Mechatronic fingernail with static and dynamic force sensing

Kõiva R, Schwank T, Walck G, Haschke R, Ritter H. Mechatronic fingernail with static and dynamic force sensing. In: International Conference on Intelligent Robots and Systems (IROS). 2018.Our fingernails help us to accomplish a variety of manual tasks, but surprisingly only a few robotic hands are equipped with nails. In this paper, we present a sensorized fingernail for mechatronic hands that can capture static and dynamic interaction forces with the nail. Over the course of several iterations, we have developed a very compact working prototype that fits together with our previously developed multi-cell tactile fingertip sensor into the cavity of the distal phalange of a human-sized robotic hand. We present the construction details, list the key performance characteristics and demonstrate an example application of finding the end of an adhesive tape roll using the signals captured by the sensors integrated in the nail. We conclude with a discussion about improvement ideas for future versions

A Biomimetic Fingerprint for Robotic Tactile Sensing

Tactile sensors have been developed since the early '70s and have greatly improved, but there are still no widely adopted solutions. Various technologies, such as capacitive, piezoelectric, piezoresistive, optical, and magnetic, are used in haptic sensing. However, most sensors are not mechanically robust for many applications and cannot cope well with curved or sizeable surfaces. Aiming to address this problem, we present a 3D-printed fingerprint pattern to enhance the body-borne vibration signal for dynamic tactile feedback. The 3D-printed fingerprint patterns were designed and tested for an RH8D Adult size Robot Hand. The patterns significantly increased the signal's power to over 11 times the baseline. A public haptic dataset including 52 objects of several materials was created using the best fingerprint pattern and material.Comment: 56th International Symposium on Robotics (ISR Europe) | September 26-27, 2023, Stuttgart, German

An Embedded, Multi-Modal Sensor System for Scalable Robotic and Prosthetic Hand Fingers

Grasping and manipulation with anthropomorphic robotic and prosthetic hands presents a scientific challenge regarding mechanical design, sensor system, and control. Apart from the mechanical design of such hands, embedding sensors needed for closed-loop control of grasping tasks remains a hard problem due to limited space and required high level of integration of different components. In this paper we present a scalable design model of artificial fingers, which combines mechanical design and embedded electronics with a sophisticated multi-modal sensor system consisting of sensors for sensing normal and shear force, distance, acceleration, temperature, and joint angles. The design is fully parametric, allowing automated scaling of the fingers to arbitrary dimensions in the human hand spectrum. To this end, the electronic parts are composed of interchangeable modules that facilitate the echanical scaling of the fingers and are fully enclosed by the mechanical parts of the finger. The resulting design model allows deriving freely scalable and multimodally sensorised fingers for robotic and prosthetic hands. Four physical demonstrators are assembled and tested to evaluate the approach

An Embedded, Multi-Modal Sensor System for Scalable Robotic and Prosthetic Hand Fingers

Grasping and manipulation with anthropomorphic robotic and prosthetic hands presents a scientific challenge regarding mechanical design, sensor system, and control. Apart from the mechanical design of such hands, embedding sensors needed for closed-loop control of grasping tasks remains a hard problem due to limited space and required high level of integration of different components. In this paper we present a scalable design model of artificial fingers, which combines mechanical design and embedded electronics with a sophisticated multi-modal sensor system consisting of sensors for sensing normal and shear force, distance, acceleration, temperature, and joint angles. The design is fully parametric, allowing automated scaling of the fingers to arbitrary dimensions in the human hand spectrum. To this end, the electronic parts are composed of interchangeable modules that facilitate the echanical scaling of the fingers and are fully enclosed by the mechanical parts of the finger. The resulting design model allows deriving freely scalable and multimodally sensorised fingers for robotic and prosthetic hands. Four physical demonstrators are assembled and tested to evaluate the approach

Design and analysis of fingernail sensors for measurement of fingertip touch fouce and finger posture

