Data Acquisition System and Pattern Image Generations for Hand Grip Device

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Boon Chin Yeo
Ming-Horng Wong
Poh Kiat Ng
Wei Jun Choong

Abstract

Grip pattern is essential to understand how an object being held in hand. One of the solutions is to use the pressure sensing glove to capture the gripping pressure distributed on the surface of the palm. The objective of this project is to develop a data acquisition system for a gripping device that can capture the grip patterns when a person is gripping an object. The design comprises of Velostat sheet, rows, and columns of conductive threads, that are sandwiched and layered to form a glove with pressure sensor grids. Arduino is used to generate the signals for data acquisition and interface with the MATLAB program through serial communication. On the MATLAB, the sensor data are organized and represented in hand pattern color image. Voltage Divider Rule (VDR) was used in an experiment with different resistor values and the effect of the image patterns were observed. Another experiment has been designed to find out the grip consistency. The results show that resistor values 330ohm can cause the image pattern create noises. Meanwhile, 4.7kohm resistance value is sufficient to eliminate most of the noises made in the pattern images. In this paper, different grip images can be obtained from different grip activities, such as holding toothbrush, lifting dumbbell, and pressing syringe. Future works can be done in resolution improvement and grip pattern recognition.

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References

G. Buscher, Risto Koiva, C. Schurmann, R. Haschke, and H. J. Ritter, “Tactile Dataglove with Fabric-Based Sensors,” Business Innovation Center Osaka, Japan, Dec. 2012, pp. 204–209.

D. Trosclair, D. Bellar, L. W. Judge, J. Smith, N. Mazerat, and A. Brignac, “Hand-Grip Strength as a Predictor of Muscular Strength and Endurance:,” Journal of Strength and Conditioning Research, vol. 25, p. S99, Mar. 2011, doi: 10.1097/01.JSC.0000395736.42557.bc.

Q. Wang, P. Markopoulos, B. Yu, W. Chen, and A. Timmermans, “Interactive wearable systems for upper body rehabilitation: a systematic review,” J NeuroEngineering Rehabil, vol. 14, no. 1, p. 20, Dec. 2017, doi: 10.1186/s12984-017-0229-y.

A. C. McConnell et al., “SOPHIA: Soft Orthotic Physiotherapy Hand Interactive Aid,” Front. Mech. Eng., vol. 3, p. 3, Jun. 2017, doi: 10.3389/fmech.2017.00003.

A. Cuadra et al., “Functional results of burned hands treated with Integra®,” Journal of Plastic, Reconstructive & Aesthetic Surgery, vol. 65, no. 2, pp. 228–234, Feb. 2012, doi: 10.1016/j.bjps.2011.09.008.

G. Waddington, J. Diong, and R. Adams, “Development Of A Hand Dynamometer For The Control Of Manually Applied Forces,” Journal of Manipulative and Physiological Therapeutics, vol. 29, no. 4, p. 8, 2006.

H. Chang, C.-H. Chen, T.-S. Huang, and C.-Y. Tai, “Development of an integrated digital hand grip dynamometer and norm of hand grip strength,” BME, vol. 26, no. s1, pp. S611–S617, Aug. 2015, doi: 10.3233/BME-151352.

D. J. Hewson, K. Li, A. Frerejean, J.-Y. Hogrel, and J. Duchêne, “Domo-Grip: Functional Evaluation and Rehabilitation Using Grip Force,” Buenos Aires, Argentina, Sep. 2010, pp. 1308–1311.

B. Wimer, R. G. Dong, D. E. Welcome, C. Warren, and T. W. McDowell, “Development of a new dynamometer for measuring grip strength applied on a cylindrical handle,” Medical Engineering & Physics, vol. 31, no. 6, pp. 695–704, Jul. 2009, doi: 10.1016/j.medengphy.2009.01.009.

K. Makino et al., “Development of a Finger Force Distribution Measurement System for Hand Dexterity,” in IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, Oct. 2018, pp. 4270–4275, doi: 10.1109/IECON.2018.8592789.

S. Pereira, R. Simoes, J. Fonseca, R. Carvalho, and J. Almeida, “Design and development of an embedded sensors matrix for pressure mapping and monitoring applications,” Microprocessors and Microsystems, vol. 74, p. 103004, Apr. 2020, doi: 10.1016/j.micpro.2020.103004.

J. Saenz-Cogollo, M. Pau, B. Fraboni, and A. Bonfiglio, “Pressure Mapping Mat for Tele-Home Care Applications,” Sensors, vol. 16, no. 3, p. 365, Mar. 2016, doi: 10.3390/s16030365.

K. Xu, Y. Lu, and K. Takei, “Multifunctional Skin-Inspired Flexible Sensor Systems for Wearable Electronics,” Adv. Mater. Technol., vol. 4, no. 3, p. 1800628, Mar. 2019, doi: 10.1002/admt.201800628.

M. O. Culjat et al., “Remote tactile sensing glove-based system,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Aug. 2010, pp. 1550–1554, doi: 10.1109/IEMBS.2010.5626824.

T. Sagisaka, Y. Ohmura, Y. Kuniyoshi, A. Nagakubo, and K. Ozaki, “High-density Conformable Tactile Sensing Glove,” Oct. 2011, pp. 537–542.

Z. Wang, J. Hoelldampf, and M. Buss, “Design and Performance of a Haptic Data Acquisition Glove,” p. 10, 2007.

Kazuya Matsuo, Kouji Murakami, Tsutomu Hasegawa, and Ryo Kurazume, “A decision method for the placement of tactile sensors for manipulation task recognition,” in 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, May 2008, pp. 1641–1646, doi: 10.1109/ROBOT.2008.4543436.

S. Sundaram, P. Kellnhofer, Y. Li, J.-Y. Zhu, A. Torralba, and W. Matusik, “Learning the signatures of the human grasp using a scalable tactile glove,” Nature, vol. 569, no. 7758, pp. 698–702, May 2019, doi: 10.1038/s41586-019-1234-z.

D. Giovanelli and E. Farella, “Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing,” Journal of Sensors, vol. 2016, p. 14, Jan. 2016.

V. Kumar, B.-C. Yeo, and W.-S. Lim, “Fall Detection with Support Vector Machine for Elderly Care using Pressure Sensor Grid,” J. Eng. Appl. Sci., vol. 15, no. 2, pp. 636–642, Oct. 2019, doi: 10.36478/jeasci.2020.636.642.

S. Pheasant and D. O’Neill, “Performance in gripping and turning —A study in hand/handle effectiveness,” Applied Ergonomics, vol. 6, no. 4, pp. 205–208, Dec. 1975, doi: 10.1016/0003-6870(75)90111-8.