Control within a virtual environment is correlated to functional outcomes when using a physical prosthesis

BackgroundAdvances such as targeted muscle reinnervation and pattern recognition control may provide improved control of upper limb myoelectric prostheses, but evaluating user function remains challenging. Virtual environments are cost-effective and immersive tools that are increasingly used to provide practice and evaluate prosthesis control, but the relationship between virtual and physical outcomes—i.e., whether practice in a virtual environment translates to improved physical performance—is not understood.MethodsNine people with transhumeral amputations who previously had targeted muscle reinnervation surgery were fitted with a myoelectric prosthesis comprising a commercially available elbow, wrist, terminal device, and pattern recognition control system. Virtual and physical outcome measures were obtained before and after a 6-week home trial of the prosthesis.ResultsAfter the home trial, subjects showed statistically significant improvements (p < 0.05) in offline classification error, the virtual Target Achievement Control test, and the physical Southampton Hand Assessment Procedure and Box and Blocks Test. A trend toward improvement was also observed in the physical Clothespin Relocation task and Jebsen-Taylor test; however, these changes were not statistically significant. The median completion time in the virtual test correlated strongly and significantly with the Southampton Hand Assessment Procedure (p = 0.05, R = − 0.86), Box and Blocks Test (p = 0.007, R = − 0.82), Jebsen-Taylor Test (p = 0.003, R = 0.87), and the Assessment of Capacity for Myoelectric Control (p = 0.005,R = − 0.85). The classification error performance only had a significant correlation with the Clothespin Relocation Test (p = 0.018, R = .76).ConclusionsIn-home practice with a pattern recognition-controlled prosthesis improves functional control, as measured by both virtual and physical outcome measures. However, virtual measures need to be validated and standardized to ensure reliability in a clinical or research setting.Trial registrationThis is a registered clinical trial: NCT03097978.

[1]  Nitish V. Thakor,et al.  User Training for Pattern Recognition-Based Myoelectric Prostheses: Improving Phantom Limb Movement Consistency and Distinguishability , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Laura A. Miller,et al.  Summary and Recommendations of the Academy's State of the Science Conference on Upper Limb Prosthetic Outcome Measures , 2009 .

[3]  B. Hudgins,et al.  A Real-Time Pattern Recognition Based Myoelectric Control Usability Study Implemented in a Virtual Environment , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Denise Reid,et al.  The use of virtual reality to improve upper-extremity efficiency skills in children with cerebral palsy: A pilot study , 2002 .

[5]  T. Kuiken,et al.  Targeted Reinnervation for Transhumeral Amputees: Current Surgical Technique and Update on Results , 2009, Plastic and reconstructive surgery.

[6]  A. M. Simon,et al.  Patient Training for Functional Use of Pattern Recognition–Controlled Prostheses , 2012, Journal of prosthetics and orthotics : JPO.

[7]  Raoul M. Bongers,et al.  Task-Oriented Gaming for Transfer to Prosthesis Use , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  R.J. Vogelstein,et al.  Air-Guitar Hero: A real-time video game interface for training and evaluation of dexterous upper-extremity neuroprosthetic control algorithms , 2008, 2008 IEEE Biomedical Circuits and Systems Conference.

[9]  Xinjun Sheng,et al.  User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control , 2015, Journal of neural engineering.

[10]  Erik J. Scheme,et al.  Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions , 2011, IEEE Transactions on Biomedical Engineering.

[11]  Kathryn Ziegler-Graham,et al.  Estimating the prevalence of limb loss in the United States: 2005 to 2050. , 2008, Archives of physical medicine and rehabilitation.

[12]  Lennart Bodin,et al.  Intra- and inter-rater reliability of the assessment of capacity for myoelectric control. , 2006, Journal of rehabilitation medicine.

[13]  G.C. Burdea,et al.  Virtual reality-enhanced stroke rehabilitation , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Todd A Kuiken,et al.  Target Achievement Control Test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses. , 2011, Journal of rehabilitation research and development.

[15]  Todd A. Todd A. Kuiken Kuiken,et al.  A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis , 2016, IEEE Journal of Translational Engineering in Health and Medicine.

[16]  Levi J. Hargrove,et al.  A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control , 2008, Biomed. Signal Process. Control..

[17]  Øyvind Stavdahl,et al.  Upper Limb Prosthetic Outcome Measures (ULPOM): A Working Group and Their Findings , 2009 .

[18]  Max Ortiz-Catalan,et al.  Offline accuracy: A potentially misleading metric in myoelectric pattern recognition for prosthetic control , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[19]  K.B. Englehart,et al.  Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  T. Kuiken,et al.  A Comparison of Pattern Recognition Control and Direct Control of a Multiple Degree-of-Freedom Transradial Prosthesis , 2016, IEEE Journal of Translational Engineering in Health and Medicine.

[21]  Levi J. Hargrove,et al.  Real-Time and Offline Performance of Pattern Recognition Myoelectric Control Using a Generic Electrode Grid With Targeted Muscle Reinnervation Patients , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Shapour Jaberzadeh,et al.  Computer-based clinical instrumentation for processing and analysis of mechanically evoked electromyographic signals in the upper limb , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Dario Farina,et al.  Myoelectric Control of Artificial Limbs¿Is There a Need to Change Focus? [In the Spotlight] , 2012, IEEE Signal Process. Mag..

[24]  Nathan E. Bunderson,et al.  Quantification of Feature Space Changes With Experience During Electromyogram Pattern Recognition Control , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.