Development of a Sensor-Based Glove Device for Extracting Human Finger Motion Data Used in the Design of Minimally Actuated Mechanical Fingers

2013 ◽  
Author(s):  
N. P. Robson
Keyword(s):  
Motion Data ◽  
Finger Motion ◽  
Human Finger

scholarly journals Research on finger movement sensing performance of conductive gloves

2019 ◽  
Vol 14 ◽  
pp. 155892501988762 ◽  
Author(s):  
Xiaoxue Han ◽  
Xuhong Miao ◽  
Xi Chen ◽  
Gaoming Jiang ◽  
Li Niu
Keyword(s):  
Index Finger ◽  
Dynamic Test ◽  
Rehabilitation Medicine ◽  
Proximal Interphalangeal Joint ◽  
Knitted Fabric ◽  
Resistance Change ◽  
Flexible Sensors ◽  
Finger Motion ◽  
Human Finger ◽  
Resistance Data

Knitted flexible sensors are sensors based on the loop structure of knitted fabric, which are soft and close-fitting. Monitoring finger motion can obtain useful information for some applications such as rehabilitation medicine, sports bionics, or human–computer interaction. In this paper, a conductive glove was knitted by SHIMA Seiki SWG 061N-15G computerized flat knitting machine. One experimenter wore it to measure motions data of index finger. The glove has a conductive intarsia area knitted by silver-nylon filaments. The experimenter performed static and dynamic test of hand posture, respectively, then observed the effect of figure bending characteristics on the glove resistance data. The result showed that human finger motion can be monitored successfully by the conductive glove without hard transducers, and both of the bending rate ( Br) and bending angle of the finger proximal interphalangeal joint ( Pba) affect the resistance change of the conductive area of the glove. In other words, the conductive glove has potentials to monitor and reflect human finger motions in detail.

scholarly journals Design of a Two-DOFs Driving Mechanism for a Motion-Assisted Finger Exoskeleton

2020 ◽  
Vol 10 (7) ◽  
pp. 2619 ◽  
Author(s):  
Giuseppe Carbone ◽  
Eike Christian Gerding ◽  
Burkard Corves ◽  
Daniele Cafolla ◽  
Matteo Russo ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Experimental Tests ◽  
Design Solution ◽  
Driving Mechanism ◽  
Dimensional Synthesis ◽  
Linkage Mechanism ◽  
Healthy Human ◽  
Wide Range ◽  
Finger Motion ◽  
Human Finger

This paper presents a novel exoskeleton mechanism for finger motion assistance. The exoskeleton is designed as a serial 2-degrees-of-freedom wearable mechanism that is able to guide human finger motion. The design process starts by analyzing the motion of healthy human fingers by video motion tracking. The experimental data are used to obtain the kinematics of a human finger. Then, a graphic/geometric synthesis procedure is implemented for achieving the dimensional synthesis of the proposed novel 2 degrees of freedom linkage mechanism for the finger exoskeleton. The proposed linkage mechanism can drive the three finger phalanxes by using two independent actuators that are both installed on the back of the hand palm. A prototype is designed based on the proposed design by using additive manufacturing. Results of numerical simulations and experimental tests are reported and discussed to prove the feasibility and the operational effectiveness of the proposed design solution that can assist a wide range of finger motions with proper adaptability to a variety of human fingers.

scholarly journals A bionic soft robotic glove mimicking finger actions based on sEMG recognition

2021 ◽  
Author(s):  
Shumi Zhao ◽  
Ziwen Wang ◽  
Yisong Lei ◽  
Shaotong Huang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Specific Surface ◽  
Action Recognition ◽  
Electric Potential ◽  
Soft Robotics ◽  
Novel Technique ◽  
Glove Finger ◽  
Finger Motion ◽  
Human Finger ◽  
Forearm Muscle ◽  
Semg Signals

Abstract Compared with rigid robots, soft robotics is more suitable to develop anthropomorphic digits that mimics the biological structures and dexterous motions of human finger. This study proposed a surface electromyogram (sEMG) sensors-based soft robotic glove system which was able to recognize the finger activities and execute the same operation via the bionic glove. Finger activities can be recognized by using electrodes sensors to monitor the electric potential variations on specific surface of the forearm muscle regions. A hybrid robotic digit was designed that utilizes pneumatic bellow actuators to satisfy the anatomical range of the finger motion in order to mimic finger action according to sEMG information. The moving trajectory of digit tip and the range motion of each joint of the robotic digit were measured in experiments under the pressure from 0kPa to 70kPa. The bionic soft robotic glove successfully demonstrated the finger action recognition and robotic digits controlling for a variety of manipulation tasks. The feasible results provided a novel technique for controlling the soft robotic glove through sEMG signals holistically and practically, and also give inspiration and guidance for multiple fingers remote operational applications.

Clustering Finger Motion Data From Virtual Reality-Based Training to Analyze Patients With Mild Cognitive Impairment

2018 ◽  
pp. 1343-1358
Author(s):  
Niken Prasasti Martono ◽  
Takehiko Yamaguchi ◽  
Takuya Maeta ◽  
Hibiki Fujino ◽  
Yuki Kubota ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Clustering Algorithm ◽  
Clinical Settings ◽  
Motion Data ◽  
Acceleration Data ◽  
Finger Motion ◽  
Learning Software ◽  
Healthy Participants

Research in virtual reality (VR) has resulted in the development of many applications in clinical settings in the areas of learning and therapy in psychology and neuropsychology because this technology can be flexible to the needs of the clinical application. VR technology has many implementations for cognitive training and as a screening tool for patients with mild cognitive impairment (MCI). The technology has been used in the screening, diagnosis, treatment and support of patients with MCI. This study found that the information recorded in VR-based learning software can be useful in analyzing individuals with MCI in order to characterize groups of participants. The authors implemented a time series clustering algorithm acting on finger motion data from nine healthy participants as a pilot study, then comprehensively reviewed the clustering result by comparing it with performance-based measures. The results indicate that the clusters formed by using the acceleration data is reasonably analogous to the performance measures (i.e., with respect to the type and number of errors that occurred).

