Dr Michael Gillham
Michael Gillham obtained his first degree BEng (Hons) Engineering from the Open University and an MSc (Hons) Electronic engineering from the University of Surrey, obtaining his PhD in Electronic engineering from the University of Kent in 2015. He has worked since 2014 as a Research Associate in the Engineering and Digital Arts at the University of Kent.
My research interests lie in mobile robotics; remote operated or semi-autonomous for exploration, human operator controlled machines, and assistive powered wheelchair technology.
I have been involved with the following projects:
- Empowering Disabled People through Ethics in Care and Technology (EDECT)
- SYStème Intelligent et Autonome d’aide aux Soins de Santé (SYSIASS)
- Empowerment of Disabled people through the User Coproduction of Assistive Technology (EDUCAT)
- A synergetic adaptive non-intrusive-navigation assistance system for empowering the disabled, elderly and infirm powered wheelchair user (SANAS)
- Multimodal information fusion for Safe Assisted drone Navigation (MASAN)
Gillham, M., Pepper, M., Kelly, S. and Howells, G. (2017). Stakeholder views addressing the development and uptake of powered wheelchair assistive technology. Disability and Rehabilitation: Assistive Technology [Online]. Available at: http://dx.doi.org/10.1080/17483107.2017.1416186.Purpose: The objective of this research is to identify stakeholder views with regard to the development of effective powered wheelchair assistive technologies more suited to the user and carer needs, whilst also meeting the requirements for other stakeholders, such that developers can be better guided towards producing solutions which have a better chance of getting to the market place and hence to the end user.
Method: A questionnaire was designed to collect the views of all stakeholders and circulated to a statistically representative number of them. The question rating data was then checked for correlation between groups, and within groups, to establish validity.
Results: The 74 stakeholders across the eight classes who responded had a good correlation between each other, with a cross class ‘Pearson’s correlation’ ranging between 0.7 and 0.95, and the ‘Fleiss’s Kappa reliability of agreement’ within each class ranging between 0.07 and 0.36.
Conclusions: This research has identified that all stakeholders should be involved in the development of the technology and that some may benefit in ‘role-reversal’ to help understand user problems and stakeholder concerns more clearly. Cost was a significant barrier to the uptake of appropriate technology, and training of users and carers was a major issue. Furthermore development should not increase user isolation and the impact on the user must be monitored for ‘quality of life’. Technical support and training should be given to the user and their carers and equipment must be adaptive to meet the changing needs of the user.
Gillham, M., Pepper, M., Kelly, S. and Howells, G. (2017). Feature determination from powered wheelchair user joystick input characteristics for adapting driving assistance. Wellcome Open Research [Online] 2:93. Available at: https://doi.org/10.12688/wellcomeopenres.12280.2.Background: Many powered wheelchair users find their medical condition and
their ability to drive the wheelchair will change over time. In order to maintain
their independent mobility, the powered chair will require adjustment over time
to suit the user's needs, thus regular input from healthcare professionals is
required. These limited resources can result in the user having to wait weeks
for appointments, resulting in the user losing independent mobility,
consequently affecting their quality of life and that of their family and carers. In
order to provide an adaptive assistive driving system, a range of features need
to be identified which are suitable for initial system setup and can automatically
provide data for re-calibration over the long term.
Methods: A questionnaire was designed to collect information from powered
wheelchair users with regard to their symptoms and how they changed over
time. Another group of volunteer participants were asked to drive a test platform
and complete a course which represented manoeuvring in a very confined
space as quickly as possible. Two of those participants were also monitored
over a longer period in their normal home daily environment. Features, thought
to be suitable, were examined using pattern recognition classifiers to determine
their suitability for identifying the changing user input over time.
Results: The results are not designed to provide absolute insight into the
individual user behaviour, as no ground truth of their ability has been
determined, they do nevertheless demonstrate the utility of the measured
features to provide evidence of the users’ changing ability over time whilst
driving a powered wheelchair.
