the supervision of Professor Andrew Hunter and moved with him to University of Lincoln, where he completed his PhD in 2010. In 2002, he worked briefly with Handel-C and FPGA at the Photonics Research Group at Aston University.
Before joining Sheffield Hallam University in 2017, he worked at Nottingham Trent University as a Lecturer/Senior Lecturer from 2013, lead and contributed to various undergraduate modules (including Systems Technology, Internet Technology, Python Programming and Artificial Intelligence) and postgraduate modules (including Embedded Systems and Group Design Project). He also supervised a number of research degree students as the director of studies/second supervisor.
Kofi spent a year working as a Lecturer at the University of Science and Technology in Ghana, before joining the Embedded and Intelligent Systems (ESI) Research Group at University of Essex in Colchester as Senior Research Officer in December 2012. He also worked on part-time basis as a Development Engineer with Metrarc Ltd, Cambridge, before joining NTU in November 2013.
Dr Appiah is active in the embedded computer vision research area and acts as a reviewer for the following journals
- IEEE Transactions on Neural Networks and Learning Systems
- IEEE Transactions on Circuits and Systems for Video Technology
- IEEE Transaction on VLSI
- IEEE Transaction on Computers
- Journal of Computer Vision and Image Understanding – Elsevier
- Pattern Recognition Letters
- IET Science, Measurement and Technology
- PLOS ONE
Specialist areas of interest
Internet of Things
Computer Systems and Networks
Network Server Management and Configuration
Network and Systems Security
2012 - Worked as part of the Embedded and Intelligence Systems research group (University of Essex) on the SYSIASS project, aimed at the development of an Autonomous and Intelligent Healthcare System, funded by European Regional Development Funds.
2009 - Worked with Imperial College, Wifore, Tactical Systems Design and Phrisk on TOTALCARE, a TSB-funded project to deliver an end-to-end demonstrator of a novel digital health-care service and monitoring framework.
2007 – Worked with e2v technologies on Basic Robust Architecture for Integrated Neural Sensors (BRAINS), a TSB-funded project to deliver new, high speed method of integrated data processing by the correct combination of parallel and serial techniques.
2005 - Worked with SecuraCorp, Eastern Kentucky University’s Department of Justice and Safety Centre, on Algorithm-Based Object Recognition and Tracking (ABORAT), a US Department of Homeland Security-funded project for developing video analytics which detect video anomalies using an embedded Video Processing Unit.
Collaborators and sponsors
Sundance Multiprocessor Technology Limited
School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
Computational Neuroscience and Cognitive Robotics Laboratory, Nottingham Trent University, Nottingham
Embedded and Intelligent Systems (EIS) Research Laboratory, University of Essex, Colchester
Laboratory of Vision Engineering, University of Lincoln, Lincoln
Ortega-Anderez, D., Lotfi, A., Langensiepen, C., & Appiah, K. (2019). A multi-level refinement approach towards the classification of quotidian activities using accelerometer data. Journal of Ambient Intelligence and Humanized Computing, 10 (11), 4319-4330. http://doi.org/10.1007/s12652-018-1110-y
Lotfi, A., Albawendi, S., Powell, H., Appiah, K., & Langensiepen, C. (2018). Supporting Independent Living for Older Adults: Employing a Visual Based Fall Detection Through Analysing the Motion and Shape of the Human Body. IEEE Access, 6, 70272-70282. http://doi.org/10.1109/ACCESS.2018.2881237
