Dr Feng Chen

Job: Senior Lecturer

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Cyber Technology Institute (CTI) (Software Technology Research Laboratory STRL))

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 257 7860

E: fengchen@dmu.ac.uk

W: http://www.dmu.ac.uk/cti


Personal profile

I was awarded my BSc, Mphil and PhD in computer science at Nankai University, Dalian University of Technology and De Montfort University in 1991, 1994 and 2007. My PhD thesis focuses on modernising legacy systems through model construction and transformation. A unified approach is proposed in the context of Model Driven Architecture (MDA) to extract different level of models from legacy source code. 

I have been an active researcher in the area of software engineering and knowledge engineering, focusing on its emerging themes including Service Oriented Architecture, Model Driven Architecture, Cloud/Edge Computing, Mobile Computing, Internet of Things and Cyber Physical System, with the long term aim of building a coherent set of conceptual frameworks, methods and tool support for the development of Smart Environments and Ambient Assisted Living Systems. 

I have a strong background in a combination of academic research and industrial application. I have gained invaluable understanding of system software, distributed systems, real-time systems, critical systems and security systems through many projects. As research outputs, I have published 74 research papers in refereed conferences and Journals.

Research group affiliations

Cyber Technology Institute

Publications and outputs 

  • A Dynamic Grid-Based Algorithm for Taxi Ridesharing in Multiple Road Condition
    A Dynamic Grid-Based Algorithm for Taxi Ridesharing in Multiple Road Condition Wang, Yuxin; Wu, Bing; Xv, Tongkun; Shen, Yanming; Chen, Feng As the way of easing urban traffic congestion, taxi ridesharing can effectively protect the environment and solve the difficulty of passengers taking taxis when taxi demand is high. In this paper, we formally define the dynamic ride-sharing problem and propose a taxi candidates-reduction ride-sharing scheduling algorithm based on dynamic grid. Regarding the congestion situation of the multiple road condition, the concept of speed decay zone was purposed to simulate this area. To solve the problem of low satisfaction in the congested situation, we devise a dynamic grid division strategy that reduces the grid size of the hotspot area to satisfy the specific needs of passengers in rush hour, and efficiently screen candidate taxis by dynamic grid index. We perform the experiments using the request dataset generated by the taxi request simulator of Beijing Chaoyang district. The performance shows that our approach reduce 35.5% computation without losing average satisfaction compared with existing ridesharing algorithm.
  • Fine-Grained Sleep-Wake Behaviour Analysis
    Fine-Grained Sleep-Wake Behaviour Analysis Fallmann, Sarah; Chen, Liming; Chen, Feng Sleep stages are traditionally assessed by experts from polysomnography measurements following specific guidelines. Sleep stage behaviour is subsequently used to detect anomalies and diagnose diseases in a laboratory setting. Recently, with the development of Internet of Things, there is a trend to use everyday technologies for sleep behaviour analysis at home, having the potential to eliminate expensive in-hospital monitoring. We propose a fine-grained sleep-wake behaviour analysis approach, which takes into consideration the influences of various factors, such as gender, health status and race. In addition, we investigate the combination of multiple data sources, in particular, actigraphy and heart rate variability, for enhancing model accuracy. Initial results show the proposed approach is recognising sleep and wake stages accurately and is providing a flexible recognition approach towards personalised sleep-based health monitoring.
  • Fuzzy-Based Fine-Grained Human Activity Recognition within Smart Environments
    Fuzzy-Based Fine-Grained Human Activity Recognition within Smart Environments Triboan, Darpan; Chen, Liming; Chen, Feng With the increasing ageing population, Smart Home (SH) has been under vigorous investigation to enable Ambient Assisted Living (AAL) and foster independent living. Human Activity Recognition (HAR) is the backbone of AAL systems in order to detect Activities of Daily Living (ADL) and provide timely, context-aware assistance. Existing SH based AAL systems primarily focus on coarse-grained activity recognition (AR) and assume successful usage of everyday objects using binary sensors. Limited attention is given to fined-grained AR by verifying the intended object interactions with evidence from multiple heterogeneous sensor data. This paper proposes a fine-grained AR