Christopher Freeman

University of Southampton, Southampton, England, United Kingdom

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Publications (47)12.7 Total impact

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    ABSTRACT: Therapies using functional electrical stimulation (FES) in conjunction with practice of everyday tasks have proven effective in facilitating recovery of upper limb function following stroke. The aim of the current study is to develop a multi-channel electrical stimulation system that precisely controls the assistance provided in goal-orientated tasks through use of advanced model-based 'iterative learning control' (ILC) algorithms to facilitate functional motor recovery of the upper limb post-stroke. FES was applied to three muscle groups in the upper limb (the anterior deltoid, triceps and wrist extensors) to assist hemiparetic, chronic stroke participants to perform a series of functional tasks with real objects, including closing a drawer, turning on a light switch and repositioning an object. Position data from the participants' impaired upper limb was collected using a Microsoft Kinect® and was compared to an ideal reference. ILC used data from previous attempts at the task to moderate the FES signals applied to each muscle group on a trial by trial basis to reduce performance error whilst supporting voluntary effort by the participant. The clinical trial is on-going. Preliminary results show improvements in performance accuracy for each muscle group, as well as improvements in clinical outcome measures pre and post 18 training sessions. Thus, the feasibility of applying precisely controlled FES to three muscle groups in the upper limb to facilitate functional reach and grasp movements post stroke has been demonstrated.
    IEEE ... International Conference on Rehabilitation Robotics : [proceedings]. 06/2013; 2013:1-5.
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    ABSTRACT: Introduction Following stroke, over 50% of patients have an impairment of one arm, affecting their ability to perform everyday reach and grasp tasks. Functional electrical stimulation (FES) has been shown to restore movement, with effectiveness increased when combined with voluntary intention. Recent clinical trials that incorporated an advanced control framework to adjust FES applied to two muscle groups in a virtual reality scenario showed reductions in impairment. Aim The current study examines the feasibility of providing precisely controlled FES to three muscle groups in the upper limb, assisting performance of real-world, functional reach and grasp tasks. Method Data were recorded from 5 hemiparetic, chronic stroke participants who undertook 18, 1 hour training sessions comprising functional tasks, such as button pressing and closing a drawer. Assistive FES, controlled by advanced iterative learning (ILC) algorithms, was applied to the anterior deltoid, triceps and wrist extensors of the impaired limb. ILC uses data from previous task attempts to update the FES applied to each muscle group on the next trial, increasing performance and encouraging voluntary effort. For assessment, participants completed unassisted and assisted functional tasks, and the error between participants? movement and idealised trajectories were recorded. The Fugl-Meyer and Action Research Arm Test were also completed pre and post-training. Results Preliminary results show that performance error reduced over a range of functional tasks. Overall improvements ranged from 22-32%, though individual joint improvement reached 63%. Data collection is on-going. Discussion The feasibility of applying precisely controlled FES to three muscle groups in the upper limb was demonstrated. The application of this technology is expected to significantly reduce upper limb impairment following chronic stroke. Work is underway to include an electrode array to precisely stimulate individual finger and hand extensors, and to adapt the system for home use. Acknowledgements This research was funded by the EPSRC (EP/I01909X/1) and a Wessex Medical Innovation Grant.
    4th Annual Conference of the International Functional Electrical Stimulation Society (UK and Ireland Chapter); 04/2013
  • 01/2013;
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    ABSTRACT: An upper-limb stroke rehabilitation system is developed that assists patients in performing real world functionally relevant reaching tasks. The system provides de-weighting of the arm via a simple spring support whilst functional electrical stimulation is applied to the anterior deltoid and triceps via surface electrodes, and to the wrist and hand extensors via a 40 element surface electrode array. Iterative learning control (ILC) is used to mediate the electrical stimulation, and updates the stimulation signal applied to each muscle group based on the error between the ideal and actual movement in the previous attempt. The control system applies the minimum amount of stimulation required, maximising voluntary effort. Low-cost, markerless motion tracking is provided via a Microsoft Kinect, with hand and wrist data provided by an electrogoniometer or data glove. The system is described and initial experimental results are presented for a stroke patient starting treatment.