Atanbori, J., Duan, W., Shaw, E., Appiah, K., & Dickinson, P. (2018). Classification of bird species from video using appearance and motion features. Ecological Informatics, 48 (2018), 12-23. http://doi.org/10.1016/j.ecoinf.2018.07.005
Costalago Meruelo, A., Machado, P., Appiah, K., Mujika, A., Leškovský, P., Alvarez, R., ... McGinnity, T.M. (2018). Emulation of chemical stimulus triggered head movement in the C. elegans nematode. Neurocomputing. http://doi.org/10.1016/j.neucom.2018.02.024
Robinson, J., Lee, K., Appiah, K., & Yousef, Y. (2017). Energy-Aware systems for improving the well-being of older people by reducing their energy consumption. International Journal on Advances in Life Sciences, 9 (3&4), 163-175. http://www.iariajournals.org/life_sciences/lifsci_v9_n34_2017_paged.pdf
Mustafa, M., Allen, T., & Appiah, K. (2017). A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition. Neural Computing and Applications. http://doi.org/10.1007/s00521-017-3028-2
Atanbori, J., Duan, W., Murray, J., Appiah, K., & Dickinson, P. (2016). Automatic classification of flying bird species using computer vision techniques. Pattern Recognition Letters, 81, 53-62. http://doi.org/10.1016/j.patrec.2015.08.015
Zhai, X., Appiah, K., Ehsan, S., Howells, G., Hu, H., Gu, D., & McDonald-Maier, K. (2015). Exploring ICMetrics to detect abnormal program behaviour on embedded devices. Journal of Systems Architecture, 61 (10), 567-575. http://doi.org/10.1016/j.sysarc.2015.07.007
Zhai, X., Appiah, K., Ehsan, S., Howells, G., Hu, H., Gu, D., & McDonald-Maier, K.D. (2015). A method for detecting abnormal program behavior on embedded devices. IEEE Transactions on Information Forensics and Security, 10 (8), 1692-1704. http://doi.org/10.1109/TIFS.2015.2422674
Munro, J., Appiah, K., & Dickinson, P. (2014). Investigating informative performance metrics for a multicore game world server. Entertainment Computing, 5 (1), 1-17. http://doi.org/10.1016/j.entcom.2013.10.001
Appiah, K., Hunter, A., Dickinson, P., & Meng, H. (2012). Implementation and applications of Tri-State self-organizing maps on FPGA. IEEE Transactions on Circuits and Systems for Video Technology, 22 (8), 1150-1160. http://doi.org/10.1109/TCSVT.2012.2197077
Dickinson, P., Hunter, A., & Appiah, K. (2009). A spatially distributed model for foreground segmentation. Image and Vision Computing, 27 (9), 1326-1335. http://doi.org/10.1016/j.imavis.2008.12.001
Appiah, K., Hunter, A., & Kluge, T. (2005). GW4: a real-time background subtraction and maintenance algorithm for FPGA implementation. WSEAS Transactions on Systems, 4 (10), 1741-1751.
Bouchut, Q., Appiah, K., Lotfi, A., & Dickinson, P. (2019). Ensemble One-vs-One SVM Classifier for Smartphone Accelerometer Activity Recognition. In 20th IEEE International Conferences on High Performance Computing and Communications (HPCC), Exeter, 28 January 2018 - 6 January 2018. IEEE: http://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00185
Albawendi, S., Lotfi, A., Powell, H., & Appiah, K. (2018). Video based fall detection using features of motion, shape and histogram. Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference on - PETRA '18, 529-536. http://doi.org/10.1145/3197768.3201539
Bhowmik, D., & Appiah, K. (2018). Embedded vision systems: A review of the literature. In 14th International Symposium on Applied Reconfigurable Computing (ARC), Santorini, Greece, 2 May 2018 - 4 May 2018. http://arc2018.esda-lab.cied.teiwest.gr/
Elbayoudi, A., Lotfi, A., Langensiepen, C., & Appiah, K. (2017). Trend analysis techniques in forecasting human behaviour evolution. ACM International Conference Proceeding Series, Part F128530, 293-299. http://doi.org/10.1145/3056540.3076198
