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![Prosthetic hand's differential mechanisms. a) Single‐dwell six‐bar differential mechanism generates two different motions for a thumb using one actuator. Reproduced with permission.[¹⁰⁸] Copyright 2018, IEEE. b) A cable and pulley system used to achieve three types of grasping, including lateral, precision, and power grasping gestures. Reproduced with permission.[¹¹⁰] Copyright 2013, IEEE. c) Rack and pinion mechanism connected to a three‐bar linkage to provide continuum differential mechanisms. Reproduced with permission.[¹¹¹] Copyright 2015, IEEE. d) A selectively lockable differential mechanism for controlling fingers’ angular position. Reproduced with permission.[¹¹²] Copyright 2021, IEEE.](publication/364939092/figure/fig10/AS:11431281179452029@1691189863318/Prosthetic-hands-differential-mechanisms-a-Single-dwell-six-bar-differential-mechanism.png)
Prosthetic hand's differential mechanisms. a) Single‐dwell six‐bar differential mechanism generates two different motions for a thumb using one actuator. Reproduced with permission.[¹⁰⁸] Copyright 2018, IEEE. b) A cable and pulley system used to achieve three types of grasping, including lateral, precision, and power grasping gestures. Reproduced with permission.[¹¹⁰] Copyright 2013, IEEE. c) Rack and pinion mechanism connected to a three‐bar linkage to provide continuum differential mechanisms. Reproduced with permission.[¹¹¹] Copyright 2015, IEEE. d) A selectively lockable differential mechanism for controlling fingers’ angular position. Reproduced with permission.[¹¹²] Copyright 2021, IEEE.
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There are millions of amputees worldwide, and amputation has significantly affected their lives. People with hand losses have difficulties performing the activities of daily living (ADLs). Prosthetic hands have been developed to perform the functions of a human hand by grasping various objects. However, mimicking the actual grasping function of a h...
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Many people struggle with mobility impairments due to lower limb amputations. To participate in society, they need to be able to walk on a wide variety of terrains, such as stairs, ramps, and level ground. Current lower limb powered prostheses require different control strategies for varying ambulation modes, and use data from mechanical sensors wi...
Citations
... The combined use of FFF and DIW techniques has gained widespread recognition to produce intricate 3D architectural sensors, renowned for their heightened sensitivity 21 . These sensors have found applications in a diverse range of fields, including pressure, vibration, and electromyography sensing 22,23 . Nonetheless, one prevailing concern that has arisen pertains to the adhesion between the sensor's structural components and electrodes in this method 24 . ...
We present here a 3D-printed pressure mapping mat, equipped with customizable architecture sensors, that offers a cost-effective and adaptable solution, overcoming the size constraints and sensing accuracy issues commonly associated with existing commercial pressure mats across various fields, such as healthcare and sports applications. Leveraging a pillar-origami structure, the demonstrated sensor offers multifaceted stiffness properties, effectively filtering skin deformations and enabling capacitive pressure sensing. Notably, the sensor’s detection range can be finely tuned, spanning from 70 to 2500 kPa, with a sensitivity range between 0.01 kPa ⁻¹ and 0.0002 kPa ⁻¹ , and an impressive response time of just 800 milliseconds. Furthermore, the inclusion of a modular sensor array enhances maintenance and allows for greater flexibility in shaping and enhancing the device’s resolution. This technology finds practical applications in wireless foot pressure mapping and sports protection pads, marking a significant milestone in the advancement of flexible and custom-shaped pressure sensor technology.
... In terms of high stability in manipulation, conventional grippers constructed by two or three rigid-linkage fingers in [28][29][30] present themselves as strong candidates when compared to soft grippers. Prosthetic hands with five rigid-phalanx fingers actuated by a cable-driven system [31][32][33] also presented advantages in heavy objects with high flexibility. Traditional robot hands, characterized by high-stiffness fingers, rigid phalanxes, and finite degrees of freedom (DoFs) at each joint, excel in controlling grasping poses and securely locking group objects with a consistent squeeze. ...
Efficiently manipulating objects in a group state poses an emerging challenge for soft robot hands. Overcoming this problem necessitates the development of hands with highly stable structures to bear heavy loads and highly compliant designs to universally adapt to various object geometries. This study introduces a novel platform for the development of robot hands aimed at manipulating multiple objects in each trial. In this setup, the objects come into soft contact with an elastic wire affixed to the finger skeletons. This combination results in a harmonious hybrid finger, inheriting both the soft, flexible properties of the wire and the robust stability provided by the finger skeleton. To facilitate this approach, a theoretical model was proposed to estimate the kinematics of manipulating multiple objects using wiring-based fingers. Based on this model, we designed a hybrid gripper comprising two wiring-based fingers for conducting experimental evaluations in manipulating four groups of samples: a pair of bevel gears, a pair of bevel gears plus a pneumatic connector, a pair of glue bottles, and a pair of silicon bottles. The experimental results demonstrated that our proposed gripper reached good performance with high success rates in durability tests conducted at various lifting velocities and high adaption with objects in soft-friendly ways. These findings hold promise for efficiently manipulating multiple complex objects in each trial without the need for complex control systems.
... 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. ...
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.
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.
In the field of intelligent prosthetics (IPs), the establishment of a natural interaction between prosthetic hands and amputees holds significant importance in restoring hand functionality, enhancing quality of life, and facilitating daily activities and social engagement. Prior investigations on surface electromyographic (sEMG) signals-controlled IPs have predominantly concentrated on gesture recognition, frequently neglecting the equally significant dimension of force level. This study proposes a control strategy integrating a multi-task learning (MTL) model to achieve synchronized recognition of gestures and force levels. The MTL model, incorporating shared convolutional blocks, self-attention, and multi-head attention layers, enhances prosthetic hand control for seamless user-device interaction. This study consistently showcases exceptional proficiency in recognizing gestures and force levels by conducting meticulous experimentation and validating the findings using datasets from diverse participants. Comparative assessments endorse the superiority of the MTL approach, particularly in real-time testing scenarios. The findings highlight the potential of this innovative myoelectric control strategy, empowering prosthetic users for prompt, precise, and intuitive responses, significantly augmenting their autonomy and quality of life.
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.
