Our latest article describing the results from SCRIPT project is now online on JNER (open access, here)
Here is a copy of the abstract:
Assistive and robotic training devices are increasingly used for rehabilitation of the hemiparetic arm after stroke, although applications for the wrist and hand are trailing behind. Furthermore, applying a training device in domestic settings may enable an increased training dose of functional arm and hand training. The objective of this study was to assess the feasibility and potential clinical changes associated with a technology-supported arm and hand training system at home for patients with chronic stroke.
A dynamic wrist and hand orthosis was combined with a remotely monitored user interface with motivational gaming environment for self-administered training at home. Twenty-four chronic stroke patients with impaired arm/hand function were recruited to use the training system at home for six weeks. Evaluation of feasibility involved training duration, usability and motivation. Clinical outcomes on arm/hand function, activity and participation were assessed before and after six weeks of training and at two-month follow-up.
Mean System Usability Scale score was 69 % (SD 17 %), mean Intrinsic Motivation Inventory score was 5.2 (SD 0.9) points, and mean training duration per week was 105 (SD 66) minutes. Median Fugl-Meyer score improved from 37 (IQR 30) pre-training to 41 (IQR 32) post-training and was sustained at two-month follow-up (40 (IQR 32)). The Stroke Impact Scale improved from 56.3 (SD 13.2) pre-training to 60.0 (SD 13.9) post-training, with a trend at follow-up (59.8 (SD 15.2)). No significant improvements were found on the Action Research Arm Test and Motor Activity Log.
Remotely monitored post-stroke training at home applying gaming exercises while physically supporting the wrist and hand showed to be feasible: participants were able and motivated to use the training system independently at home. Usability shows potential, although several usability issues need further attention. Upper extremity function and quality of life improved after training, although dexterity did not. These findings indicate that home-based arm and hand training with physical support from a dynamic orthosis is a feasible tool to enable self-administered practice at home. Such an approach enables practice without dependence on therapist availability, allowing an increase in training dose with respect to treatment in supervised settings.
This study has been registered at the Netherlands Trial Registry (NTR): NTR3669.
Purpose: We drew on an interdisciplinary research design to examine stroke survivors’ experiences of living with stroke and with technology in order to provide technology developers with insight into values, thoughts and feelings of the potential users of a to-be-designed robotic technology for home-based rehabilitation of the hand and wrist. Method: Ten stroke survivors and their family carers were purposefully selected. On the first home visit, they were introduced to cultural probe. On the second visit, the content of the probe packs were used as prompt to conduct one-to-one interviews with them. The data generated was analysed using thematic analysis. A third home visit was conducted to evaluate the early prototype. Results: User requirements were categorised into their network of relationships, their attitude towards technology, their skills, their goals and motivations. The user requirements were used to envision the requirements of the system including providing feedback on performance, motivational aspects and usability of the system. Participants’ views on the system requirements were obtained during a participatory evaluation. Conclusion: This study showed that prior to the development of technology, it is important to engage with potential users to identify user requirements and subsequently envision system requirements based on users’ views.Implications for Rehabilitation
An understanding of how stroke survivors make sense of their experiences of living with stroke is needed to design home-based rehabilitation technologies.
Linking stroke survivors’ goals, motivations, behaviour, feelings and attitude to user requirements prior to technology development has a significant impact on improving the design.
Read More: http://informahealthcare.com/doi/abs/10.3109/17483107.2015.1036469
We have had a press release of SCRIPT project which has been followed by enthusiasm from the media and also stroke patients and stroke community urging us to make the device available to the market ASAP.
A video on demand coverage is also available here: RupltyTV
The following new team members have joined us recently:
- Miss Udeshika Dissanayake, researching in the area of rehabilitation robots and QEEG feedback
- Miss Bernadette Iyawe, researching in the area of designing assistive and haptic affordances for the blind and partially sighted
- Mr Sudhir Sharma, researching serious games and robotics for rehabilitation
- Mr Azeemsha Thacham Poyil, researching combining robotics with EMG and QEEG
I am glad to let you know that Accompany project is receiving media attention, if interested, please follow these links:
I am glad to let you know of the launch of an exciting rehabilitation and assistive technology journal:
Initial submissions will have a discounted open access fee so submissions are encouraged. Any queries, please write to me.