Anderez, D.O., Appiah, K., Lotfi, A., & Langesiepen, C. (2017). A hierarchical approach towards activity recognition. ACM International Conference Proceeding Series, Part F128530, 269-274. http://doi.org/10.1145/3056540.3076194
Appiah, K., Machado, P., Meruelo, A.C., & McGinnity, T.M. (2016). C. elegans behavioural response germane to Hardware modelling. Proceedings of the International Joint Conference on Neural Networks, 2016-October, 4743-4750. http://doi.org/10.1109/IJCNN.2016.7727823
Meruelo, A.C., Machado, P., Appiah, K., & McGinnity, T.M. (2016). Challenges in clustering C. elegans neurons using computational approaches. Proceedings of the International Joint Conference on Neural Networks, 2016-October, 4775-4781. http://doi.org/10.1109/IJCNN.2016.7727827
Machado, P., Costalago Meruelo, A., Lama, N., Adama, D., Appiah, K., McGinnity, T.M., ... Blau, A. (2016). Si elegans: Evaluation of an innovative optical synaptic connectivity method for C. elegans Phototaxis using FPGAs. Proceedings of the International Joint Conference on Neural Networks, 2016-October, 185-191. http://doi.org/10.1109/IJCNN.2016.7727197
Albawendi, S., Appiah, K., Powell, H., & Lotfi, A. (2016). Video based fall detection with enhanced motion history images. ACM International Conference Proceeding Series, 29-June-2016. http://doi.org/10.1145/2910674.2935832
Elbayoudi, A., Lotfi, A., Langensiepen, C., & Appiah, K. (2016). Determining behavioural trends in an ambient intelligence environment. ACM International Conference Proceeding Series, 29-June-2016. http://doi.org/10.1145/2910674.2935834
Albawendi, S., Appiah, K., Powell, H., & Lotf, A. (2016). Overview of Behavioural Understanding System with Filtered Vision Sensor. Proceedings - 2015 International Conference on Interactive Technologies and Games, ITAG 2015, 90-95. http://doi.org/10.1109/iTAG.2015.21
Machado, P., Appiah, K., McGinnity, T.M., & Wade, J. (2015). Si elegans: Hardware architecture and communications protocol. Proceedings of the International Joint Conference on Neural Networks, 2015-September. http://doi.org/10.1109/IJCNN.2015.7280771
Elbayoudi, A., Langensiepen, C., Lotfi, A., & Appiah, K. (2015). Modelling and simulation of activities of daily living representing an older Adult's behaviour. 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2015 - Proceedings. http://doi.org/10.1145/2769493.2769544
Acampora, G., Appiah, K., Hunter, A., & Vitiello, A. (2015). Interoperable services based on activity monitoring in Ambient Assisted Living environments. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - IA 2014: 2014 IEEE Symposium on Intelligent Agents, Proceedings, 81-88. http://doi.org/10.1109/IA.2014.7009462
Costalago Meruelo, A., Machado, P., Appiah, K., & McGinnity, T.M. (2015). Si elegans: A computational model of C. Elegans muscle response to light. NEUROTECHNIX 2015 - Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics, 121-126. http://doi.org/10.5220/0005712201210126
Zhai, X., Appiah, K., Ehsan, S., Cheung, W.M., Howells, G., Hu, H., ... McDonald-Maier, K. (2014). Detecting compromised programs for embedded system applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8350 LNCS, 221-232. http://doi.org/10.1007/978-3-319-04891-8_19
Appiah, K., Hunter, A., Lotfi, A., Waltham, C., & Dickinson, P. (2014). Human behavioural analysis with self-organizing map for ambient assisted living. IEEE International Conference on Fuzzy Systems, 2430-2437. http://doi.org/10.1109/FUZZ-IEEE.2014.6891833
Appiah, K., Hunter, A., & Waltham, C. (2011). Low-power and efficient ambient assistive care system for elders. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 97-102. http://doi.org/10.1109/CVPRW.2011.5981824