Reconstructing Humans’ Hand Motion: Preliminary Results and Applications in the Design of Mechanical Fingers for Anthropomorphic Tasks

2013 ◽  
Author(s):  
Shramana Ghosh ◽  
Hyosang Moon ◽  
Nina P. Robson
Keyword(s):  
Degrees Of Freedom ◽  
Low Cost ◽  
Optical Tracking ◽  
Human Hand ◽  
Hand Motion ◽  
Preliminary Results ◽  
Motion Data ◽  
Higher Derivative ◽  
Acceleration Data ◽  
Finger Motion

This paper reports the development of a low-cost sensor-based glove device using commercially available components that can be used to obtain position, velocity and acceleration data for individual fingers of the hand. Optical tracking of the human hand and finger motion is a challenging task due to the large number of degrees of freedom (DOFs) packed in a relatively small space. We propose methods to simplify the hand motion capture by utilizing accelerometers and adopting a reduced marker protocol. The preliminary results show that the use of relative position, velocity and acceleration homogeneous transformations enable us in getting improved finger motion data with respect to those obtained from a Vicon system. This data is directly related to contact and curvature constraints between the fingers and the grasped object. Once obtained from the glove, the higher derivative specifications are used in formulating the synthesis task for the design of robotic fingers.

Clustering Finger Motion Data from Virtual Reality-Based Training to Analyze Patients with Mild Cognitive Impairment

2016 ◽  
Vol 8 (4) ◽  
pp. 29-42 ◽  
Author(s):  
Niken Prasasti Martono ◽  
Takehiko Yamaguchi ◽  
Takuya Maeta ◽  
Hibiki Fujino ◽  
Yuki Kubota ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Clustering Algorithm ◽  
Clinical Settings ◽  
Motion Data ◽  
Acceleration Data ◽  
Finger Motion ◽  
Learning Software ◽  
Healthy Participants

Research in virtual reality (VR) has resulted in the development of many applications in clinical settings in the areas of learning and therapy in psychology and neuropsychology because this technology can be flexible to the needs of the clinical application. VR technology has many implementations for cognitive training and as a screening tool for patients with mild cognitive impairment (MCI). The technology has been used in the screening, diagnosis, treatment and support of patients with MCI. This study found that the information recorded in VR-based learning software can be useful in analyzing individuals with MCI in order to characterize groups of participants. The authors implemented a time series clustering algorithm acting on finger motion data from nine healthy participants as a pilot study, then comprehensively reviewed the clustering result by comparing it with performance-based measures. The results indicate that the clusters formed by using the acceleration data is reasonably analogous to the performance measures (i.e., with respect to the type and number of errors that occurred).

A Mechanical Design of One D.O.F. Anthropomorphic Finger Based on a Human Finger Motion

2000 ◽  
Author(s):  
Giorgio Figliolini ◽  
Marco Ceccarelli
Keyword(s):  
Mechanical Design ◽  
Analytical Formulation ◽  
Numerical Examples ◽  
Kinematic Characteristics ◽  
Practical Design ◽  
Finger Motion ◽  
Human Finger

Abstract In this paper we propose an analytical formulation for simulation and design of one d.o.f. articulated finger mechanism with three phalanges. The formulation is based on a study of design and operation of index human finger. In particular, we propose a suitable mechanical design for an anthropomorphic finger as both an approximation of human architecture and an easy practical design. Kinematic characteristics are illustrated with numerical examples.

On redundancy resolution of the human thumb, index and middle fingers in cooperative object translation

Robotica ◽  
2016 ◽  
Vol 35 (10) ◽  
pp. 1992-2017 ◽  
Author(s):  
Felix Orlando Maria Joseph ◽  
Laxmidhar Behera ◽  
Tomoya Tamei ◽  
Tomohiro Shibata ◽  
Ashish Dutta ◽  
...  
Keyword(s):  
Kinematic Model ◽  
Inverse Kinematic ◽  
Small Object ◽  
Redundancy Resolution ◽  
Joint Angles ◽  
Motion Data ◽  
Grasping And Manipulation ◽  
Finger Motion ◽  
And Control ◽  
Neuromuscular Coordination

SUMMARYRedundancy in motion, and synergy in neuromuscular coordination provides significant versatility to the human fingers while performing coordinated grasping and manipulation tasks in several ways. This paper explores how humans may resolve the redundancy in their thumb, index and middle fingers when these digits flex to cooperatively translate a small object toward the palm. It is observed that humans actively employ a secondary subtask of maximizing instantaneous manipulability that helps determine all intermediate finger configurations when performing the primary subtask of following a tip trajectory. This behavior is accurately captured by an inverse kinematic model based on aredundancyparameter. The joint angles get determined unambiguously though the redundancy parameter is shown to depend on the instantaneous finger configurations and also, to attain negative values. Further, this parameter is noted to vary significantly across subjects performing the same kinematic task. The findings, that are based on the experimental finger motion data garnered from 12 subjects, are reckoned to be of significant importance, especially in reference to the challenges in design and control of finger exoskeletons for cooperative manipulation.

Saving and Reproduction of Human Motion Data by Using Haptic Devices with Different Configurations

2011 ◽  
Vol 131 (3) ◽  
pp. 267-274 ◽  
Author(s):  
Noboru Tsunashima ◽  
Yuki Yokokura ◽  
Seiichiro Katsura
Keyword(s):  
Human Motion ◽  
Haptic Devices ◽  
Motion Data
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