Lightweight soft neuroprosthetic hand

doi:10.21203/rs.3.rs-47149/v1

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Lightweight soft neuroprosthetic hand

https://doi.org/10.21203/rs.3.rs-47149/v1

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published 16 Aug, 2021

Read the published version in Nature Biomedical Engineering →

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posted 10 Aug, 2020

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Mainly composed of electrical motors and sophisticated mechanical components, existing neuroprosthetic hands1,2 are typically heavy (>400 g) and expensive (>USD 10,000), and they lack the compliance and tactile feedback of human hands. These limitations hamper neuroprosthetic hands’ innovation and broad utility for amputees3-5. Here we report the design, fabrication and applications of a lightweight (292 g) and potentially low-cost (component cost below USD 500) soft neuroprosthetic hand with simultaneous myoelectric control and tactile feedback. The soft neuroprosthetic hand consists of five soft fingers and a palm to give six active degrees of freedom under pneumatic actuation, four electromyography sensors that measure the surface electromyogram signals to control the hand to deliver four common grasp types, and five hydrogel-elastomer capacitive sensors on the fingertips that measure the touch pressure and elicit electrical stimulation on the skin of the residual limb. The soft finger is made of a fiber-reinforced elastomeric structure embedded with rigid segments to mimic the soft-joint/rigid-bone anatomy of the human finger. We use a set of standardized tests6 to compare the speed and dexterity of the soft neuroprosthetic hand and a conventional rigid neuroprosthetic hand7 on two transradial amputees. The soft neuroprosthetic hand gives overall superior performances to the rigid hand. We further demonstrate that one transradial amputee wearing the soft neuroprosthetic hand can regain the versatile hand functions with primitive touch sensation and real-time closed-loop control in daily activities such as handling tools, eating, shaking hands, petting animals, and recognizing touch pressure. This work not only represents a new paradigm for designing soft neuroprosthetic devices but also opens an avenue to widespread applications of lightweight, low-cost, and compliant hand replacements for amputees.

Physical sciences/Engineering/Biomedical engineering

Physical sciences/Engineering/Mechanical engineering

Yes there is potential Competing Interest. G.G., N.Z., H.X., S.L. X.Y.Z., and X.H.Z. are inventors of a patent application that covers the design and fabrication of the soft neuroprosthetic hand.

