Development and Evaluation of an Adaptive Multi-DOF Finger with Mechanical-Sensor Integrated for Prosthetic Hand

To realize the adaptive grasping of objects with diverse shapes and to capture the joint angles of the finger, a multi degree of freedom (DOF) adaptive finger for prosthetic hand is proposed in this paper. The fingers are designed with three joints. The maximum rotation angle of the finger joints is 90°. The angle at which the finger joints bend can be captured. Firstly, the prototype design, forward kinematics and force analysis of phalanges are described in detail. In order to achieve an adaptive motion pattern similar to that of the human hand, this paper investigates the optimization of the torsion spring stiffness coefficient so that the metacarpophalangeal (MCP) joints, proximal interphalangeal (PIP) joints, and distal interphalangeal (DIP) joints of the bionic finger meet a motion ratio of approximately 3:3:1. Then, in order to realize the joint angle measurement in the process of grasping an object, the mechanical-sensor integrated finger joint is designed, and the composition, angle measurement principle and measurement circuit are introduced in detail. Finally, joint angle measurement, movement law evaluation and object grasping experiments are performed to verify the validity of the designed finger. The experimental results show that the root-mean-square (RMS) of the DIP, PIP and MCP angle measurement errors are 0.36°, 0.59° and 0.32°, respectively. The designed finger is able to grasp objects with different shapes stably.

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Development of an Anthropomorphic Prosthetic Hand with Underactuated Mechanism

Applied Sciences ◽

10.3390/app10124384 ◽

2020 ◽

Vol 10 (12) ◽

pp. 4384

Author(s):

Wooseok Ryu ◽

Youngjin Choi ◽

Yong Je Choi ◽

Yeong Geol Lee ◽

Sungon Lee

Keyword(s):

Degrees Of Freedom ◽

Power Transmission ◽

Human Hand ◽

Prosthetic Hand ◽

Underactuated Mechanism ◽

Hand Size ◽

Distal Interphalangeal ◽

Compact Design ◽

Constant Velocity Joints ◽

Self Adaptive

An anthropomorphic prosthetic hand for wrist or forearm amputees is developed herein. The prosthetic hand was designed with an underactuated mechanism, which makes self-adaptive grasping possible, as well as natural motions such as flexion and extension. The finger and thumb modules were designed with four degrees of freedom by motions of the distal interphalangeal, proximal interphalangeal, and metacarpophalangeal joints. In this research, we pursued several novel trials in prosthetic hand design. By using two four-bar linkages composed of a combination of linkages and gears for coupling joints at each finger, it was possible to make a compact design, and the linkage has advantages such as accurate positioning, uniform power transmission, and high payload. Also, by using constant-velocity joints, torque is transferred to finger modules regardless of adduction/abduction motions. In addition, adduction/abduction and self-adaptive grasping motions are passively realized using torsional springs. The developed prosthetic hand was fabricated with a weight of 475 g and a human hand size of 175 mm. Experiments with diverse objects showed its good functionality.

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Human Hand Anatomy-Based Prosthetic Hand

Sensors ◽

10.3390/s21010137 ◽

2020 ◽

Vol 21 (1) ◽

pp. 137

Author(s):

Larisa Dunai ◽

Martin Novak ◽

Carmen García Espert

Keyword(s):

3D Printing ◽

Degrees Of Freedom ◽

Use Of Force ◽

Hand Movements ◽

Human Hand ◽

Prosthetic Hand ◽

Object Surface ◽

Hand Phalanges ◽

High Level

The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.

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Effect of nonlinear distortions in frequency doublers on angle-measurement errors

Measurement Techniques ◽

10.1007/bf00825312 ◽

1977 ◽

Vol 20 (3) ◽

pp. 366-368

Author(s):

B. N. Ivanov ◽

Yu. S. Ivanov

Keyword(s):

Measurement Errors ◽

Angle Measurement ◽

Frequency Doublers ◽

Nonlinear Distortions

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Modifying Kinect placement to improve upper limb joint angle measurement accuracy

Journal of Hand Therapy ◽

10.1016/j.jht.2016.06.010 ◽

2016 ◽

Vol 29 (4) ◽

pp. 465-473 ◽

Cited By ~ 5

Author(s):

