Aftab Khan | Research Associate

Aftab Khan

My research agenda is centred on Computational Behaviour Analysis, Machine Learning and Pattern Recognition. I’m particularly interested in human behaviour analysis through automated activity recognition. Such activity data is generally captured in an opportunistic manner utilising a variety of sensing modalities, most notably pervasive/ubiquitous sensors e.g., accelerometers and environmental sensors. The purpose of this technical agenda is to support and promote health and wellbeing of humans, largely by providing situated support. Such innovative assistive technology enables research that has a positive impact on people’s lives. The key to this is fundamental research in innovative machine learning techniques, especially focusing on sequential data analysis, grounded in a thorough understanding of the application domain.

I am currently involved in a project related to the automatic assessment of behaviour in children and young adults with certain disabilities in which they exhibit a range of extreme problem behaviours. This involves the development of a Human Activity Recognition system that uses appropriate sensing technologies in order to track these problem behaviours. I have also been involved in an EPSRC project (TEDDI) that explores pervasive sensing and activity recognition for behaviour analysis in buildings. In this project, we developed a hierarchical machine learning model for occupancy estimation for optimal resource management in the context of smart buildings.

Prior to this I was involved in an EPSRC project that addressed the challenging problem of autonomous cognition at the interface of vision and language. We developed various machine learning and pattern recognition techniques towards building an adaptive multi-level framework for autonomous bootstrapping of high and low level visual representations within a constrained, rule-governed environment such as sports.


Movement recognition technology as a method of assessing spontaneous general movements in high risk infants
Marcroft C, Khan A, Embleton N, Trenell M, Ploetz T, Frontiers in Neurology
Beyond Activity Recognition: Skill Assessment from Accelerometer Data
Khan A, Mellor S, Berlin E, Robin T, McNaney R, Olivier P, Ploetz T, UbiComp '15 ACM International Joint Conference on Pervasive and Ubiquitous Computing1155-1166
A Novel Markov Logic Rule Induction Strategy for Characterizing Sports Video Footage
Windridge D, Kittler J, de Campos T, Yan F, Christmas W, Khan A, IEEE MultiMedia24-35


Occupancy Monitoring using Environmental & Context Sensors and a Hierarchical Analysis Framework
Khan A, Nicholson J, Mellor S, Jackson D, Ladha K, Ladha C, Hand J, Clarke J, Olivier P, Ploetz T, BuildSys '14 Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings90-99
Multilevel Chinese Takeaway Process and Label-Based Processes for Rule Induction in the Context of Automated Sports Video Annotation
Khan A, Windridge D, Kittler J, IEEE Transactions on Cybernetics


A framework for automatic sports video annotation with anomaly detection and transfer learning
de Campos T, Khan A, Yan F, FarajiDavar N, Windridge D, Kittler J, Christmas W, Machine Learning and Cognitive Science (MLCOGS), collocated with EUCOGIII
Automatic correction of annotation boundaries in activity datasets by class separation maximization
Kirkham R, Khan A, Bhattacharya S, Hammerla N, Mellor S, Roggen D, Ploetz T, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 13): HASCA Workshop


Anomaly detection and knowledge transfer in automatic sports video annotation
Almajai I, Yan F, de Campos T, Khan A, Christmas W, Windridge D, Kittler J, Detection and Identification of Rare Audiovisual Cues109-117


Anomaly Detection and Knowledge Transfer in Automatic Sports Video Annotation
Almajai I, Yan F, deCampos T, Khan A, Christmas W, Windridge D, Kittler J, Proceedings of DIRAC Workshop, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010)
Ball event recognition using HMM for automatic tennis annotation
Almajai I, Kittler J, deCampos T, Christmas W, Yan F, Windridge D, Khan A, 17th IEEE International Conference on Image Processing (ICIP)1509-1512
Lattice-based Anomaly Rectification for Sport Video Annotation
Khan A, Windridge D, de Campos T, Kittler J, Christmas W, Pattern Recognition (ICPR), 20th International Conference on4372-4375

Associated Projects

  • Automatic Assessment of Problem Behaviour
    In certain disabilities, children and young adults can exhibit a range of extreme problem behaviours, including episodes of biting, kicking and self-injurious behaviour which might occur numerous times even in a single day. Existing treatments rely upo...
    August 30, 2011
  • Ambient Kitchen
    The Ambient Kitchen is a platform for research in pervasive computing that was installed at Culture Lab in 2007. It is a proof-of-concept context-aware computing environment, originally designed to demonstrate the potential for technology to support ol...
    August 30, 2011
  • TEDDI: Building Management and Energy Demand
    This research project involves the design and development of a sensing infrastructure that consists of networked physical (e.g. presence sensors, power consumption sensors) and virtual (e.g. calendar and room booking sensors, application usage sensors)...
    August 30, 2011
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