The phase two ‘walkthrough’ of the Graphical User Interface (GUI), (hardware and control independent) resulted in version 2.0 of the software for the UL FES Rehab Tool. As a result of the user feedback, 23 design revisions were made to the GUI and demonstrated the impact of user involvement and usability testing on the design process. Testing the software and hardware in combination during phase three on healthy participants allowed further refinement of the software and GUI. In addition, a library of suitable functional tasks that therapists could use during the hospital based, final phase of the usability testing was designed and evaluated. Finally, and importantly the functionality of the state machine controller, including the robustness of the angle tracking algorithms, was evaluated on healthy participants. This allowed the design team to iteratively adjust the functionality and GUI at each stage the development process. This is the first study in the UK that provides a detailed report of the impact of therapist involvement on the design of an ANR. A usability engineering approach was successfully utilised in order to identify and address the most significant usability problems with the GUI.
The next chapter, chapter six, covers phase four, the lab based usability evaluation with stroke patients. Due to the importance of setup time on the adoption of medical devices, this phase includes further detail of an early method to predict setup time.
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Chapter 6: Development of a tool to predict setup time
6.1 Introduction
Chapter five outlined the methods and findings from phases two and three of the usability evaluation process (software design refinement and full system rapid prototyping, with healthy participants). Phase three, the rapid prototyping of version 2.0 of the software with healthy participants resulted in a demonstrably robust and usable platform, version 3.0. The next stage in the design process was to test the UL FES Rehab Tool with stroke patients.
Upper limb impairments exhibited post stroke are frequently associated with reduced movement speed, smoothness of movement, precision, as well as an increase in variability of movement and poor coordination (van Vliet et al., 2013; Zackowski et al., 2004). These impairments mean that system evaluation with healthy participants does not provide a sufficient demonstration of efficacy or usability of the system. Specific challenges include: achieving FES-assisted, voluntary-initiated hand opening in the presence of spasticity or contractures (Makowski, Knutson, Chae, & Crago, 2014), achieving robust triggering in the presence of variable movement, together with the potential limits on the extent of stimulation-assisted movement. Additional challenges were delivering an optimum amount of stimulation, to coincide with the particpants’ voluntary effort, so as to produce efficient and smooth movement sequences (Makowski, Knutson, Chae, & Crago, 2013), at the same time as avoiding a hypersensitive response to stimulation. These additional challenges when attempting to use the system with stroke patients are likely to increase the difficulty and hence time taken to setup up the system.
As discussed in chapter two, and highlighted in the literature (Demain et al., 2013; Hochstenbach-Waelen & Seelen, 2012), rapid setup times are crucial to the adoption of rehabilitation technologies. A factor highlighted both throughout the literature (McHugh, Swain, & Jenkinson, 2013), and in the early study advisory group meetings, is the short amount of time available for upper limb therapy. Unsurprisingly, a short setup time was ranked equal first as the most desirable system requirement to emerge from the therapist advisory group meetings. In spite of the importance of short setup times, the literature review in chapter four highlighted the scarcity of studies that have examined setup time for rehabilitation devices (Pedrocchi et al., 2013; Fitzgerald,
115 Kelly, et al., 2008; Dijkers et al., 1991). Even those that did measure setup time tended to rely on self-reports and did not clearly state what they defined as setup time (e.g. when timing commenced and finished) (Prenton et al., 2014; Heller et al., 2013; van Swigchem, Vloothuis, den Boer, Weerdesteyn, & Geurts, 2010; Burridge et al., 2008). A better understanding of these factors has the potential to inform the design and use of future rehabilitation devices.
Although it is clear that setup time should be as short as possible, one issue that has not been addressed in the literature is the need for setup time to be predictable. As has been highlighted previously, therapy time per patient is typically constrained due to limited resources, and as such commencing an ANRT-assisted session, only to run out of time, could dissuade therapists from using the system. Some aspects of setup time for ANRT are inherent in the design of the device, e.g. donning of electrodes and sensors, and adjustment of stimulation levels. Whereas other aspects, such as the choice of functional task and the alignment of this to the patients level of impairment and functional goals are modifiable. Some researchers have already recognised the need to utilise patients’ clinical presentation to inform setup parameters for ANRT (Cozens et al., 2013), in this case robotic therapy. The author was part of a clinical team of experts that developed an informatics framework, SILCK (Synthesising and Interpreting Language for Clinical Kinematics), that has been embedded within software to allow automated control of a rehabilitation robotic device, iPAM. This concept has the potential to be utilised and developed for other ANRT, ultimately reducing the overhead of setup time and improving device usability. Therefore, the aim of the work is to develop a tool for the prediction of setup time for the UL FES Rehab Tool.