approach which fuses multimodal data from single objects and handles the imprecise nature of non-binary sensor measurements. This approach leverages the fuzzy ontology to model fine-grained actions with imprecise membership states of the sensors in relation to object and fuzzyDL reasoning tool to classify action completion. In addition, a microservice architecture is proposed with a non-intrusive heterogeneous ambient and embedded object based sensing method. The sensing method integrates both off-the-shelf and bespoke devices to collect fine-grained object level interactions. A case study is provided to illustrate the use of the fine-grained AR approach to recognize kitchen-based activities. The Publisher's final version can be found by following the DOI link.
  • Enhanced Multi-Source Data Analysis for Personalized Sleep-Wake Pattern Recognition and Sleep Parameter Extraction
    Enhanced Multi-Source Data Analysis for Personalized Sleep-Wake Pattern Recognition and Sleep Parameter Extraction Fallmann, S.; Chen, Liming; Chen, Feng Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the growing demand for personalized healthcare and the prevalence of the Internet of Things, there is a trend to use everyday technologies for sleep behavior analysis at home, having the potential to eliminate expensive in-hospital monitoring. In this paper, we conceived a multi-source data mining approach to personalized sleep-wake pattern recog-nition which uses physiological data and personal information to facilitate fine-grained detection. Physiological data includes actigraphy and heart rate variability and personal data makes use of gender, health status and race infor-mation which are known influence factors. Moreover, we developed a personal-ized sleep parameter extraction technique fused with the sleep-wake approach, achieving personalized instead of static thresholds for decision-making. Results show that the proposed approach improves the accuracy of sleep and wake stage recognition, therefore, offers a new solution for personalized sleep-based health monitoring. The file attached to this record is the author's final peer reviewed version.
  • An Assistive Augmented Reality-based Smartglasses Solution for Individuals with Autism Spectrum Disorder
    An Assistive Augmented Reality-based Smartglasses Solution for Individuals with Autism Spectrum Disorder Machado, Eduardo; Carrillo, Ivan; Saldana, David; Chen, Feng; Chen, Liming Acquiring daily living skills can be difficult for children and adults with autism spectrum disorders (ASD). Increasing evidences indicate that classic occupational interventions approaches such as Discrete Trial Teaching(DTT) results to be boring and frustrating to individuals with ASD. As consequence, they spend most of the time off task and face difficulties to sustaining their selective attention. Moreover, in-person interventions are both costly and difficult to access. Evidence-based research shows that the use of augmented reality(AR) strengthens and attracts the attention of individuals with ASD, enhancing their engagement and user's task performance. However, despite of the benefits, the use of AR as an assistive technology by this segment of population, still presents low rates of adaption. Platforms such as smartphone and tablet, used to run AR technologies provokes an head-down posture that decrease the user's awareness to the physical environment putting themselves in risk of injury. Moreover, they are forced to have their hands occupied and most of the existent applications are lacks to be personalized to different user's needs. This paper introduces a conceptual framework for developing real-time personalized assistive AR-based smartglasses system. The solution aims to solve the issues related to in-person occupational interventions i.e constant need for professional supervision during intervention as well as limited intervention duration and frequency. In addition, we also target issues related to classic AR-based platforms i.e head-down postures and task-specific design. Ingenieria y Soluciones Informatica, Sevilla
  • A Home-Based IoT-Enabled Framework for Sleep Behaviour Assessment