Conclusions: Determining the driving features and adjustable elements
provides the initial step towards developing an adaptable assistive technology
for the user when the ground truths of the individual and their machine have
been learned by a smart pattern recognition system
Gillham, M. and Howells, G. (2015). A Dynamic Localized Adjustable Force Field Method for Real-time Assistive Non-holonomic Mobile Robotics. International Journal of Advanced Robotic Systems [Online]:1. Available at: http://doi.org/10.5772/61190.Providing an assistive navigation system that augments
rather than usurps user control of a powered wheelchair
represents a significant technical challenge. This paper
evaluates an assistive collision avoidance method for a
powered wheelchair that allows the user to navigate safely
whilst maintaining their overall governance of the platform
motion. The paper shows that by shaping, switching and
adjusting localized potential fields we are able to negotiate
different obstacles by generating a more intuitively natural
trajectory, one that does not deviate significantly from the
operator in the loop desired-trajectory. It can also be seen
that this method does not suffer from the local minima
problem, or narrow corridor and proximity oscillation,
which are common problems that occur when using
potential fields. Furthermore this localized method enables
the robotic platform to pass very close to obstacles, such as
when negotiating a narrow passage or doorway.
Gillham, M., Howells, G., Spurgeon, S. and McElroy, B. (2013). Floor Covering and Surface Identification for Assistive Mobile Robotic Real-Time Room Localization Application. Sensors [Online] 13:17501-17515. Available at: http://dx.doi.org/10.3390/s131217501.Assistive robotic applications require systems capable of interaction in the human world, a workspace which is highly dynamic and not always predictable. Mobile assistive devices face the additional and complex problem of when and if intervention should occur; therefore before any trajectory assistance is given, the robotic device must know where it is in real-time, without unnecessary disruption or delay to the user requirements. In this paper, we demonstrate a novel robust method for determining room identification from floor features in a real-time computational frame for autonomous and assistive robotics in the human environment. We utilize two inexpensive sensors: an optical mouse sensor for straightforward and rapid, texture or pattern sampling, and a four color photodiode light sensor for fast color determination. We show how data relating floor texture and color obtained from typical dynamic human environments, using these two sensors, compares favorably with data obtained from a standard webcam. We show that suitable data can be extracted from these two sensors at a rate 16 times faster than a standard webcam, and that these data are in a form which can be rapidly processed using readily available classification techniques, suitable for real-time system application. We achieved a 95% correct classification accuracy identifying 133 rooms’ flooring from 35 classes, suitable for fast coarse global room localization application, boundary crossing detection, and additionally some degree of surface type identification.
Conference or workshop item
Howells, G. and Gillham, M. (2017). Attitude Control of Small Probes for De-orbit, Descent and Surface Impact on Airless Bodies Using a Single PWM Thruster. In: 6th International Conference on Space Mission Challenges for Information Technology. IEEE. Available at: http://dx.doi.org/10.1109/SMC-IT.2017.16.A single thruster attitude and de-orbital control method is proposed, capable of delivering a small spin stabilized probe with payload to the surface of an airless body such as the Moon. Nutation removal, attitude control and fast large angle maneuvers have been demonstrated and shown to be effective using a commercially available single standard cold gas pulse width modulated controlled thruster model. Maximum final impact angle due to drift and residual velocities was found to be less than 5 degrees and the maximum angle of attack to be 4 deg. The conventional 3-axis control would require as many as twelve thrusters requiring a more substantial structure with complex pipework, and a more sophisticated controller. The single thruster concept minimises the mass requirement and thus cost of the mission, making the concept of small networked surface probes for extended science missions more viable. Experiments based on computer simulation have shown that strict design and mission profile requirements can be fulfilled using the single thruster control method.