    01/2013;
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    Fengmin Le, Ivan Markovsky, Christopher Freeman, Eric Rogers
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    ABSTRACT: Two methods for recursive identification of Hammerstein systems are considered. In the first method, the recursive least squares algorithm is applied to an overparameterized representation of the Hammerstein model and a rank-1 approximation is used to recover the linear and nonlinear parameters from the estimated overparameterized form. In the second method, the linear and nonlinear parameters are recursively estimated in an alternate manner. The superiority of the second method is confirmed using a numerical simulation example, together with experimentally measured data from electrically stimulated muscles.
    04/2012;
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    ABSTRACT: To provide effective stroke rehabilitation, a control scheme is developed for upper arm tracking in 3D space using electrical stimulation. In accordance with clinical need, the case where stimulation is applied to two muscles in the arm and shoulder is considered, with the arm supported against gravity by an exoskeletal mechanism. An upper limb model with five degrees of freedom is first developed to represent the unconstrained upper arm, and an input/output linearization controller is applied to decouple the actuated joint angles, and combined with a state-feedback optimal tracking controller. Linear iterative learning controllers are then designed to enforce precise tracking over repeated attempts at the task, and stability conditions for the unactuated joint angles are given. Experimental results confirm practical performance.
    American Control Conference (ACC), 2012; 01/2012
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    ABSTRACT: This paper considers iterative learning control law design for both trial-to-trial error convergence and along the trial performance. It is shown how a class of control laws can be designed using the theory of linear repetitive processes for this problem where the computations are in terms of Linear Matrix Inequalities (LMIs). Results from the experimental application of these laws on a gantry robot performing a pick and place operation are also given. The control laws only use plant output information and hence the use of a state observer is avoided.
    01/2012;
  • Ying Tan, Jian-Xin Xu, Mikael Norrlof, Christopher Freeman
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    ABSTRACT: Input saturation is inevitable in many engineering applications. Most existing iterative learning control (ILC) algorithms that can deal with input saturation require that the reference signal is realizable within the saturation bound. For engineering systems without precise models, it is hard to verify this requirement. In this note, a “reference governor” (RG) is introduced and is incorporated with a range of existing ILC algorithms (primary ILC algorithms). The role of the RG is to re-design the reference signal so that the modified reference signal is realizable. Two types of the RG are proposed: one modifies the amplitude of the reference signal and the other modifies the frequency. Our main results provide design guidelines for two RGs. Moreover, a design trade-off between the convergence speed and tracking performance is also discussed. A simple simulation result verifies the effectiveness of the proposed methods.
    01/2011;
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    ABSTRACT: An upper limb stroke rehabilitation system is developed which combines electrical stimulation with mechanical arm support, to assist patients to perform 3D reaching tasks in a virtual reality environment. The Stimulation Assistance through Iterative Learning (SAIL) platform applies electrical stimulation to two muscles in the arm using model-based control schemes which learn from previous trials of the task. This results in highly accurate movement which maximises the therapeutic effect of treatment. The principal components of the system are described and experimental results confirm its efficacy for clinical use in upper limb stroke rehabilitation.
    01/2011;
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    ABSTRACT: A system is developed which combines electrical stimulation with a robotic support system to provide assistance to stroke patients performing 3D upper limb reaching tasks in a virtual reality environment. The electrical stimulation is applied to two muscles in the subject's arm using model-based control schemes which learn from previous trials of the task in order to result it highly accurate movement. The principal components of the system are described and experimental results conrm its ecacy for use in upper limb stroke rehabilitation.
