Research ArticlePROSTHETICS

Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users

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Science Robotics  20 Jun 2018:
Vol. 3, Issue 19, eaat3630
DOI: 10.1126/scirobotics.aat3630
  • Fig. 1 Results from clothespin relocation test.

    The time required to transfer three pins from a horizontal to a vertical bar is shown. Performance of LR-based control on two different days without retraining (LR1 and LR2) and two conventional control strategies, CC and SC, is shown separately for each participant (one panel each). The three arm positions—down (D), frontal (F), and up (U)—are shown separately on the right and pooled on the left. The proposed regression-based control outperformed CC for all participants and performed similarly to or better than SC. It was robust against session transfer and less affected by a change of arm position than CC. Asterisks indicate significant differences; plus signs indicate outliers; error bars indicate 25th and 75th percentiles.

  • Fig. 2 Results from box-and-blocks test.

    The reported outcome measure is the average time per block. Performances of LR-based control on two different days without retraining (LR1 and LR2) and two conventional control strategies, CC and SC, are shown separately for each participant (one panel each). LR was robust across sessions, and its performance was similar to or better than the conventional control systems in the second day, although only one DOF was needed. n.a., not applicable.

  • Fig. 3 Experimental setup.

    (A) Setup for PC-supported user training with regression algorithm. During training, the prosthesis was connected to a PC to record EMG signals while the user received instructions via the user screen. An initial regression model was applied for virtual control tasks for training the user in a game-like software framework and optional co-adaptive learning between the user and the algorithm. (B) Computer game for training the user in conducting noncombined and combined motions of different activation levels with the regression-based control. Evaluation in the clothespin relocation test, executed in arm positions down (C), frontal (D), and up (E). (F) Evaluation in the box-and-blocks test.

  • Fig. 4 Schematic explanation of the evaluated control techniques.

    (A) Regression-based control uses eight channels that are mapped into two DOFs that can be activated proportionally and simultaneously. (B) CC is based on two channels. For switching the active DOF, a short and strong co-activation is required. (C) In SC, which also uses two channels, the rate of increase in EMG envelope determines the activated DOF.

  • Table 1 Number of dropped pins.

    Dropped pins for all control techniques and arm positions. Each technique/position combination involved 10 runs or 30 transferred pins per participant. That is, the number of dropped pins was relatively low, with almost no significant differences between techniques and conditions. D, down; F, frontal; U, up. Dash entries indicate no drops.

    MethodLR1LR2CCSC
    Arm positionDFUDFUDFUDFU
    Participant 1111
    Participant 22622
    Participant 32321522251
    Participant 411
    Participant 5512411512
  • Table 2 Participant characteristics.

    Dash entries indicate zero.

    ParticipantType and affected sideGenderAgeYears after
    amputation
    Residual
    limb
    Own
    prosthesis
    Frequency
    of use
    Experience
    advanced control
    1Amputation, leftM5635Medium1 DOFDailyMedium
    2Congenital, leftF37Short2 DOF, SCDailyLittle
    3Congenital, leftF24LongNone
    4Bilateral amputation,
    left side tested
    M587Medium2 DOF, SC/CCDailyNone
    5Congenital, rightM44Long1 DOFVery seldomLittle

Supplementary Materials

  • Supplementary Materials

    Supplementary Material for:

    Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users

    Janne M. Hahne,* Meike A. Schweisfurth, Mario Koppe, Dario Farina

    *Corresponding author. Email: janne.hahne{at}bccn.uni-goettingen.de

    Published 20 June 2018, Sci. Robot. 3, eaat3630 (2018)
    DOI: 10.1126/scirobotics.aat3630

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    Other Supplementary Material for this manuscript includes the following:

    • Movie S1 (.avi format). Regression-based control of myoelectric hand prosthesis in comparison with conventional control.

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