A design framework for a home-based stroke rehabilitation system: Identifying the key components

Rennick-Egglestone, Stefan; Axelrod, Lesley; Nind, Thomas; Turk, Ruth; Wilkinson, Anna; Burridge, Jane; Fitzpatrick, Geraldine; Mawson, Sue; Robertson, Zoe; Hughes, Ann‐Marie; Ng, Kher Hui; Pearson, Will; Shublaq, Nour; Probert-Smith, Penny; Rickets, I.W.; Rodden, Tom

doi:10.4108/icst.pervasivehealth2009.6049

Cited by 41 publications

(35 citation statements)

References 38 publications

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“…In total we collected over 40 hours of log data, 10 hours of interviews, and made 17 phone calls and 22 home visits. This process took place in conjunction with a number of other activities including initial scoping studies and design workshops [3,26], and an analysis of clinical needs for upper limb function.…”

Section: Design Methodology and Study Overviewmentioning

confidence: 99%

“…One approach to addressing this has been the development of computational systems to reduce the cost of therapy in a clinical setting, including complex "robotic therapists" which can guide limbs through movements [22]. VR systems have also been used to promote rehabilitative exercise [26]. More recent approaches have focused on technologies that support self-managed rehabilitation in the home [12,13,18,25], which is understandable given that returning home quickly tends to be a strong preference for people recovering from stroke [28], and given the tendency to discharge patients from the clinical setting earlier [11].…”

Section: Stroke Rehabilitation and Motivationmentioning

confidence: 99%

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Motivating mobility

Balaam

Rennick-Egglestone

Fitzpatrick

et al. 2011

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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How to motivate and support behaviour change through design is becoming of increasing interest to the CHI community. In this paper, we present our experiences of building systems that motivate people to engage in upper limb rehabilitation exercise after stroke. We report on participatory design work with four stroke survivors to develop a holistic understanding of their motivation and rehabilitation needs, and to construct and deploy engaging interactive systems that satisfy these. We reflect on the limits of motivational theories in trying to design for the lived experience of motivation and highlight lessons learnt around: helping people articulate what motivates them; balancing work, duty, fun; supporting motivation over time; and understanding the wider social context. From these we identify design guidelines that can inform a toolkit approach to support both scalability and personalisability.

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Section: Design Methodology and Study Overviewmentioning

confidence: 99%

Section: Stroke Rehabilitation and Motivationmentioning

confidence: 99%

Motivating mobility

Balaam

Rennick-Egglestone

Fitzpatrick

et al. 2011

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…Already in 2009 Chan et al [13] pointed out that the technology development of home-care support was dominated by a technology-push rather than a demand-pull approach and a better understanding of human needs would help put attention on use demands rather than what is technologically possible. In recent years there has however been an increased interest in pervasive health applications and the end-users of healthcare technology, from both industry and the CSCW and HCI research communities [10,[14][15][16][17][18][19].…”

Section: State-of-the-artmentioning

confidence: 99%

Managing Telehealth and Telecare

Turner

2014

Pervasive Health

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“…In 2009, stroke was the principal single cause of adult disability in the developed world [2]. A common sequela to survivors is motor disability of the upper limb.…”

Section: Introductionmentioning

confidence: 99%

Detecting affective states in virtual rehabilitation

Rivas¹,

Orihuela‐Espina²,

Sucar³

et al. 2015

Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare

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Abstract-Virtual rehabilitation supports motor training following stroke by means of tailored virtual environments. To optimize therapy outcome, virtual rehabilitation systems automatically adapt to the different patients' changing needs. Adaptation decisions should ideally be guided by both the observable performance and the hidden mind state of the user. We hypothesize that some affective aspects can be inferred from observable metrics. Here we present preliminary results of a classification exercise to decide on 4 states; tiredness, tension, pain and satisfaction. Descriptors of 3D hand movement and finger pressure were collected from 2 post-stroke participants while they practice on a virtual rehabilitation platform. Linear Support Vector Machine models were learnt to unfold a predictive relation between observation and the affective states considered. Initial results are promising (ROC Area under the curve (meanstd): 0.713  0.137). Confirmation of these opens the door to incorporate surrogates of mind state into the algorithm deciding on therapy adaptation.

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A design framework for a home-based stroke rehabilitation system: Identifying the key components

Cited by 41 publications

References 38 publications

Motivating mobility

Motivating mobility

Managing Telehealth and Telecare

Detecting affective states in virtual rehabilitation

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