The future of upper extremity rehabilitation robotics: research and practice

Vu, Philip; Chestek, Cynthia A.; Nason, Samuel R.; Kung, Theodore A.; Kemp, Stephen W.P.; Cederna, Paul S.

doi:10.1002/mus.26860

Cited by 24 publications

(13 citation statements)

References 121 publications

(145 reference statements)

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“…In recent years there have been new developments in prosthetic limbs that aim to address these issues from both an engineering and surgical perspective. Technological advancements in biomechanics have allowed for prosthetic limbs to progress from body powered devices that provide singular control, to more innovative neural interfaces that provide intuitive control ( Vu et al, 2020a ). These new developments seek to address both mechanical issues related to prosthetics as well as sophistication of the motor control system.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, they are problematic from a mechanical and infectious standpoint with the use of surface electrodes in TMR and percutaneous wiring in RPNI ( Schofield et al, 2020 ; Vu et al, 2020b ). The clinical applications of these surgical techniques ( Vu et al, 2020a ; Herr et al, 2021 ; Karczewski et al, 2021 ) and electrode technology ( Ghafoor et al, 2017 ; Rijnbeek et al, 2018 ; Larson and Meng, 2020 ; Raspopovic et al, 2020 ) has been reviewed in substantial detail in previous studies.…”

Section: Introductionmentioning

confidence: 99%

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Clinical Basis for Creating an Osseointegrated Neural Interface

et al. 2022

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As technology continues to improve within the neuroprosthetic landscape, there has been a paradigm shift in the approach to amputation and surgical implementation of haptic neural prosthesis for limb restoration. The Osseointegrated Neural Interface (ONI) is a proposed solution involving the transposition of terminal nerves into the medullary canal of long bones. This design combines concepts of neuroma formation and prevention with osseointegration to provide a stable environment for conduction of neural signals for sophisticated prosthetic control. While this concept has previously been explored in animal models, it has yet to be explored in humans. This anatomic study used three upper limb and three lower limb cadavers to assess the clinical feasibility of creating an ONI in humans. Anatomical measurement of the major peripheral nerves- circumference, length, and depth- were performed as they are critical for electrode design and rerouting of the nerves into the long bones. CT imaging was used for morphologic bone evaluation and virtual implantation of two osseointegrated implants were performed to assess the amount of residual medullary space available for housing the neural interfacing hardware. Use of a small stem osseointegrated implant was found to reduce bone removal and provide more intramedullary space than a traditional implant; however, the higher the amputation site, the less medullary space was available regardless of implant type. Thus the stability of the endoprosthesis must be maximized while still maintaining enough residual space for the interface components. The results from this study provide an anatomic basis required for establishing a clinically applicable ONI in humans. They may serve as a guide for surgical implementation of an osseointegrated endoprosthesis with intramedullary electrodes for prosthetic control.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clinical Basis for Creating an Osseointegrated Neural Interface

et al. 2022

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“…Time-domain or frequency-domain features of the EMG signals can be used for training and classi cation, with varying classi cation accuracy (Fang et al, 2020). Unfortunately, using EMG sensors on a large set of individual muscles within the residual limb is challenging because crosstalk between sensors restricts the number of independent EMG signals that are actually available (Vu et al, 2020a). This problem restricts the degrees of freedom within the hand that may be controlled via EMG (Graimann and Dietl, 2013).…”

Section: Introductionmentioning

confidence: 99%

First-in-human demonstration of functional task performance using a sonomyographic prosthesis

Engdahl

King

Bashatah

et al. 2022

Preprint

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Ultrasound-based sensing of muscle deformation, known as sonomyography, has shown promise for accurately classifying the intended hand grasps of individuals with upper limb loss in offline settings. Building upon this previous work, we present the first-in-human demonstration of real-time prosthetic hand control using sonomyography to perform functional tasks. An individual with congenital bilateral limb absence was fitted with sockets containing a low-profile ultrasound transducer placed over forearm muscle tissue in the residual limbs. A classifier was trained using linear discriminant analysis to recognize ultrasound images of muscle contractions for three discrete hand configurations (rest, tripod grasp, index finger point) under a variety of arm positions designed to cover the reachable workspace. A prosthetic hand mounted to the socket was then controlled using this classifier. Using this real-time sonomyographic control, the participant was able to complete three functional tasks that required selecting different hand grasps in order to grasp and move one-inch wooden blocks over a broad range of arm positions. Additionally, these tests were successfully repeated without retraining the classifier across three hours of prosthesis use and following simulated donning and doffing of the socket. This study supports the feasibility of using sonomyography to control upper limb prostheses in real-world applications.

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“…Others explore algorithmic control systems (Wolf et al, 2020 ); prosthetic device designs (Naufel et al, 2020 ); and specific outcomes such as sensation or myoelectric control (Tyler, 2015 ; Geethanjali, 2016 ; Ghafoor et al, 2017 ; Sensinger and Dosen, 2020 ; Raspopovic et al, 2021 ). The former comprehensive reviews are outdated and do not cover newer developments such as the Agonist-antagonist Myoneural Interface (AMI) or Osseointegration (OI) (Navarro et al, 2005 ; Kung et al, 2013 ; Bates et al, 2020 ; Vu et al, 2020a ; Yildiz et al, 2020 ). Additionally, we see each of these reviews as important focal points highlighting gaps and advances within individual domains, without necessarily considering how these domains integrate with each other to form a cohesive solution or standard of practice.…”

Section: Introductionmentioning

confidence: 99%

The Need to Work Arm in Arm: Calling for Collaboration in Delivering Neuroprosthetic Limb Replacements

2021

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Over the last few decades there has been a push to enhance the use of advanced prosthetics within the fields of biomedical engineering, neuroscience, and surgery. Through the development of peripheral neural interfaces and invasive electrodes, an individual's own nervous system can be used to control a prosthesis. With novel improvements in neural recording and signal decoding, this intimate communication has paved the way for bidirectional and intuitive control of prostheses. While various collaborations between engineers and surgeons have led to considerable success with motor control and pain management, it has been significantly more challenging to restore sensation. Many of the existing peripheral neural interfaces have demonstrated success in one of these modalities; however, none are currently able to fully restore limb function. Though this is in part due to the complexity of the human somatosensory system and stability of bioelectronics, the fragmentary and as-yet uncoordinated nature of the neuroprosthetic industry further complicates this advancement. In this review, we provide a comprehensive overview of the current field of neuroprosthetics and explore potential strategies to address its unique challenges. These include exploration of electrodes, surgical techniques, control methods, and prosthetic technology. Additionally, we propose a new approach to optimizing prosthetic limb function and facilitating clinical application by capitalizing on available resources. It is incumbent upon academia and industry to encourage collaboration and utilization of different peripheral neural interfaces in combination with each other to create versatile limbs that not only improve function but quality of life. Despite the rapidly evolving technology, if the field continues to work in divided “silos,” we will delay achieving the critical, valuable outcome: creating a prosthetic limb that is right for the patient and positively affects their life.

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The future of upper extremity rehabilitation robotics: research and practice

Cited by 24 publications

References 121 publications

Clinical Basis for Creating an Osseointegrated Neural Interface

Clinical Basis for Creating an Osseointegrated Neural Interface

First-in-human demonstration of functional task performance using a sonomyographic prosthesis

The Need to Work Arm in Arm: Calling for Collaboration in Delivering Neuroprosthetic Limb Replacements

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