Evidence base

There has been relatively little research investigating muscle activation patterns whilst reaching in a horizontal plane for neurologically intact as well as stroke participants. A normative data set can act as a template to distinguish between normal and abnormal data and so clarify the effectiveness of treatment (Knutson &

Soderburg, 1995).

The temporal relationship between synergistic and antagonistic muscle activity in neurologically intact subjects has been shown to be dependent on direction of movement in supported (Karst & Hasan, 1991; Thoroughman & Shadmehr, 1999) and unsupported reaching (Flanders & Herrmann, 1992) as well as with speed and

57 distance within the sagittal plane. The effect on EMG signals of varying two

components over a reaching task has been examined (Buneo et al., 1994). Varying the length of the trajectory through which the arm moved in a given time resulted in greater EMG amplitude, and varying the length of trajectory while maintaining a constant speed resulted in a decrease in agonist amplitude with distance. In a separate study of muscle activity during both supporting and unsupported reaching in the sagittal plane with healthy older people, changes were identified in EMG amplitude but not timing (Prange et al., 2007).

The EMG activity of shoulder and elbow muscles of neurologically intact subjects was examined during unsupported reaching movements in the horizontal plane in which amplitude speed and direction were varied (Gabriel, 1997). Reaching movements demanding higher angular velocities were associated with increased EMG amplitudes of the shoulder and elbow agonist muscles while temporal parameters between opposing muscle groups at each joint were invariant.

Commonly, studies of chronic stroke participants investigate changes in muscle activation patterns during unassisted movements. Studies have shown that in a single session, reaching movements of the paretic arm of chronic stroke subjects in three dimensions were hindered by inadequate recruitment in the agonist muscles (amplitude rather than timing), not abnormal co-contraction of the agonist and antagonist (Gowland et al., 1992; McCrea et al., 2005). However excess biceps brachii co-contraction limiting performance during voluntary reaching in two dimensions has also been reported (Leonard et al., 2006).

Relatively few studies have examined changes in chronic stroke participants’ muscle activation patterns resulting from an intervention consisting of a robot or electrical stimulation. Lum reported increased antagonist EMG amplitudes in two of four table top movement patterns after training patients in the MIME robot for 24 one hour sessions over an eight week period (Lum et al., 2004). By contrast, Hu reported that electromyographic activation levels of the major agonist and antagonist muscle pair of the elbow joint, biceps brachii and triceps brachii, significantly decreased in the first half of the training course, which was associated with an improvement in tracking skill and a decrease in spasticity (Hu et al., 2007).

No studies were identified that examined how muscle activation patterns vary in either neurologically intact or chronic stroke participants during fully supported

58 reaching across trajectories varying in length, speed and direction with the added variable of resistance. This is despite the fact that resistance training has been shown to reduce musculoskeletal impairments after stroke (Morris et al., 2004).

2.5 Summary

The overall objective of the study was to test the feasibility of re-educating upper limb movement post stroke using ILC mediated by ES using a robotic workstation.

This chapter has provided an overview of the research, knowledge and

understanding of the topics relevant to this research. Parameters known to influence normal movement, motor learning (such as practice intensity and feedback) and different forms of control theory (motor and engineering) have been discussed and put into context, building on existing knowledge.

Reviews of the clinical evidence from robotic therapy demonstrate that changes to motor control have been identified at an impairment level around the shoulder and elbow. Robots allow a participant to have proportional assistance, which has the disadvantage that if the stroke patient does not contribute voluntary movement, the resultant system provides essentially passive movement. The user perception studies show that stroke participants have a positive view of rehabilitation robotics, but have not used a published question set which can be used across different devices.

Reviews of electrical stimulation suggest that positive effects were enhanced when associated with the person’s intention to move, however relatively little work has been done on the shoulder and elbow. Even if a stroke participant does not contribute voluntary movement, benefits are still conferred through the reported benefits of ES such as increased muscle strength (Bowman et al., 1979). Changes in cortical excitability have also been recorded in healthy participants (Ridding et al., 2001) which may apply to stroke participants. The main limitation is that there is little incentive for the participant to work at the limit of their ability which has been

reported as being important for motor learning (Schmidt & Lee, 1999). Techniques have not allowed feedback to adjust stimulation parameters during tasks. An ES system which adjusts the ES in response to the users’ performance in order to provide only the minimum level of stimulation needed to assist the participant in performing a task to a high level of accuracy is required.

59 The chapter concludes with a section on evaluation of the intervention. This includes a discussion of measures, including screening measures and the ICF framework.

Outcome measures chosen to identify unassisted tracking error, and isometric force for neurologically intact and stroke participants using the workstation are outlined.

Additionally for stroke participants, clinical outcome measures, percentage maximum ES and participant perceptions are discussed. Associated changes in muscle activation patterns (using EMG) for neurologically intact and stroke

participants during tracking tasks in the workstation are also measured as part of the evaluation of the intervention. The final section discusses EMG and the factors affecting the signals. The types of normalisation used in this study have been outlined. Existing evidence relating to the assessment of upper limb movement using EMG during reaching for both neurologically intact and stroke participants has been discussed. No studies have been identified that have examined how muscle activation patterns vary in neurologically intact older and stroke participants during fully supported reaching across trajectories varying in length, speed and direction with the added variable of resistance. This will be addressed in the present study.

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3 Methodology

This chapter discusses preliminary work such as the development of the robot, arm modelling and ILC algorithms, and selection of different parameters that were used in the participant studies. These included: selection of the tracking tasks, EMG (muscle choice, procedure for data recording and analysis, and noise evaluation), ES (muscle choice, placing and parameters) and selection of outcome measures (tracking error definition, screening criteria and clinical measures). The following two sections are structured as 1) design, 2) participant information 3) intervention 4) data processing and analysis) and 5) statistical analysis. Section 3.2 outlines the method for answering the question ‘What are the isometric force, unassisted tracking error and muscle activation patterns for neurologically intact participants during tracking tasks in the workstation?’. The final section (3.3) addresses the aims of the study by outlining the method for answering the clinical questions: ‘what are the isometric force, unassisted tracking error and muscle activation patterns for stroke participants during tracking tasks in the workstation?’; how are these affected by an intervention programme using the robot and ES?’; ‘are these changes

associated with clinical outcome measures of impairments and activities?’ and ‘what are the stroke participants’ perceptions of the system?’ .