Chatzidimitriadis, S., Oprea, P., Gillham, M. and Sirlantzis, K. (2017). Evaluation of 3D obstacle avoidance algorithm for smart powered wheelchairs. In: Seventh International Conference on Emerging Security Technologies (EST). IEEE, pp. 157-162. Available at: https://doi.org/10.1109/EST.2017.8090416.This research investigates the feasibility for the development of a novel 3D collision avoidance system for smart powered wheelchairs operating in a cluttered setting by using a scenario generated in a simulated environment using the Robot Operating System development framework. We constructed an innovative interface with a commercially available powered wheelchair system in order to extract joystick data to provide the input for interacting with the simulation. By integrating with a standard PWC control system the user can operate the PWC joystick with the model responding in real-time. The wheelchair model was equipped with a Kinect depth sensor segmented into three layers, two representing the upper body and torso, and a third layer fused with a LIDAR for the leg section. When using the assisted driving algorithm there was a 91.7% reduction in collisions and the course completion rate was 100% compared to 87.5% when not using the algorithm.
Canoz, V., Gillham, M., Oprea, P., Chaumont, P., Bodin, A., Laux, P., Lebigre, M., Howells, G. and Sirlantzis, K. (2017). Embedded hardware for closing the gap between research and industry in the assistive powered wheelchair market. In: IEEE/SICE International Symposium on System Integration (SII 2016). IEEE. Available at: https://doi.org/10.1109/SII.2016.7843983.Literature is abound with smart wheelchair platforms of various developments, yet to date there has been little technology find its way to the market place. Many trials and much research has taken place over the last few decades however the end user has benefited precious little. There exists two fundamental difficulties when developing a smart powered wheelchair assistive system, the first is need for the system to be fully compatible with all of the manufacturers, and the second is to produce a technology and business model which is marketable and therefore desirable to the manufacturers. However this requires the researchers to have access to hardware which can be used to develop practical systems which integrate and communicate seamlessly with current manufacturer’s wheelchair systems. We present our powered wheelchair system which integrates with 95% of the powered wheelchair controller market; our system allows researchers to access the low level embedded system with more powerful computational devices running sophisticated software enabling rapid development of algorithms and techniques. When they have been evaluated they can be easily ported to the embedded processor for real-time evaluation and clinical trial.
Gillham, M., Howells, G., Pepper, M. and Kelly, S. (2016). Developing Effective Intelligent Assistance for the Powered Wheelchair User. In: Technology for Independence Conference (T4i). Available at: https://dx.doi.org/10.15131/shef.data.4256522.This research is working towards developing a pre-production prototype system which can provide a low-cost real-time adjustable and adaptable driving assistance system for powered wheelchair users. Currently we are seeking to obtain information from user joystick input and their driving quality to identify symptoms and make adjustments to the driving assistance system.
Gillham, M., Howells, G. and Kelly, S. (2016). Assistive trajectories for human-in-the-loop mobile robotic platforms. In: Sixth International Conference on Emerging Security Technologies. Available at: http://dx.doi.org/10.1109/EST.2015.19.
Henderson, M., Kelly, S., Horne, R., Gillham, M., Pepper, M. and Capron, J. (2014). Powered Wheelchair Platform for Assistive Technology Development. In: 2014 Fifth International Conference on Emerging Security Technologies (EST),. IEEE, pp. 52-56. Available at: http://dx.doi.org/10.1109/EST.2014.20.Literature shows that numerous wheelchair platforms, of various complexities, have been developed and evaluated for Assistive Technology purposes. However there has been little consideration to providing researchers with an embedded system which is fully compatible, and communicates seamlessly with current manufacturer's wheelchair systems. We present our powered wheelchair platform which allows researchers to mount various inertial and environment sensors, and run guidance and navigation algorithms which can modify the human desired joystick trajectory, so as to assist users with negotiating obstacles, and moving from room to room. We are also able to directly access other currently manufactured human input devices and integrate new and novel input devices into the powered wheelchair platform for clinical and research assessment.
Ferrer, S., Kokosy, A., Capron, J., Pepper, M., Henderson, M., Kelly, S. and Gillham, M. (2014). Système universel à bas coût d’aide à la conduite d’un fauteuil roulant électrique. In: Handicap 2014.