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2002.Includes bibliographical references (leaves 142-148).A new type of wearable sensor for detecting fingertip touch force and finger posture is presented. Unlike traditional electronic gloves, in which sensors are embedded along the finger and on the fingerpads, this new device does not constrict finger motion and allows the fingers to directly contact the environment without obstructing the human's natural haptic senses. The fingertip touch force and finger posture are detected by measuring changes in the coloration of the fingernail; hence, the sensor is mounted on the fingernail and does not interfere with bending or touching actions. Specifically, the fingernail is instrumented with miniature light emitting diodes (LEDs) and photodetectors in order to measure changes in the reflection intensity when the fingertip is pressed against a surface or when the finger is bent. The changes in intensity are then used to determine changes in the blood volume under the fingernail, a technique termed "reflectance photoplethysmography." By arranging the LEDs and photodetectors in a spatial array, the two-dimensional pattern of blood volume can be measured and used to predict the touch force and posture. This thesis first underscores the role of the fingernail sensor as a means of indirectly detecting fingertip touch force and finger posture by measuring the internal state of the finger. Desired functionality and principles of photoplethysmography are used to create a set of design goals and guidelines for such a sensor.(cont.) A working miniaturized prototype nail sensor is designed, built, tested, and analyzed. Based on fingertip anatomy and photographic evidence, mechanical and hemodynamic models are created in order to understand the mechanism of the blood volume change at multiple locations within the fingernail bed. These models are verified through experiment and simulation. Next, data-driven, mathematical models or filters are designed to comprehensively predict normal touching forces, shear touching forces, and finger bending based on readings from the sensor. A method to experimentally calibrate the filters is designed, implemented, and validated. Using these filters, the sensors are capable of predicting forces to within 0.5 N RMS error and posture angle to within 10 degrees RMS error. Performances of the filters are analyzed, compared, and used to suggest design guidelines for the next generation of sensors. Finally, applications to human-machine interface are discussed and tested, and potential impacts of this work on the fields of virtual reality and robotics are proposed.by Stephen A. Mascaro.Ph.D

Experimental and analysis study on GloveMAP grasping force signal using Gaussian filtering method and principal component analysis (PCA)

This research paper presents the analysis study of human grasping forces for several objects by using a DataGlove called GloveMAP.The grasping force is generated from the bending of proximal and intermediate phalanges of the fingers when touching with a surface.A flexiforce sensor is installed at the finger’s position of the GloveMAP.The acquired grasping force signals are filtered by using a Gaussian filtering for the purpose of removing noises.A Principal Component Analysis technique (PCA) is employed to reduce the dimension of the grasping force signal, and follows by the extraction of its features.In the experiment, five subjects are selected to perform the grasping activities.The experimental results show that the Gaussian filter could be used to smoothen the grasping force signals. Moreover, the first and the second principal components of PCA could be used to extract features of grasping force signals

Development of Finger ContracturePrevention System For Early Post StrokeRehabilitation

芝浦尾工業大学2016年

The interaction between motion and texture in the sense of touch

Besides providing information on elementary properties of objects, like texture, roughness, and softness, the sense of touch is also important in building a representation of object movement and the movement of our hands. Neural and behavioral studies shed light on the mechanisms and limits of our sense of touch in the perception of texture and motion, and of its role in the control of movement of our hands. The interplay between the geometrical and mechanical properties of the touched objects, such as shape and texture, the movement of the hand exploring the object, and the motion felt by touch, will be discussed in this article. Interestingly, the interaction between motion and textures can generate perceptual illusions in touch. For example, the orientation and the spacing of the texture elements on a static surface induces the illusion of surface motion when we move our hand on it or can elicit the perception of a curved trajectory during sliding, straight hand movements. In this work we present a multiperspective view that encompasses both the perceptual and the motor aspects, as well as the response of peripheral and central nerve structures, to analyze and better understand the complex mechanisms underpinning the tactile representation of texture and motion. Such a better understanding of the spatiotemporal features of the tactile stimulus can reveal novel transdisciplinary applications in neuroscience and haptics

Low-cost sensory glove for human-robot collaboration.

Masters Degree. University of KwaZulu-Natal, Durban.Human Robot Collaboration (HRC) is a technique that enables humans and robots to co-exist in the same environment by preforming operations together. HRC has become a vital goal for industry to achieve progress towards the fourth industrial revolution (Lotz, Himmel, & Ziefle, 2019) as it focuses on creating advanced production/manufacturing plants that have high levels of productivity, efficiency, quality and automation. Sensory gloves can be used to enhance the Human Robot Collaboration environment in order to achieve progress towards Industry 4.0. It can provide a safe environment where humans and robots can interact and work in conjunction. However, challenges exist in terms of cost, accuracy, repeatability and dynamic range of such devices. The project researched and developed a low-cost sensory glove to enable a user to collaborate with an industrial robot in a production environment. The sensory glove was used to provide a process whereby humans could collaborate with the robot through physical interaction under safe conditions. The sensory glove used IMU sensors in order to track the orientation of the user’s hand accurately. An algorithm was developed and designed to extract the data from the glove and create a simulated three-dimensional render of the hand as it moved through free space. This involved the design and development of an electronic system architecture that powers the glove. A control system was developed to enable the extraction of data and create the simulated three-dimensional hand model. It produced the image that the robot would sense when interacting with the worker. Testing was conducted on the cost, accuracy, dynamic range, repeatability and potential application of the system. The results showed that it was an innovative and low-cost method for humans and robots to collaborate in a safe environment. The apparatus established a process whereby humans and robots could perform operations together