    01/2011;
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    ABSTRACT: Iterative Learning Control algorithms have been shown to offer a high level of performance both theoretically and in practical applications. However the convergence of the error is generally highly dependent on the initial choice of input applied to the plant. Here techniques are applied which generate an optimal initial input selection, and the effect this has on the error over subsequent trials is examined. The approach is then applied experimentally on a gantry robot test facility.
    01/2011;
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    Zhonglun Cai, Daisy Tong, Christopher Freeman, Eric Rogers
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    ABSTRACT: A nonlinear model-based iterative learning control (ILC) algorithm is applied to the problem of upper limb stroke rehabilitation. A 3D system is developed to assist stroke patients performing 3D upper limb reaching tasks in a virtual reality environment, combining a mechanical support with electrical stimulation applied to two muscles in the patient's arm. ILC is shown to provide accurate trajectory tracking, which maximises the system's potential to provide effective treatment during future clinical trials. Principal components of the system are described and experimental results confirm its efficacy for use in upper limb stroke rehabilitation.
    01/2011;
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    ABSTRACT: This paper develops significant new results on the design of Iterative Learning Control (ILC) schemes based on treating the problem within the framework of the stability/control theory for linear repetitive processes. These processes propagate in two independent directions and arise in the modeling of a number of physical processes. The duration of information propagation in one of the two directions is finite, and this is a key link to ILC which has been developed as a technique for controlling systems which are required to repeat the same operation over a finite duration known as the trial length and information from previous executions is used to update the control input for the next trial and thereby sequentially improve performance. The experimental performance of the new algorithms on a gantry robot is reported, including a comparison with alternative designs.
    IEEE Transactions on Control Systems Technology 01/2011; · 2.00 Impact Factor
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    ABSTRACT: An upper limb stroke rehabilitation system is developed which combines electrical stimulation with mechanical arm support, to assist patients performing 3D reaching tasks in a virtual reality environment. The Stimulation Assistance through Iterative Learning (SAIL) platform applies electrical stimulation to two muscles in the arm using model-based control schemes which learn from previous trials of the task. This results in accurate movement which maximises the therapeutic effect of treatment. The principal components of the system are described and experimental results confirm its efficacy for clinical use in upper limb stroke rehabilitation.
    01/2011;
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    ABSTRACT: The initial choice of input in iterative learning control (ILC) generally has a signifficant effect on the error incurred over subsequent trials. In this paper techniques are developed which use experimental data gathered over previous applications of ILC in order to generate an initial input signal for the tracking of a new reference trajectory. A model-based approach is then incorporated to overcome the limitation of insufficient previous experimental data, and a robust design procedure is developed. Experimental evaluation results are obtained using a gantry robot facility.
    01/2010;
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    Fengmin Le, Ivan Markovsky, Christopher T. Freeman, Eric Rogers
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    ABSTRACT: Despite significant recent interest in the identification of electrically stimulated muscle models, current methods are based on underlying models and identification techniques that make them unsuitable for use with subjects who have incomplete paralysis. One consequence of this is that very few model-based controllers have been used in clinical trials. Motivated by one case where a model-based controller has been applied to the upper limb of stroke patients, and the modelling limitations that were encountered, this paper first undertakes a review of existing modelling techniques with particular emphasis on their limitations. A Hammerstein structure, already known in this area, is then selected, and a suitable identification procedure and set of excitation inputs are developed to address these short-comings. The technique that is proposed to obtain the model parameters from measured data is a combination of two iterative schemes: the first of these has rapid convergence and is based on alternating least squares, and the second is a more complex method to further improve accuracy. Finally, experimental results are used to assess the efficacy of the overall approach.