    A Home-Based IoT-Enabled Framework for Sleep Behaviour Assessment Fallmann, Sarah; Chen, Liming; Chen, Feng Sleep has an impact on a person's life including their health and wellbeing, thus assessing sleep behaviour is of high importance to gain insight into people's general health status. In general, current sleep behaviour assessment is restricted to a controlled scenario within a hospital environment limited by the time of monitoring and to specific factors. As healthcare is shifting from reactive to preventive and predictive care with the support of digital health and IoT technology, there is a growing demand to make sleep assessment possible at home. In this paper, we propose a sleep behaviour assessment framework considering different facets of sleep such as sleep quality, regularity, circadian rhythm, environmental conditions and sleep hygiene. Hence, we describe methodologies and techniques which can help realise home-based sleep assessment. A salient feature of the framework is that it takes into account personal preferences and influential factors as well as doctor's recommendations and clinical history, thus, allowing personalised medical and behavioural assessment. In addition, the proposed framework supports a modular service-oriented design adaptable to both doctor and user needs and availability of underpinning technologies.
  • ICA-Based EEG Feature Analysis and Classification of Learning Styles
    ICA-Based EEG Feature Analysis and Classification of Learning Styles Alhasan, Khawla; Aliyu, Suleiman; Chen, Liming; Chen, Feng A thorough investigation of the electroencephalograph (EEG) information may support an enriched awareness of the mechanism of understanding different learning styles patterns. Wavelet analysis is a powerful technique that uniquely permits the decomposition of complex information of trends, discontinuities, a repeated pattern. The purpose of such methods is to be able to assign simple segments at diverse locations and scales, to be remodelled afterward effectively. In this paper, we attempt to classify individual cognitive learning styles using artificial neural networks and unsupervised learning. First, we apply Independent component analysis (ICA) to extract relevant features (artefacts removal) of the EEG records. We analyse the ICA-based EEG channels data using inter-quartiles to show the degree of dispersion and skewness. Next, self-organising maps (SOM) are then created to characterise different cognitive learning styles from selected ICA-based channel data.
  • Smart Assisted Living: Toward An Open Smart-Home Infrastructure
    Smart Assisted Living: Toward An Open Smart-Home Infrastructure Chen, Feng; García-Betances, Rebeca I; Chen, Liming; Cabrera-Umpiérrez, María Fernanda; Nugent, Chris Smart Homes (SH) offer a promising approach to assisted living for the ageing population. Yet the main obstacle to the rapid development and deployment of Smart Home (SH) solutions essentially arises from the nature of the SH field, which is multidisciplinary and involves diverse applications and various stakeholders. Accordingly, an alternative to a one-size-fits-all approach is needed in order to advance the state of the art towards an open SH infrastructure. This book makes a valuable and critical contribution to smart assisted living research through the development of new effective, integrated, and interoperable SH solutions. It focuses on four underlying aspects: (1) Sensing and Monitoring Technologies; (2) Context Interference and Behaviour Analysis; (3) Personalisation and Adaptive Interaction, and (4) Open Smart Home and Service Infrastructures, demonstrating how fundamental theories, models and algorithms can be exploited to solve real-world problems. This comprehensive and timely book offers a unique and essential reference guide for policymakers, funding bodies, researchers, technology developers and managers, end users, carers, clinicians, healthcare service providers, educators and students, helping them adopt and implement smart assisted living systems.
  • Multi-granular Activity Recognition within a Multiple Occupancy Environment
    Multi-granular Activity Recognition within a Multiple Occupancy Environment Triboan, Darpan; Chen, Liming; Chen, Feng; Wang, Zumin
  • An Experimental Study of Learning Behaviour in an ELearning Environment
    An Experimental Study of Learning Behaviour in an ELearning Environment Alhasan, K.; Chen, Liming; Chen, Feng To reach an adaptive eLearning course, it is crucial to control and monitor the student behaviour dynamically to implicitly diagnose the student learning style. Eye tracing can serve that purpose by investigate the gaze data behaviour to the learning content. In this study, we conduct an eye tracking experiment to analyse the student pattern of behaviour to output his learning style as an aspect of personalisation in an eLearning course. We use the electroencephalography EEG Epoc that reflects users emotions to improve our result with more accurate data. Our objective is to test the hypothesis whether the verbal and visual learning Styles reflect actual preferences according to Felder and Silverman Learning Style Model in an eLearning environment. Another objective is to use the outcome presented in this experiment as the starting point for further exhaustive experiments. In this paper, we present the actual state of our experiment, conclusions, and plans for future development.

