Novel Grasping Mechanisms of 3D‐Printed Prosthetic Hands

Moeinnia, Hadi; Su, Haotian; Kim, Woo Soo

doi:10.1002/aisy.202200189

Cited by 12 publications

(4 citation statements)

References 116 publications

(136 reference statements)

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“…Furthermore, metamaterials have already been used to enable grasping motions for different objects through mechanical metamaterial sensing, improve user comfort with auxetic metamaterials, and develop artificial skin sensation pathways composed of tactile sensors. [101][102][103] These functionalities can be improved further using more optimized metamaterial-based sensors and facilitate fully-functional neural interfaces for prosthetics. In addition to NICU-specific applications, hearing aids, and assistive robotics, AI-based metamaterial design can also improve applications in personalized medicine.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

AI‐Based Metamaterial Design for Wearables

Yigci,

Ahmadpour,

Tasoglu

2023

Advanced Sensor Research

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Continuous monitoring of physiological parameters has remained an essential component of patient care. With an increased level of consciousness regarding personal health and wellbeing, the scope of physiological monitoring has extended beyond the hospital. From implanted rhythm devices to non‐contact video monitoring for critically ill patients and at‐home health monitors during Covid‐19, many applications have enabled continuous health monitorization. Wearable health sensors have allowed chronic patients as well as seemingly healthy individuals to track a wide range of physiological and pharmacological parameters including movement, heart rate, blood glucose, and sleep patterns using smart watches or textiles, bracelets, and other accessories. The use of metamaterials in wearable sensor design has offered unique control over electromagnetic, mechanical, acoustic, optical, or thermal properties of matter, enabling the development of highly sensitive, user‐friendly, and lightweight wearables. However, metamaterial design for wearables has relied heavily on manual design processes including human‐intuition‐based and bio‐inspired design. Artificial intelligence (AI)‐based metamaterial design can support faster exploration of design parameters, allow efficient analysis of large data‐sets, and reduce reliance on manual interventions, facilitating the development of optimal metamaterials for wearable health sensors. Here, AI‐based metamaterial design for wearable healthcare is reviewed. Current challenges and future directions are discussed.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

AI‐Based Metamaterial Design for Wearables

Yigci,

Ahmadpour,

Tasoglu

2023

Advanced Sensor Research

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“…[76] Therefore, a reliable system for the users to control the prosthetic hands is needed. 3D printed sEMG sensing techniques show promising performance for detecting grasping gestures in the prosthetic hand [30] as it has several advantages over conventional methods. [77] A precise and user-customized sEMG sensor could reduce the post-processes after the signal measurement.…”

Section: Emg Analysis and Applicationsmentioning

confidence: 99%

“…[29] By combining the 3D printed electrodes with EMG data acquisition systems, the EMG signals can be used in the advanced field, such as the control of prosthetics. [30] In this paper, we will explore the various EMG sensing systems developed by additive manufacturing techniques and their mechanisms in EMG detection. Furthermore, a comprehensive overview of the EMG sensing system analysis and applications will be discussed.…”

Section: Introductionmentioning

confidence: 99%

3D Printed Electromyography Sensing Systems

Moeinnia

Kim

2023

Advanced Sensor Research

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Electromyography (EMG) has been widely used in robotics and biomedical applications for sensing and diagnostic purposes. Because of the complex shape of human limbs and the uneven and flexible surface of the human skin, EMG sensing often faces the challenge of stable signal detection. As manufacturing technology advances, additive manufacturing has shown its potential to improve the existing EMG sensing system further. 3D printing technologies offer the advantage of custom fabrication to fit the designated locations of EMG detection. 3D printing also provides flexible and stretchable features, which allow for a comfortable user experience. This paper presents the recent development of novel 3D‐printed EMG sensing systems. The process of EMG signal detection is compared with the standard system. The corresponding applications with 3D‐printed sensing systems in different fields of study are also discussed. Finally, by reviewing the state‐of‐the‐art technology, the future of 3D printing in EMG sensing and the challenges facing the field are discussed.

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“…Many attempts to acquire a proportional signal to the prosthetic’s grasping force have been made using a variety of different techniques. Some examples include techniques using pressure chambers and pressure sensors [ 21 , 22 ], liquid metal sensors [ 23 ], piezo electrodes [ 24 ], magnets and hall effect sensors [ 25 ], sensor fabrics [ 26 , 27 ], camera and image processing [ 28 ], strain gauges and custom-designed loadcells [ 29 , 30 ], and other multi-sensor approaches [ 31 , 32 ]. All these examples have a common theme: the sensing element is embedded in the finger.…”

Section: Introductionmentioning

confidence: 99%

An Improved Approach for Grasp Force Sensing and Control of Upper Limb Soft Robotic Prosthetics

2023

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The following research proposes a closed loop force control system, which is implemented on a soft robotic prosthetic hand. The proposed system uses a force sensing approach that does not require any sensing elements to be embedded in the prosthetic’s fingers, therefore maintaining their monolithic structural integrity, and subsequently decreasing the cost and manufacturing complexity. This is achieved by embedding an aluminum test specimen with a full bridge strain gauge circuit directly inside the actuator’s housing rather than in the finger. The location of the test specimen is precisely at the location of the critical section of the bending moment on the actuator housing due to the tension in the driving tendon. Therefore, the resulting loadcell can acquire a signal proportional to the prosthetic’s grasping force. A PI controller is implemented and tested using this force sensing approach. The experiment design includes a flexible test object, which serves to visually demonstrate the force controller’s performance through the deformation that the test object experiences. Setpoints corresponding to “light”, “medium”, and “hard” grasps were tested with pinch, tripod, and full grasps and the results of these tests are documented in this manuscript. The developed controller was found to have an accuracy of ±2%. Additionally, the deformation of the test object increased proportionally with the given grasp force setpoint, with almost no deformation during the light grasp test, slight deformation during the medium grasp test, and relatively large deformation of the test object during the hard grasp test.

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Novel Grasping Mechanisms of 3D‐Printed Prosthetic Hands

Cited by 12 publications

References 116 publications

AI‐Based Metamaterial Design for Wearables

AI‐Based Metamaterial Design for Wearables

3D Printed Electromyography Sensing Systems

An Improved Approach for Grasp Force Sensing and Control of Upper Limb Soft Robotic Prosthetics

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