Priorities for the design and control of upper limb prostheses: A focus group study.

BACKGROUND Common prosthetic options do not allow for enough independent control signals to control all the movements of the arm. Invasive approaches to obtain prosthetic control signals are being developed to provide people with upper limb loss improved prosthetic control and feedback. OBJECTIVE/HYPOTHESIS This study explored the prosthetic qualities that are important to users and examined the factors that play into the decision to consider invasive prosthetic interfaces that allow for enhanced prosthetic control. METHODS Individuals participated in semi-structured focus groups or in individual semi-structured interviews (N = 11). A semi-structured interview guide containing open-ended questions was used to learn about ideal prosthesis qualities and interest in prosthetic technology interfaces including targeted muscle reinnervation, peripheral nerve interface, and cortical interface. Qualitative content analysis with an inductive approach was used for transcript analysis. RESULTS Participants were most interested in improving the dexterity and durability of prosthetic options. Recovery time, anticipated risk, medical co-morbidities, and baseline functional status influenced willingness to consider invasive prosthetic interfaces. Participants were interested in learning more about all three invasive interfaces but had the most concerns about cortical interfaces. CONCLUSIONS Attitudes toward invasive control interfaces vary. Further education on invasive control interfaces and additional conversations between prosthetic developers and people with limb loss will help to develop effective prosthetic devices that potential consumers will use.

[1]  J.W. Sensinger,et al.  Adaptive Pattern Recognition of Myoelectric Signals: Exploration of Conceptual Framework and Practical Algorithms , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Z T Irwin,et al.  Chronic recording of hand prosthesis control signals via a regenerative peripheral nerve interface in a rhesus macaque. , 2016, Journal of neural engineering.

[3]  M. J. Highsmith,et al.  Differences in myoelectric and body-powered upper-limb prostheses: Systematic literature review. , 2015, Journal of rehabilitation research and development.

[4]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[5]  Dario Farina,et al.  Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals , 2004, IEEE Transactions on Biomedical Engineering.

[6]  M. Bransby-Zachary,et al.  Upper Limb Traumatic Amputees , 1997, Journal of hand surgery.

[7]  Jacob L. Segil,et al.  Mechanical design and performance specifications of anthropomorphic prosthetic hands: a review. , 2013, Journal of rehabilitation research and development.

[8]  H. Bernard Research Methods in Anthropology: Qualitative and Quantitative Approaches , 1988 .

[9]  R.Fff. Weir,et al.  A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Todd A Kuiken,et al.  Targeted Muscle Reinnervation and Advanced Prosthetic Arms , 2015, Seminars in Plastic Surgery.

[11]  H. Bernard,et al.  Techniques to Identify Themes , 2003 .

[12]  E. Biddiss,et al.  Upper limb prosthesis use and abandonment: A survey of the last 25 years , 2007, Prosthetics and orthotics international.

[13]  Alexandre Cardoso,et al.  Classification of EMG signals using artificial neural networks for virtual hand prosthesis control , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Josef Parvizi,et al.  Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas , 2013, Journal of neural engineering.

[15]  Dinesh Bhatia,et al.  Growth and Advancements in Neural Control of Limb , 2015 .

[16]  J. Olson,et al.  The evolution of functional hand replacement: From iron prostheses to hand transplantation. , 2014, Plastic surgery.

[17]  Cynthia A Chestek,et al.  Factors associated with interest in novel interfaces for upper limb prosthesis control , 2017, PloS one.

[18]  Reid R. Harrison,et al.  Recording sensory and motor information from peripheral nerves with Utah Slanted Electrode Arrays , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  J P Durance,et al.  Upper limb amputees: a clinic profile. , 1988, International disability studies.

[20]  M. Swiontkowski Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms , 2010 .

[21]  J. Davidson A survey of the satisfaction of upper limb amputees with their prostheses, their lifestyles, and their abilities. , 2002, Journal of hand therapy : official journal of the American Society of Hand Therapists.

[22]  Mark Luborsky,et al.  The identification and analysis of themes and patterns. , 1994 .

[23]  Christopher M. Frost,et al.  Development of a Regenerative Peripheral Nerve Interface for Control of a Neuroprosthetic Limb , 2016, BioMed research international.

[24]  D. Durand,et al.  Improved nerve cuff electrode recordings with subthreshold anodic currents , 1998, IEEE Transactions on Biomedical Engineering.

[25]  Kengo Ohnishi,et al.  Neural machine interfaces for controlling multifunctional powered upper-limb prostheses , 2007, Expert review of medical devices.

[26]  Ulrika Wijk Ot,et al.  Forearm amputees' views of prosthesis use and sensory feedback , 2015 .

[27]  Alicia J. Davis,et al.  Surveying the interest of individuals with upper limb loss in novel prosthetic control techniques , 2015, Journal of NeuroEngineering and Rehabilitation.