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Supplementary Information
FigureS1.png
Extended Data Fig. 1 | Simulation of the fine finite-element model and experimental results of the soft neuroprosthetic hand under different applied pneumatic pressures. A, Photographs of 1-DoF flexion of the index finger (as an exemplary motion of normal fingers) under pressures from 0 kPa to 120 kPa. B, Photographs of 2-DoF flexion of the thumb under pressures from 0 kPa to 80 kPa.
FigureS2.jpg
Extended Data Fig. 2 | Schematic illustration of two kinds of soft neuroprosthetic hands and their pneumatic control schemes. A, The pumps, valves, electronic boards and battery (the rechargeable lithium battery with the capacity of 800 mAh and the weight of 67 g by Geshi Inc., China) are contained in a small bag (length: 240 mm, width: 80 mm, height: 110 mm; weight: 444 g). B, The pumps, valves, electronic boards and battery are integrated in the palm and socket.
FigureS3.jpg
Extended Data Fig. 3 | Block diagram of the myoelectric control interface in the soft neuroprosthetic hand. The myoelectric control interface is designed for intuitive control of the soft neuroprosthetic hand. The myoelectric control is achieved by a customized onboard measurement and control system consisting of four-channel EMG sensors, control unit (including the signal processing unit for EMG decoding and the micro-controller for pneumatic actuation), pumps, valves, solid-state relays, and the power (battery and voltage regulators). The four-channel EMG sensors (embedded in the socket and mounted on the skin of residual forearm muscles) record the muscle activities of amputees, which is processed by the readout electronics with the amplification and Butterworth filtering (20-450 Hz). The signal processing unit receives the amplified and filtered signals from EMG sensors and classifies the signals into several discrete classes related to the grasp types of amputees’ intention. Through a universal asynchronous receiver/transmitter (UART) port, the classification results are sent to a micro-controller (Nano, Arduino Inc., Italy). The micro-controller employs the classified grasp types to control the pumps and valves through two solid-state relays, resulting in the intuitive control of the soft neuroprosthetic hand. The pins (D2-D7) and pins (D8-D13) connect the output pins of the micro-controller relating to the corresponding pins of pump and valve relays.
FigureS4.jpg
Extended Data Fig. 4 | Fabrication and assembly of the soft neuroprosthetic hand. A, Molding of inner elastomeric tubes (fingers, thumb, thumb-palm connection). B, Vacuum defoaming. C, Cured inner tubes. D, Winding of the finger/thumb. E, Carbon fiber-reinforced plastics (CFRP) laminates attachment. F, Sleeve wrapping. G, Making joint segments of the sleeve. H, Molding of the outer elastomeric tube of the finger/thumb. I, Limiting layer attachment of the thumb-palm connection. J, Winding of the connection pad. K, Molding of the outer tube of the thumb-palm connection. L, Cured finger/thumb, thumb-palm connection. M, Terminal connectors. N, The 3Dprinted palm skeleton. O, Assembly of the thumb with the thumb-palm connection. P, Assembly of the opposable thumb and fingers to the palm skeleton. Q, Integrating capacitive touch sensors on the fingertips. R, Installing the pumps, valves, electrical boards and battery in a waist bag. S, Installing the pumps, valves, control boards and battery in the palm and socket. T, Connecting the palm skeleton with the socket to form a soft neuroprosthetic hand.
FigureS5.jpg
Extended Data Fig. 5 | The four EMG-controlled grasp types (excluding rest) for the soft neuroprosthetic hand and the kinematic relationship in each finger to achieve the specific grasp type. A, the six most frequently used grasp types of human hands in daily activities42. B, The regrouped four grasp types in the soft neuroprosthetic hand based on the results in (A). C, Kinematic relations of different fingers in the four predefined grasp types in (B).
FigureS6.png
Extended Data Fig. 6 | Still images of a subject wearing the soft neuroprosthetic hand in the training process for intuitive control. A transradial amputee can quickly adapt to the soft neuroprosthetic hand and master its functionality by training. For the training algorithms, please refer to the Methods section. The red button is used for switching the power of the hand on/off and the silver button for switching to the training mode. As shown in the figure and supplementary Video 4, we can see that a subject can rapidly put on the soft neuroprosthetic hand within 3 seconds and master its function to intuitively control it after about 1 min. The training process is repeated for about 15 min.
FigureS7.jpg
Extended Data Fig. 7 | Descriptions of the standardized tests to evaluate the function of the soft neuroprosthetic hand. A, Testing tasks, including the Box and Blocks Test (BBT), all seven tasks in the Jebsen-Taylor Hand Function Test (i.e., J1-J7), and nine selected tasks in the Southampton Hand Assessment Procedure (i.e., S1-S9). B and C, Weights, dimensions and still images of the 17 objects used in the standardized tests.
FigureS8.jpg
Extended Data Fig. 8 | Evaluation of the soft neuroprosthetic hand on another transradial amputee with the standardized tests (the same as in Fig. 3A), including the Box and Blocks Test (e.g., counting the number of blocks per minute), all seven tasks in the Jebsen-Taylor Hand Function Test (e.g., J1-writing, J2-simulated page-turning, J3-lifting small common objects, J4-simulated feeding, J5-stacking checkers, J6-lifting large light objects, and J7-lifting large heavy objects), and nine abstract tasks of the Southampton Hand Assessment Procedure (e.g., grasping nine kinds of objects, such as S1-spherical light, S2-spherical heavy, S3-tripod light, S4-power light, S5-power heavy, S6-tip light, S7-tip heavy, S8-extension light, and S9-extension heavy). Values in panel represent the mean and the standard deviation (n = 3). A p value less than 0.05 (p < 0.05) is considered statistically significant.
FigureS9.jpg
Extended Data Fig. 9 | Demonstration of the advantage of the soft neuroprosthetic hand in handling fragile objects such as a strawberry, a piece of bread and a paper cup filled with water compared to the rigid i-Limb hand. A, the soft neuroprosthetic hand. B, the i-Limb hand. Each set of tests have been performed with three experimental trials and the presented images are from one set of experiments (Supplementary Video 7).
FigureS10.jpg
Extended Data Fig. 10 | Experimental setup and results of the subject wearing the soft neuroprosthetic hand to discriminate three cylinders with different diameters. In this test, we program the stimulation frequencies of the electrical pulses based on the different ranges of ΔC/C0 of the touch sensor on the middle finger (i.e., no stimulation when ΔC/C0≤0.1, 5 Hz when 0.1 <ΔC/C0 ≤ 0.3, 20 Hz when 0.3<ΔC/C0≤0.4, and 35 Hz when ΔC/C0>0.4). The statistical results demonstrate that the subject can correctly discriminate the grasped subject with an accuracy of 96.25% (77 successes in all 80 tests).
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Extended Data Table 1
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Extended Data Table 2
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Extended Data Table 3
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Extended Data Table 4
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Extended Data Table 5
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Extended Data Table 6
Supp.video1.83100video3446274qb5gpd.mp4
"Video1_Simulation and experiments of individual motion of five soft fingers
Supplvideo283100video3446275qb5gpd.mp4
Video2_Demonstration of independent control of six DoFs with one pump
Supplvideo3.83100video3446292qb5gpz.mp4
Video3_Demonstration of the durability of a soft finger
Supplvideo4.83100video3446293qb5gp7.mp4
Video4_Demonstration of fast wearing and training of a soft neuroprosthetic hand.mp4",
Supppvideo5.83100video3446294qb5gpk.mp4
Evaluation of the soft neuroprosthetic hand with the standardized tests
Supplvideo6.83100video3446295qb5gpd.mp4
Experimental results of the standardized tests by the same subject wearing a rigid neuroprosthetic hand
Supplvideo7.83100video3446296qb5gpg.mp4
Demonstration of the compliant advantage of the soft neuroprosthetic hand.
Supplvideo8.83100video3446297qb5gp9.mp4
Demonstration of the four EMG-controlled grasp types
Supplvideo9.83100video3446298qb5gpy.mp4
Demonstration of versatile hand functions in daily activities of the subject
Supplvideo10.83100video3446299qb5gp5.mp4
Demonstration of handling objects with different shapes and sizes
Supplvideo11.83100video3446300qb5gpc.mp4
Demonstration of holding heavy payloads
Supplvideo12.83100video3446301qb5gpf.mp4
Demonstration of the touch sensation of individual finger and multiple fingers
Supplvideo13.83100video3446302qb5gpf.mp4
Demonstration of the closed-loop control capability of the subject.
Suppvideo14.83100video3446303qb5gpf.mp4
Demonstration of the graded tactile feedback of the subject

PDF

Journal Publication

published 16 Aug, 2021

Read the published version in Nature Biomedical Engineering →

Version 1

posted 10 Aug, 2020

You are reading this latest preprint version

Lightweight soft neuroprosthetic hand

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