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Biomimetic sensory feedback through peripheral nerve stimulation improves dexterous use of a bionic hand

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Science Robotics  24 Jul 2019:
Vol. 4, Issue 32, eaax2352
DOI: 10.1126/scirobotics.aax2352
  • Fig. 1 Participant and sensorized bionic hand.

    A transradial amputee (A) had two total USEAs (B) implanted, one each, into the residual median and ulnar nerves of the arm. Activation of contact sensors on the DEKA LUKE arm (C) triggered stimulation of individual USEA electrodes or groups of USEA electrodes so that the amputee felt a sensation on his phantom hand at the corresponding location. For example, when contact was made with the index fingertip sensor, current was delivered through USEA electrodes with projection fields on the phantom index fingertip. Thus, when the prosthetic index fingertip made contact with an object, the participant experienced a sensation on the index fingertip.

  • Fig. 2 Centroids of the projected fields for cutaneous percepts (circles) and location of proprioceptive percepts (black arrows) evoked by stimulation through individual USEA electrodes in the residual median or ulnar nerves.

    A total of 119 sensory percepts were evoked (72% from median nerve) 2 weeks after the implantation surgery. The quality of the evoked percepts varied across electrodes: 37% vibration (red), 29% pressure (green), 16% pain (blue), 12% tightening (orange), 3% movement (arrows), 3% tapping (yellow), and 1% buzzing (black). A map of the complete projected fields can be found in fig. S3.

  • Fig. 3 Sensory feedback improves object manipulation.

    The participant’s task was to move a fragile object that breaks if the grip force is too strong. With sensory feedback, the participant moved the object more often without breaking it and did so more rapidly (basic). With divided attention (distr.), the feedback-induced boost in performance was maintained, but only the effect on duration remained statistically significant. *P < 0.05, n = 80 for both basic and distr. cases. Data show means ± SEM.

  • Fig. 4 Sensory feedback improves grip precision.

    (A) Forces (means ± SD) generated by the participant when grasping a load cell while viewing one of eight different virtual objects. Sensory feedback improved grip precision, as evidenced by less variable grip force on six of eight objects. Without sensory feedback, the participant erred on the side of caution and underestimated desired grip force for fragile objects (bread, eggs, and open water bottle). (B) Coefficient of variation (means ± SEM) of grip force across all eight objects. Sensory feedback significantly reduced the coefficient of variation (i.e., the ratio of grip precision to grip force). Asterisk (*) indicates different means (P < 0.05), and sharp (#) indicates different SDs (P < 0.05); n = 40 for each object.

  • Fig. 5 Biomimetic sensory feedback improves performance on object discrimination tasks.

    (A) Example force (top; blue) and change in force (top; red) when the participant actively manipulated a soft foam block. Note the repetitive waxes and wanes in force (e.g., at ~2 s), associated with the participant’s active exploration of the object. Traditional linear encoding tracks force only (bottom; light blue), whereas the first-order biomimetic encoding incorporates the first derivative of force (bottom; light red) and second-order biomimetic mimics the aggregate responses of tactile nerve fibers (bottom; light green). Linear algorithms were scaled (doubled) such that peak stimulation amplitude and frequency were matched to the biomimetic algorithms; arrows highlight the time when peak stimulation occurs for the different algorithms. (B) Biomimetic sensory feedback improved response time relative to its nonbiomimetic counterpart in size and compliance (comp.) discrimination tasks. Performance across experiments varied because of changes in stimulation parameters, but biomimetic stimulation consistently outperformed nonbiomimetic stimulation. *P < 0.05, n = 32 for binary versus biomimetic 1, n = 48 for linear versus biomimetic 1, and n = 32 for binary versus biomimetic 2. Data show means ± SEM.

  • Fig. 6 Sensory feedback supports ADLs.

    The participant performed several one- and two-handed ADLs while using the sensorized prosthesis, including moving an egg (A), picking grapes (B), texting on his phone (C), and shaking hands with his wife (D).

  • Table 1 Stimulation parameters used for each task.

    TaskSensory encoding algorithm(s)USEA electrodesAmplitude
    (μA)
    Frequency
    (Hz)
    Duration
    (μs)
    Fragile object (first set)Traditional linear2, 5, 6, 9, 10, 12, 15, 16, 20, 2580–10010–100200
    Fragile object (second set)Traditional linear2, 5, 6, 9, 10, 12, 15, 16, 20, 23, 2570–10010–100200
    GRIPBiomimetic 15, 6, 9, 10, 12, 15, 16, 20, 2380–9510–200320
    Size discriminationBiomimetic 12, 5, 6, 9, 10, 12, 15, 16, 20, 2380–9510–200200
    Size discriminationBinary5, 6, 9, 10, 12, 15, 16, 20, 25, 26100100320
    Compliance discrimination (first set)Biomimetic 1 versus traditional linear2, 5, 6, 9, 10, 12, 15, 16, 20, 2390–10010–200200
    Compliance discrimination (second
    set)
    Biomimetic 1 versus traditional linear2, 5, 6, 9, 10, 12, 15, 16, 20, 2380–9510–200320
    Compliance discrimination (first set)Biomimetic 1 versus scaled traditional
    linear
    2, 5, 6, 9, 10, 12, 15, 16, 20, 2380–9510–200320
    Compliance discrimination (second set)Biomimetic 1 versus scaled traditional
    linear
    5, 6, 9, 10, 12, 15, 16, 20, 23, 25, 2680–10010–200320
    Compliance discriminationBiomimetic 2 versus scaled traditional
    linear
    5, 6, 9, 10, 12, 15, 16, 20, 23, 25, 267010–400320
    ADL (first set)Traditional linear23, 26, 33, 41, 42, 47, 6370–10010–100200
    ADL (second set)Traditional linear23, 26, 27, 33, 3460–100100200
    ADL (third set)Traditional linear9, 10, 2080–10010–100200
  • Table 2 Sensory encoding algorithms.

    Ft, frequency at time t; At, amplitude at time t; ct, normalized contact value at time t; vt, velocity at time t; at, acceleration at time t; min, minimum value; max, maximum value. Note that for all algorithms, sensory feedback is off and no stimulation occurs when ct = 0.

    Sensory encoding algorithm(s)Analytic formulation
    BinaryFt = Fmin
    At = Amin
    Traditional linearFt = ct(FmaxFmin) + Fmin
    At = ct(AmaxAmin) + Amin
    Scaled traditional linearFt = 2ct(FmaxFmin) + Fmin
    At = 2ct(AmaxAmin) + Amin
    Biomimetic 1Ft={ct(FmaxFmin)+Fmin,vt<0(5vt+ct)*(FmaxFmin)+Fmin,vt0
    At={ct(AmaxAmin)+Amin,vt<0(5vt+ct)*(AmaxAmin)+Amin,vt0
    Biomimetic 2Ft = 186ct − 185ct−1 + 1559vt − 360vt−1 − 109vt−2 + 364at + 170at−1 − 3
    At = Amin
  • Table 3 Motor control specifications.

    DOFRangePrecisionAngle at rest
    Thumb adduction/abduction0°–75°0.08° per bit22.5°
    Thumb reposition/opposition50°–100°0.10° per bit80°
    Index extend/flex0°–90°0.09° per bit27°
    D3, D4, and D5 extend/flex0°–90°0.09° per bit27°
    Wrist supinate/pronate−120°–175°0.29° per bit
    Wrist extend/flex−55°–55°0.11° per bit
  • Table 4 Sensor specifications.

    SensorRangePrecision
    Thumb adduction/abduction0°–75°2.08 × 10−4° per bit
    Thumb reposition/opposition0°–100°1.56 × 10−4° per bit
    Index extend/flex0°–90°1.74 × 10−4° per bit
    MRP extend/flex0°–90°1.74 × 10−4° per bit
    Wrist supinate/pronate−120°–175°5.30 × 10−5° per bit
    Wrist extend/flex−55°–55°1.42 × 10−4° per bit
    All contact sensors0 to 25.6 N0.1 N per bit

Supplementary Materials

  • robotics.sciencemag.org/cgi/content/full/4/32/eaax2352/DC1

    Stability of the USEA

    Decoding intended movements with a modified Kalman filter

    Surgical procedure

    Fig. S1. Modified Box and Blocks test.

    Fig. S2. Prosthesis efficiency and profitability task.

    Fig. S3. Projected fields of electrically evoked sensations.

    Fig. S4. Fragile object test.

    Fig. S5. Grasping Relative Index of Performance task.

    Fig. S6. Size discrimination task.

    Fig. S7. Compliance discrimination task.

    Fig. S8. Stability of USEA-evoked sensations.

    References (6066)

  • Supplementary Materials

    The PDF file includes:

    • Stability of the USEA
    • Decoding intended movements with a modified Kalman filter
    • Surgical Procedure
    • Fig. S1. Modified Box and Blocks test.
    • Fig. S2. Prosthesis efficiency and profitability task.
    • Fig. S3. Projected fields of electrically evoked sensations.
    • Fig. S4. Fragile object test.
    • Fig. S5. Grasping Relative Index of Performance task.
    • Fig. S6. Size discrimination task.
    • Fig. S7. Compliance discrimination task.
    • Fig. S8. Stability of USEA-evoked sensations.
    • References (6066)

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