Click here to view a full listing of Feng Chen's publications and outputs.

Key research outputs


Research interests/expertise

Software Engineering

Software Re-engineering

Distributed Computing

Software Evolution

Software Architecture

Program/Model Transformation

Domain Specific Modelling

Knowledge Engineering

Areas of teaching

Software Engineering 


2015 to 2016, PGCertHE, De Montfort University, UK

Apr. 2003 to Jul. 2007, Doctor of Philosophy in Software Engineering, Software Technology Research Laboratory (STRL), De Montfort University, UK

Sep. 1991 to Jul. 1994, Master Degree in Computer Science, Computer Science and Engineering Department, Dalian University of Technology, China

Sep. 1987 to Jul. 1991, Bachelor Degree in Computer Science, Computer Science Department, NanKai University, China

Courses taught

Postgraduate Course:

Advanced Requirements Engineering and Software Architecture/ Requirement Analysis and Cloud-based Systems

Software Project Management and Testing/ Software Quality Assurance and Testing

Undergraduate Course:

Server and Storage Solutions,

Lab of Java Programming/ OO Software Design 

Membership of professional associations and societies

IEEE Membership


SOCRADES EU Project, EU Research project, 03/2009-03/2010. The developed engineering tool was used by Ford UK Ltd.

SPADzeroTM Sign Communication Control System for Dailys UK Ltd., 06/2007-12/2007. SPADzeroä is a stand-alone executable software product for data distribution to remote display.

FermaT Integrated Platform (FIPfor Software Maintenance Ltd. (SML), UK, 07/2005–06/2007. The software product has been used for program analysis, demonstration and training by SML.

SIFT Project for Fuji Film Company, 04/2004-10/2004. The developed image processing software package was tested and delivered to Fuji Film Company.

PLC Simulation System for 2000t/d Cement Product Line, 06/2002-12/2002. The system was used by Beijing Building Materials Industry School for teaching and training.

MIS for Environmental Protection Administration, 08/2001-12/2002. The system was installed in Dalian and Dandong Bureau of Environmental Protection Administration. The software won the Second Class of Dalian Information System Application Prize, China.

Multi-interface Virtual Operating System Based on Windows (VRTOS), 02/2001-02/2002. VRTOS was used for developing W-CDMA and 3G protocol stack in Dalian HuaChang Electronics & Communication Technology Co. Ltd., China.

IC Card Tax Declaration System, 06/1998-12/1999. The system was filed as a practical new style patent of China, titled “General Smart Card Declaration Terminal” (ZL 96 2 39055.0).

Counselling System for Entry-Exit Inspection and Quarantine, 08/1995-02/1996. The Software was awarded Golden Snake Prize in 1996. The software was used by Dalian Entry-Exit Inspection and Quarantine for more than 10 years.

Phosphate Elimination Control System for Prominent GMBH, Germany, Research project, Part of BMFT Grant (02WA9029/1). 01/1995-07/1995. The neuro-fuzzy learning algorithm based controller was installed in Heldelberg sewage plant, Germany to improve the effort of biological phosphate elimination and reduces 30% usage of the chemical dosage.

Conference attendance

Local Chair, IEEE Smart World Congress (SWC2019)

General Chair2nd Workshop on advanced Technologies for Smarter Assisted Living solutions: Towards an open Smart Home infrastructure (SmarterAAL'19)

Program Co-Chair, IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2018), IEEE Outstanding Leadership Award

Program Chair, IEEE International Workshop on Software Cybernetics 2017 (IWSC2017)

Program Chair, IEEE International Workshop on Software Cybernetics 2016 (IWSC2016)

Technical program committee member, more than 10 international conferences

Key research outputs

(IF=4.639) Triboan, D. Chen, L., Chen, F., Wang, Z. (2019) A semantics-based approach to sensor data segmentation in real-time Activity Recognition. Future Generation Computer Systems. 93, 224-236

(IF=3.557) Ding F., Liu Y., Chen X., Chen F. (2018). Rising Star Evaluation in Heterogeneous Social Network. IEEE Access. 6, 29436-29443

(IF=3.241) Zhang, Y., Guo, H., Chen, F., Yang, H. (2017) Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation. Neurocomputing. 249, 1-18

(IF=2.278) Yang, H. Chen, F., Aliyu, Y. (2017) Modern software cybernetics: New trends. Journal of Systems and Software, 124, 169-186

(IF=1.924) Triboan, D. Chen, L., Chen, F., Wang, Z. (2017) Semantic segmentation of real-time sensor data stream for complex activity recognition. Personal and Ubiquitous Computing. 21(3), 411-425

Cope, J., Siewe, F., Chen, F., Maglaras, L., Janicke, H. (2017) On data leakage from non-production systems, Information & Computer Security, 25(4), 454-474, Emerald

Cope, J., Maglaras, L., Siewe, F., Chen, F., Janicke (2017) A Framework for Minimising Data Leakage from Non-Production Systems. Book chapter, In book: Bigdata Analytics: Tools and Technology for Effective Planning. Chapman and Hall/CRC.

