Review



prosthesis control algorithms  (BioMimetic Therapeutics)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    BioMimetic Therapeutics prosthesis control algorithms
    Two approaches to collecting training data for supervised machine-learning algorithms for myoelectric prostheses. A) With mimic training, the user watches a <t>prosthesis</t> move through pre-programmed movements and attempts to mimic the movement of the prosthesis with their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the pre-programmed kinematics of the prosthesis. B) With mirror training, the user performs bilaterally mirrored movements, such that the motion of their intact contralateral hand mirrors that of their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the mirrored kinematics of the contralateral limb (determined by motion capture).
    Prosthesis Control Algorithms, supplied by BioMimetic Therapeutics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/prosthesis+control+algorithms/pmc11197051-236-5-12?v=BioMimetic+Therapeutics
    Average 90 stars, based on 1 article reviews
    prosthesis control algorithms - by Bioz Stars, 2026-07
    90/100 stars

    Images

    1) Product Images from "Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms"

    Article Title: Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms

    Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

    doi: 10.1109/TNSRE.2024.3400729

    Two approaches to collecting training data for supervised machine-learning algorithms for myoelectric prostheses. A) With mimic training, the user watches a prosthesis move through pre-programmed movements and attempts to mimic the movement of the prosthesis with their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the pre-programmed kinematics of the prosthesis. B) With mirror training, the user performs bilaterally mirrored movements, such that the motion of their intact contralateral hand mirrors that of their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the mirrored kinematics of the contralateral limb (determined by motion capture).
    Figure Legend Snippet: Two approaches to collecting training data for supervised machine-learning algorithms for myoelectric prostheses. A) With mimic training, the user watches a prosthesis move through pre-programmed movements and attempts to mimic the movement of the prosthesis with their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the pre-programmed kinematics of the prosthesis. B) With mirror training, the user performs bilaterally mirrored movements, such that the motion of their intact contralateral hand mirrors that of their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the mirrored kinematics of the contralateral limb (determined by motion capture).

    Techniques Used: Muscles

    Performance of amputee participants completing a clothespin relocation task (CRT) with a myoelectric prosthesis trained with mirror or mimic training. A) Participants complete the CRT faster with mirror training. B) Success rate was similar for mirror and mimic training (out of 39 attempts for mimic training and out of 42 attempts for mirror training). C) The cognitive load associated with mirror and mimic training is also similar both during training and online use. D) There were also no significant differences between embodiment scores associated with mirror and mimic training. Data from four unilateral transradial amputees for A and B. Data from three unilateral transradial amputees for C & D. Violin plots show the kernel density estimation. Black horizontal lines denote the mean, white circles denote the median, and vertical gray lines denote the interquartile range. * p < 0.05, Wilcoxon rank-sum test.
    Figure Legend Snippet: Performance of amputee participants completing a clothespin relocation task (CRT) with a myoelectric prosthesis trained with mirror or mimic training. A) Participants complete the CRT faster with mirror training. B) Success rate was similar for mirror and mimic training (out of 39 attempts for mimic training and out of 42 attempts for mirror training). C) The cognitive load associated with mirror and mimic training is also similar both during training and online use. D) There were also no significant differences between embodiment scores associated with mirror and mimic training. Data from four unilateral transradial amputees for A and B. Data from three unilateral transradial amputees for C & D. Violin plots show the kernel density estimation. Black horizontal lines denote the mean, white circles denote the median, and vertical gray lines denote the interquartile range. * p < 0.05, Wilcoxon rank-sum test.

    Techniques Used:



    Similar Products

    prosthesis control algorithms  (BioMimetic Therapeutics)
    90
    BioMimetic Therapeutics prosthesis control algorithms
    Two approaches to collecting training data for supervised machine-learning algorithms for myoelectric prostheses. A) With mimic training, the user watches a <t>prosthesis</t> move through pre-programmed movements and attempts to mimic the movement of the prosthesis with their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the pre-programmed kinematics of the prosthesis. B) With mirror training, the user performs bilaterally mirrored movements, such that the motion of their intact contralateral hand mirrors that of their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the mirrored kinematics of the contralateral limb (determined by motion capture).
    Prosthesis Control Algorithms, supplied by BioMimetic Therapeutics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/prosthesis+control+algorithms/pmc11197051-236-5-12?v=BioMimetic+Therapeutics
    Average 90 stars, based on 1 article reviews
    prosthesis control algorithms - by Bioz Stars, 2026-07
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Two approaches to collecting training data for supervised machine-learning algorithms for myoelectric prostheses. A) With mimic training, the user watches a prosthesis move through pre-programmed movements and attempts to mimic the movement of the prosthesis with their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the pre-programmed kinematics of the prosthesis. B) With mirror training, the user performs bilaterally mirrored movements, such that the motion of their intact contralateral hand mirrors that of their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the mirrored kinematics of the contralateral limb (determined by motion capture).

    Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

    Article Title: Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms

    doi: 10.1109/TNSRE.2024.3400729

    Figure Lengend Snippet: Two approaches to collecting training data for supervised machine-learning algorithms for myoelectric prostheses. A) With mimic training, the user watches a prosthesis move through pre-programmed movements and attempts to mimic the movement of the prosthesis with their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the pre-programmed kinematics of the prosthesis. B) With mirror training, the user performs bilaterally mirrored movements, such that the motion of their intact contralateral hand mirrors that of their phantom limb. The resulting training dataset consists of EMG from the residual muscles of the phantom hand and the mirrored kinematics of the contralateral limb (determined by motion capture).

    Article Snippet: In the present study, the prosthesis control algorithms were trained in a biomimetic way, such that the users intended kinematics were consistent with the prosthesis action.

    Techniques: Muscles

    Performance of amputee participants completing a clothespin relocation task (CRT) with a myoelectric prosthesis trained with mirror or mimic training. A) Participants complete the CRT faster with mirror training. B) Success rate was similar for mirror and mimic training (out of 39 attempts for mimic training and out of 42 attempts for mirror training). C) The cognitive load associated with mirror and mimic training is also similar both during training and online use. D) There were also no significant differences between embodiment scores associated with mirror and mimic training. Data from four unilateral transradial amputees for A and B. Data from three unilateral transradial amputees for C & D. Violin plots show the kernel density estimation. Black horizontal lines denote the mean, white circles denote the median, and vertical gray lines denote the interquartile range. * p < 0.05, Wilcoxon rank-sum test.

    Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

    Article Title: Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms

    doi: 10.1109/TNSRE.2024.3400729

    Figure Lengend Snippet: Performance of amputee participants completing a clothespin relocation task (CRT) with a myoelectric prosthesis trained with mirror or mimic training. A) Participants complete the CRT faster with mirror training. B) Success rate was similar for mirror and mimic training (out of 39 attempts for mimic training and out of 42 attempts for mirror training). C) The cognitive load associated with mirror and mimic training is also similar both during training and online use. D) There were also no significant differences between embodiment scores associated with mirror and mimic training. Data from four unilateral transradial amputees for A and B. Data from three unilateral transradial amputees for C & D. Violin plots show the kernel density estimation. Black horizontal lines denote the mean, white circles denote the median, and vertical gray lines denote the interquartile range. * p < 0.05, Wilcoxon rank-sum test.

    Article Snippet: In the present study, the prosthesis control algorithms were trained in a biomimetic way, such that the users intended kinematics were consistent with the prosthesis action.

    Techniques: