Literature DB >> 9184900

A low-cost instrumented glove for monitoring forces during object manipulation.

M C Castro1, A Cliquet.   

Abstract

A rehabilitation program toward restoring upper limb movements based on neuromuscular electrical stimulation (NMES) depends on closed-loop control performance, which has been limited by the development of sensors for practical daily use. This work proposes a system to obtain force feedback. The system is comprised of a Lycra commercial glove with force sensing resistors (FSR's) attached to the distal phalanxes of the thumb, index and long fingers. After amplification and filtering, the signal is digitized through an analog-to-digital (A/D) converter. The polynomial fitting coefficients for the characteristic curves, obtained during the sensor calibration process, were inserted in the software thus enabling the reading of forces exerted during object manipulation. The system was applied to 30 normal subjects in order to verify its feasibility and to acquire knowledge of the normal hand function. Different ways of grasping have been detected according to the Force versus Time curve pattern and to the fingers predominantly used in grasping. Results have also shown the influence of parameters such as gender, age, hand size, and object weight in the normal function. The system did show efficacy. It was able to determine grasp forces during object manipulation for up to 73% of the studied sample. This is significant since a single glove was used in a wide range of subjects. For best results in medical applications, the glove should be tailored to the particular characteristics of an individual user.

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Year:  1997        PMID: 9184900     DOI: 10.1109/86.593280

Source DB:  PubMed          Journal:  IEEE Trans Rehabil Eng        ISSN: 1063-6528


  9 in total

1.  An artificial grasping evaluation system for the paralysed hand.

Authors:  M C de Castro; A Cliquet Júnior
Journal:  Med Biol Eng Comput       Date:  2000-05       Impact factor: 2.602

2.  Object manipulation improvements due to single session training outweigh the differences among stimulation sites during vibrotactile feedback.

Authors:  Cara E Stepp; Yoky Matsuoka
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-10-06       Impact factor: 3.802

3.  Statistical Prediction of Hand Force Exertion Levels in a Simulated Push Task using Posture Kinematics.

Authors:  Sol Lim; Clive D'Souza
Journal:  Proc Hum Factors Ergon Soc Annu Meet       Date:  2017-09-28

4.  Vibrotactile sensory substitution for object manipulation: amplitude versus pulse train frequency modulation.

Authors:  Cara E Stepp; Yoky Matsuoka
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-10-13       Impact factor: 3.802

5.  Effectiveness of supplemental grasp-force feedback in the presence of vision.

Authors:  M Zafar; C L Van Doren
Journal:  Med Biol Eng Comput       Date:  2000-05       Impact factor: 3.079

6.  Design of a lightweight, cost effective thimble-like sensor for haptic applications based on contact force sensors.

Authors:  Manuel Ferre; Ignacio Galiana; Rafael Aracil
Journal:  Sensors (Basel)       Date:  2011-12-06       Impact factor: 3.576

7.  Repeated training with augmentative vibrotactile feedback increases object manipulation performance.

Authors:  Cara E Stepp; Qi An; Yoky Matsuoka
Journal:  PLoS One       Date:  2012-02-27       Impact factor: 3.240

8.  Detailed study of amplitude nonlinearity in piezoresistive force sensors.

Authors:  Leonel Paredes-Madrid; Luis Emmi; Elena Garcia; Pablo Gonzalez de Santos
Journal:  Sensors (Basel)       Date:  2011-09-14       Impact factor: 3.576

9.  Digitizing abdominal palpation with a pressure measurement and positioning device.

Authors:  Jia-Lien Hsu; Chia-Hui Lee; Chung-Ho Hsieh
Journal:  PeerJ       Date:  2020-12-17       Impact factor: 2.984

  9 in total

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