Meng, H., Appiah, K., Hunter, A., & Dickinson, P. (2011). FPGA implementation of naive bayes classifier for visual object recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 123-128. http://doi.org/10.1109/CVPRW.2011.5981831
Appiah, K., Hunter, A., Dickinson, P., & Meng, H. (2010). Binary object recognition system on FPGA with bSOM. Proceedings - IEEE International SOC Conference, SOCC 2010, 254-259. http://doi.org/10.1109/SOCC.2010.5784755
Dickinson, P., Hunter, A., & Appiah, K. (2010). Segmenting video foreground using a multi-class MRF. Proceedings - International Conference on Pattern Recognition, 1848-1851. http://doi.org/10.1109/ICPR.2010.456
Meng, H., Appiah, K., Yue, S., Hunter, A., Hobden, M., Priestley, N., ... Cy, P. (2010). A modified model for the Lobula Giant Movement Detector and its FPGA implementation. Computer Vision and Image Understanding, 114 (11), 1238-1247. http://doi.org/10.1016/j.cviu.2010.03.017
Appiah, K., Hunter, A., Dickinson, P., & Meng, H. (2010). Accelerated hardware video object segmentation: From foreground detection to connected components labelling. Computer Vision and Image Understanding, 114 (11), 1282-1291. http://doi.org/10.1016/j.cviu.2010.03.021
Appiah, K., Meng, H., Hunter, A., & Dickinson, P. (2010). Binary histogram based split/merge object detection using FPGAs. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, 45-52. http://doi.org/10.1109/CVPRW.2010.5543760
Appiah, K., Hunter, A., Owens, J., Aiken, P., & Lewis, K. (2009). Autonomous real-time surveillance system with distributed IP cameras. 2009 3rd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009. http://doi.org/10.1109/ICDSC.2009.5289387
Meng, H., Appiah, K., Hunter, A., Yue, S., Hobden, M., Priestley, N., ... Pettit, C. (2009). A modified Sparse Distributed Memory model for extracting clean patterns from noisy inputs. Proceedings of the International Joint Conference on Neural Networks, 2084-2089. http://doi.org/10.1109/IJCNN.2009.5178873
Appiah, K., Hunter, A., Meng, H., Yue, S., Hobden, M., Priestley, N., ... Pettit, C. (2009). A binary Self-Organizing Map and its FPGA implementation. Proceedings of the International Joint Conference on Neural Networks, 164-171. http://doi.org/10.1109/IJCNN.2009.5179001
Meng, H., Yue, S., Hunter, A., Appiah, K., Hobden, M., Priestley, N., ... Pettit, C. (2009). A modified neural network model for Lobula Giant Movement Detector with additional depth movement feature. Proceedings of the International Joint Conference on Neural Networks, 2078-2083. http://doi.org/10.1109/IJCNN.2009.5179023
Appiah, K., Hunter, A., Kluge, T., Aiken, P., & Dickinson, P. (2009). FPGA-based anomalous trajectory detection using SOFM. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5453, 243-254. http://doi.org/10.1007/978-3-642-00641-8_24
Appiah, K., Hunter, A., Dickinson, P., & Owens, J. (2008). A run-length based connected component algorithm for FPGA implementation. Proceedings of the 2008 International Conference on Field-Programmable Technology, ICFPT 2008, 177-184. http://doi.org/10.1109/FPT.2008.4762381
Appiah, K., Dickinson, P., & Hunter, A. (2006). An intelligent reconfigurable infant monitoring system. Proceedings of the 6th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2006, 471-476.
Dickinson, P., Appiah, K., Hunter, A., & Ormston, S. (2005). An FPGA-based infant monitoring system. Proceedings - 2005 IEEE International Conference on Field Programmable Technology, 2005, 315-316. http://doi.org/10.1109/FPT.2005.1568578
Appiah, K., & Hunter, A. (2005). A single-chip FPGA implementation of real-time adaptive background model. Proceedings - 2005 IEEE International Conference on Field Programmable Technology, 2005, 95-102. http://doi.org/10.1109/FPT.2005.1568531
External Examiner – University of Derby (BEng Computer Network Engineering)