Description: Fitts (1954) presented a model for human psychomotor behaviour for rapid aimed movements. The model provides an index of performance that could be used for analysing performance when aiming at targets. This model has been studied by different groups including MacKenzie (1992) who applied the model to human-computer interfaces. This PhD is aimed at incorporating Fitts model and MacKenzie’s method to assess human performance and compare between healthy individuals and those with impairments caused by some conditions such as stroke, traumatic brain injury, multiple sclerosis and spinal cord injury. Furthermore, it will focus on not only assessing and predicting, but also developing adaptive models that can make the interface easier to use for different user groups.
Applicants should have a very strong first degree or (preferably) a Master’s degree in Cybernetics, Computer Science, Biomechanics or other relevant area, and are expected to have strong interdisciplinary interests (e.g. in robotics, rehabilitation, neuroscience). They are also expected to have very good programming skills.
The PhD will be conducted under Dr Farshid Amirabdollahian and Dr Steuber’s supervision and candidates are invited to informally contact Dr Amirabdollahian.
Applications: application forms can be found here.
Further information and an application form can be obtained from Mrs Lorraine Nicholls, Research Student Administrator, STRI, University of Hertfordshire, College Lane, Hatfield, Herts, AL10 9AB, Tel: 01707 286083, email: firstname.lastname@example.org.
Alternatively, follow the link to online applications, download the file and email to Mrs Nichols. Applications should also include two references and transcripts of previous academic degrees. The short-listing process for studentship applications will begin on 9 June 2014.
Deadline: Applications should also include two references and transcripts of previous academic degrees. The short-listing process for studentship applications will begin on 9 June 2014.
We invite applications for a PhD studentship in the Centre for Computer Science and Informatics Research at the University of Hertfordshire. The project will involve the design of adaptive rehabilitation and assistive robotics systems that are based on computational models of the cerebellum. For informal enquiries contact Dr Farshid Amirabdollahian (email@example.com) or Dr Volker Steuber (firstname.lastname@example.org). More information can be found on our webpages:
and in our publications, for example:
- Reinoud Maex and Volker Steuber (2013). An integrator circuit in cerebellar cortex. European Journal of Neuroscience 38, 2917-32.
- Radhika Chemuturi, Farshid Amirabdollahian and Kerstin Dautenhahn (2013). Adaptive training algorithm for robot-assisted upper-arm rehabilitation, applicable to individualised and therapeutic human-robot interaction. Journal of Neuroengineering and Rehabilitation 10:102.
- Volker Steuber and Dieter Jaeger (2012). Modeling the generation of output by the cerebellar nuclei. Neural Networks 47, 112-119.
- Farshid Amirabdollahian and Garth Johnson (2011). Analysis of the results from use of haptic peg-in-hole task for assessment in neurorehabilitation. Journal of Applied Bionics and Biomechanics 8, 1-11.
- Jason Rothman, Laurence Cathala, Volker Steuber and R. Angus Silver (2009). Synaptic depression enables neuronal gain control. Nature 457, 1015-1018.
- Farshid Amirabdollahian, Rui Loureiro, Elizabeth Gradwell, Christine Collin, William Harwin, Garth Johnson (2007). Multivariate Analysis of the Fugl-Meyer Outcome Measures Assessing the Effectiveness of the GENTLE/S Robot-Mediated Stroke Therapy. Journal of Neuroengineering and Rehabilitation 4:4.
Applicants should have excellent computational and numerical skills and a good first degree in computer science, maths, physics, neuroscience, or a related discipline. Successful candidates are eligible for a research studentship award from the University (approximately GBP 13,600 per annum bursary plus the payment of the standard UK student fees). Applicants from outside the UK or EU are eligible, but will have to pay half of the overseas fees out of their bursary. Information about the current tuition fees can be found under http://www.herts.ac.uk/apply/fees-and-funding.
Research in Computer Science at the University of Hertfordshire has been recognized as excellent by the latest Research Assessment Exercise, with 55% of the research submitted being rated as world leading or internationally excellent. The Centre for Computer Science and Informatics Research provides a very stimulating environment, offering a large number of specialized and interdisciplinary seminars as well as general training opportunities. The University of Hertfordshire is situated in Hatfield, in the green belt just north of London.
Application forms should be returned to Mrs Lorraine Nicholls, Research Student Administrator, STRI, University of Hertfordshire, College Lane, Hatfield, Herts, AL10 9AB, Tel: 01707 286083, l.nicholls @ herts.ac.uk. The short-listing process will begin on 20 February 2014.