Gillham, M., Howells, G., Spurgeon, S., Kelly, S. and Pepper, M. (2013). Real-time Doorway Detection and Alignment Determination for Improved Trajectory Generation in Assistive Mobile Robotic Wheelchairs. In: Emerging Security Technologies (EST), 2013 Fourth International Conference on. pp. 62-65.Powered wheelchair users may find operation in enclosed environments such as buildings difficult; a fundamental problem exists: wheelchairs are not much narrower than the doorway they wish to pass through. The ability to detect and pass through doorways represents a major current challenge for automated guided wheelchairs. We utilize a simple doorway pattern recognition technique for fast processing in a real-time system for robotic wheelchair users. We are able to show a 96% detection and identification of 5 individual doorways and an 86% recognition rate of 22 separate approach angles and translations. We conclude that pattern recognition using features obtained from simple constrained infrared ranging sensor data binning can be utilized for fast identification of doorways, and important coarse position and approach angle determination, suitable for real-time trajectory adjustment, representing a significant enhancement in this area.
Gillham, M., McElroy, B., Howells, G., Spurgeon, S., Kelly, S., Batchelor, J. and Pepper, M. (2012). Highly efficient Localisation utilising Weightless neural systems. In: 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN. ESANN, pp. 543-548. Available at: https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2012-138.pdf.Efficient localisation is a highly desirable property for an autonomous navigation system. Weightless neural networks offer a real-time approach to robotics applications by reducing hardware and software requirements for pattern recognition techniques. Such networks offer the potential for objects, structures, routes and locations to be easily identified and maps constructed from fused limited sensor data as information becomes available. We show that in the absence of concise and complex information, localisation can be obtained using simple algorithms from data with inherent uncertainties using a combination of Genetic Algorithm techniques applied to a Weightless Neural Architecture.
Gillham, M., McElroy, B., Howells, G., Kelly, S., Spurgeon, S. and Pepper, M. (2012). Weightless Neural System Employing Simple Sensor Data for Efficient Real-Time Round-Corner, Junction and Doorway Detection for Autonomous System Path Planning in Smart Robotic Assisted Healthcare Wheelchairs. In: Emerging Security Technologies (EST), 2012 Third International Conference. IEEE, pp. 161-164. Available at: http://dx.doi.org/10.1109/EST.2012.21.Human assistive devices need to be effective with real-time assistance in real world situations: powered wheelchair users require reassuring robust support, especially in the area of collision avoidance. However, it is important that the intelligent system does not take away control from the user. The patient must be allowed to provide the intelligence in the system and the assistive technology must be engineered to be sufficiently smart to recognize and accommodate this. Robotic assistance employed in the healthcare arena must therefore emphasize positive support rather than adopting an intrusive role. Weightless Neural Networks are an excellent pattern recognition tool for real-time applications. This paper introduces a technique for look-ahead identification of open doorways and junctions. Simple sensor data in real-time is used to detect open doors with inherent data uncertainties using a technique applied to a Weightless Neural Network Architecture.
Gillham, M., Howells, G., Spurgeon, S., Kelly, S. and Pepper, M. (2012). Real-time sensor data for efficient localisation employing a weightless neural system. In: Systems and Computer Science (ICSCS), 2012 1st International Conference on. IEEE, pp. 1-5. Available at: http://dx.doi.org/10.1109/IConSCS.2012.6502448.Mobile robotic localisation obtained from simple sensor data potentially offers real-time real-world integration. Computationally highly efficient Weightless Neural Networks, when used for location determination, further enhances performance potential. This paper introduces techniques for the identification of rooms or locations in the absence of complex and succinct information. Using simple floor colour and texture, and room geometrics from ranging data, although inherent uncertainties exist, these limited simple fused real-time sensor data can be easily resolved into a room identification criterion using architectures generated by a Genetic Algorithm technique applied to a Weightless Neural Network Architecture.