Psychoula, I., Chen, L., Chen, F. (2017) “Privacy modelling and management for assisted living within smart homes”, e-Health Networking, Applications and Services (Healthcom), IEEE. (Best Paper)

(IF=1.063) Triboan, D. Chen, L., Chen, F., Wang, Z. (2016) Towards a Service-oriented Architecture for a Mobile Assistive System with Real-time Environmental Sensing. Tsinghua Science & Technology. 21(6), 581-597

Chen, F., Al-Bayatti, A., Siewe, F. (2016) Context-aware Cloud Computing for Personal Learning Environment. Book chapter, In book: Cloud-Based STEM Education for Improved Learning Outcomes, IGI Global.

(IF=0.613) Chen, F. et al. (2014) A Precondition-based Approach to Workflow Oriented Software Re-engineering. Computer Science and Information Systems, 11(1), 1-27

AlHakami, H., Chen, F., Janicke, H. (2014) An Extended Stable Marriage Problem Algorithm for Clone Detection. International Journal of Software Engineering & Applications (IJSEA). 5(4), 103-122

AlHakami, H., Chen, F., Janicke, H. (2014) SMP-Based Approach for Intelligent Service Interaction, International Journal of Advanced Computer Science and Applications (IJACSA). Special Issue on Extended Papers from Science and Information Conference 2014, 124-131

Current research students

Alhasan, Khawla  Second Supervisor

Aliyu, Suleiman  First Supervisor

Da Silva Machado, Eduardo  Second Supervisor

Fallmann, Sarah  Second Supervisor

Ma, Fengbao  First Supervisor

Psychoula, Ismini  Second Supervisor 

Singh, Lakhvir  Second Supervisor 

Triboan, Darpan  Second Supervisor

Yagnik, Tarjana  First Supervisor

Zheng, Yumei  Second Supervisor

Externally funded research grants information

"Acrossing - Advanced TeChnologies and PlatfoRm fOr Smarter ASsisted LivING" (2016-2019, € 3.8M, DMU € 810K). Co-I, Coordinator of EU Horizon 2020 project.

"Affordable Virtual Engineering Tools" (2014-2015, £60K). PI, TSB KTP project.

Internally funded research project information

“A Feasibility Study of the Use of Smart Textiles to Support Assistive Living” (2017-2018, £2K). PI, internal RIF project, De Montfort University, UK

Ÿ“Marketing of Professional Training Courses within Cyber Technologies” (3 months, 2016). Supervisor, Internship project, De Montfort University, UK

Published patents

"General Smart Card Declaration Terminal" (1998). A Practical New Style Patent of China (ZL 96 2 39055.0, First Order in Four Inventers).

Professional esteem indicators


IEEE Transactions on Reliability; Journal of Systems and Software (Elsevier);

Personal and Ubiquitous Computing Journal (Springer);

Journal of Software: Evolution and Process (Wiley);

Arabian Journal for Science and Engineering;

Journal of Ambient Intelligence and Humanized Computing (Springer);

Journal of Computational Methods in Sciences and Engineering (IOS). 


PhD external examiner: Masoud Khamallag, University of Bradford, Towards an Improved Framework of E-Government Implementation in Chaotic Environment; Proposed Social Collaboration Model. 2018

PhD external examiner: Richard Greenwell, Edinburgh Napier University, An Approach to the Semantic Intelligence Cloud. 2017

PhD internal examiner at De Montfort University: more than 10 students.


External academic panel member for Review of Course Provision: Postgraduate Computer Science and Technology, University of Bedfordshire, July 2016.

External academic panel member for a validation of BSc (Hons) Software Engineering and Network Engineering programmes at University Campus Suffolk in Ipswich, April 2015.


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