    Control Engineering Practice. 01/2010;
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    ABSTRACT: Many people have problems with using their arm after a stroke, which can affect their ability to perform activities like reaching out to pick an object up. Over the last few years new technologies which use robots and electrical stimulation (ES) of muscles have been used to help with these problems. We have developed a new system which employs both of these technologies in conjunction with a technique called “Iterative Learning Control” (ILC) to help people recover movement in their arm. The aim of this study was to investigate whether ILC mediated by ES is a plausible intervention in upper limb stroke rehabilitation. Five hemiparetic participants with reduced upper limb function were asked to move their arm to track a slowing moving dot of light. This was for planar motion, replicating reaching out to an object across a table top. The participants’ arm was supported by the robot and ES was applied to the triceps brachii muscle. During each training task, the same tracking movement was repeated 6 times. After each repetition, ILC was used to compute the amount of stimulation (amplitude and timing) to be applied to the muscle on the next trial by incorporating data from previous trials of the task. Participants undertook 18 or 25, 1hour treatment sessions composed of training tasks varying in length, orientation and speed. Unassisted tracking (no ES supplied) was undertaken pre and post each treatment session. The Action Research Arm Test and the Fugl-Meyer Assessment (FMA) test were administered pre and post all treatment sessions. Significant improvements were found in 3 of 4 unassisted tracking tasks. FMA results also showed an improvement (although this was not clinically relevant). Thus, this study has shown feasibility in using ILC mediated ES for upper limb rehabilitation.
    01/2010;
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    Christopher Freeman, Paul Lewin, Eric Rogers, James Ratcliffe
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    ABSTRACT: Synchronisation is routinely required to coordinate the actions of the various sub-systems involved in process applications. This is commonly achieved through direct mechanical coupling, involving gears, drive belts and cams. Apart from the additional cost incurred, these components are subject to wear, constrain the layout of the plant, and may have limited accuracy. It is shown in this paper that a mechanical linkage between two sub-systems may be replaced by instead implementing a control scheme comprising an iterative learning controller together with a supervisory control loop. To illustrate the approach, two types of iterative learning controller are first implemented on a gantry robot test facility to confirm the high levels of tracking accuracy that may be achieved. The supervisory control loop is then added to synchronise the ‘pick and place’ action of the robot with a conveyor system moving at constant velocity. Experimental results are provided to confirm both the accurate tracking performance produced by the iterative learning controller, and the high level of synchronisation achieved by the overall scheme.
    Transactions of the Institute of Measurement and Control 01/2010; · 0.66 Impact Factor
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    ABSTRACT: An inability to perform tasks involving reaching is a common problem for stroke patients. This paper provides an insight into mechanisms associated with recovery of upper limb function by examining how stroke participants’ upper limb muscle activation patterns differ from those of neurologically intact participants, and how they change in response to an intervention. In this study, five chronic stroke participants undertook nine tracking tasks in which trajectory (orientation and length), speed and resistance to movement were varied. During these tasks, EMG signals were recorded from triceps, biceps, anterior deltoid, upper, middle and lower trapezius and pectoralis major. Data collection was performed in sessions both before, and after, an intervention in which participants performed a similar range of tracking tasks with the addition of responsive electrical stimulation applied to their triceps muscle. The intervention consisted of eighteen one hour treatment sessions, with two participants attending an additional seven sessions. During all sessions, each participant’s arm was supported by a hinged arm-holder which constrained their hand to move in a two dimensional plane. Analysis of the pre intervention EMG data showed that timing and amplitude of peak EMG activity for all stroke participants differed from neurologically intact participants. Analysis of post intervention EMG data revealed that statistically significant changes in these quantities had occurred towards those of neurologically intact participants.
    Journal of electromyography and kinesiology: official journal of the International Society of Electrophysiological Kinesiology 10/2009; · 2.00 Impact Factor
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    ABSTRACT: An experimental test facility is developed for use by stroke patients in order to improve sensory-motor function of their upper limb. Subjects are seated at the workstation and their task is to repeatedly follow reaching trajectories that are projected onto a target above their arm. To do this they use voluntary control with the addition of electrical stimulation mediated by advanced control schemes applied to muscles in their impaired shoulder and arm. Full details of the design of the workstation and its periphery systems are given, together with a description of its use during the treatment of stroke patients.
    Medical Engineering & Physics 04/2009; · 1.78 Impact Factor