Na Jin Seo ◽

Mojtaba F. Fathi ◽

Pilwon Hur ◽

Vincent Crocher

Keyword(s):

Upper Limb ◽

Measurement Accuracy ◽

Joint Angle ◽

Angle Measurement

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Gravity-Based Characterization of Three-Axis Accelerometers in Terms of Intrinsic Accelerometer Parameters

Journal of Research of the National Institute of Standards and Technology ◽

10.6028/jres.122.032 ◽

2017 ◽

Vol 122 ◽

Cited By ~ 4

Author(s):

Jon Geist ◽

Muhammad Yaqub Afridi ◽

Craig D. McGray ◽

Michael Gaitan

Keyword(s):

Gravitational Field ◽

Coordinate System ◽

Measurement Errors ◽

Angle Measurement ◽

Sensitivity Matrix ◽

Cross Sensitivity ◽

Measurement Capability ◽

Intrinsic Parameters ◽

The Cross ◽

Alignment Errors

Cross-sensitivity matrices are used to translate the response of three-axis accelerometers into components of acceleration along the axes of a specified coordinate system. For inertial three-axis accelerometers, this coordinate system is often defined by the axes of a gimbal-based instrument that exposes the device to different acceleration inputs as the gimbal is rotated in the local gravitational field. Therefore, the cross-sensitivity matrix for a given three-axis accelerometer is not unique. Instead, it depends upon the orientation of the device when mounted on the gimbal. We define nine intrinsic parameters of three-axis accelerometers and describe how to measure them directly and how to calculate them from independently determined cross-sensitivity matrices. We propose that comparisons of the intrinsic parameters of three axis accelerometers that were calculated from independently determined cross-sensitivity matrices can be useful for comparisons of the cross-sensitivity-matrix measurement capability of different institutions because the intrinsic parameters will separate the accelerator-gimbal alignment differences among the participating institutions from the purely gimbal-related differences, such as gimbal-axis orthogonality errors, z-axis gravitational-field alignment errors, and angle-setting or angle-measurement errors.

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Kalman-Filtering-Based Joint Angle Measurement with Wireless Wearable Sensor System for Simplified Gait Analysis

IEICE Transactions on Information and Systems ◽

10.1587/transinf.e94.d.1716 ◽

2011 ◽

Vol E94-D (8) ◽

pp. 1716-1720 ◽

Cited By ~ 15

Author(s):

Hiroki SAITO ◽

Takashi WATANABE

Keyword(s):

Gait Analysis ◽

Kalman Filtering ◽

Joint Angle ◽

Angle Measurement ◽

Sensor System ◽

Wearable Sensor

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Design and multichannel electromyography system-based neural network control of a low-cost myoelectric prosthesis hand

Mechanical Sciences ◽

10.5194/ms-12-69-2021 ◽

2021 ◽

Vol 12 (1) ◽

pp. 69-83

Author(s):

Saygin Siddiq Ahmed ◽

Ahmed R. J. Almusawi ◽

Bülent Yilmaz ◽

Nuran Dogru

Keyword(s):

Neural Network ◽

Control Method ◽

Low Cost ◽

Hand Movement ◽

Cost Effective ◽

Network Control ◽

Human Hand ◽

Prosthetic Hand ◽

Myoelectric Prosthesis ◽

System File

Abstract. This study introduces a new control method for electromyography (EMG) in a prosthetic hand application with a practical design of the whole system. The hand is controlled by a motor (which regulates a significant part of the hand movement) and a microcontroller board, which is responsible for receiving and analyzing signals acquired by a Myoware muscle device. The Myoware device accepts muscle signals and sends them to the controller. The controller interprets the received signals based on the designed artificial neural network. In this design, the muscle signals are read and saved in a MATLAB system file. After neural network program processing by MATLAB, they are then applied online to the prosthetic hand. The obtained signal, i.e., electromyogram, is programmed to control the motion of the prosthetic hand with similar behavior to a real human hand. The designed system is tested on seven individuals at Gaziantep University. Due to the sufficient signal of the Mayo armband compared to Myoware sensors, Mayo armband muscle is applied in the proposed system. The discussed results have been shown to be satisfactory in the final proposed system. This system was a feasible, useful, and cost-effective solution for the handless or amputated individuals. They have used the system in their day-to-day activities that allowed them to move freely, easily, and comfortably.

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