Geodesic
A model for estimating the value of the applied pressure based on the analysis of tactile sensor signals using machine learning methods
Vestnik KRAUNC. Fiziko-matematičeskie nauki, Tome 37 (2021) no. 4, pp. 119-130 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

Currently, in the field of developing sensing systems for robotic means, one of the urgent tasks is the problem of interpreting the data of tactile pressure and proximity sensors. As a rule, the solution to this problem is complicated both by the dependence of the indicators of tactile sensors on the type of object's material and by the design features of each individual device. In this study, an analysis of existing works devoted to the interpretation of the readings of tactile sensor devices was carried out. According to the analysis results a machine learning model was proposed that allows estimating the amount of pressure applied to the surface of a tactile pressure sensor of a capacitive type. The architecture of the proposed model includes two key blocks of data analysis, the first one is aimed at recognizing the type of interaction object's material and the second is devoted to the direct assessment of the magnitude of the pressure applied to the sensor. Several machine learning methods were considered as supporting models for processing and interpreting the signals of this device: linear regression, polynomial regression, decision tree regression, partial least squares regression and a fully connected feedforward neural network. 
@article{VKAM_2021_37_4_a11,
     author = {P. S. Kozyr and R. N. Yakovlev},
     title = {A model for estimating the value of the applied pressure based on the analysis of tactile sensor signals using machine learning methods},
     journal = {Vestnik KRAUNC. Fiziko-matemati\v{c}eskie nauki},
     pages = {119--130},
     year = {2021},
     volume = {37},
     number = {4},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VKAM_2021_37_4_a11/}
}
TY  - JOUR
AU  - P. S. Kozyr
AU  - R. N. Yakovlev
TI  - A model for estimating the value of the applied pressure based on the analysis of tactile sensor signals using machine learning methods
JO  - Vestnik KRAUNC. Fiziko-matematičeskie nauki
PY  - 2021
SP  - 119
EP  - 130
VL  - 37
IS  - 4
UR  - http://geodesic.mathdoc.fr/item/VKAM_2021_37_4_a11/
LA  - ru
ID  - VKAM_2021_37_4_a11
ER  - 
%0 Journal Article
%A P. S. Kozyr
%A R. N. Yakovlev
%T A model for estimating the value of the applied pressure based on the analysis of tactile sensor signals using machine learning methods
%J Vestnik KRAUNC. Fiziko-matematičeskie nauki
%D 2021
%P 119-130
%V 37
%N 4
%U http://geodesic.mathdoc.fr/item/VKAM_2021_37_4_a11/
%G ru
%F VKAM_2021_37_4_a11
P. S. Kozyr; R. N. Yakovlev. A model for estimating the value of the applied pressure based on the analysis of tactile sensor signals using machine learning methods. Vestnik KRAUNC. Fiziko-matematičeskie nauki, Tome 37 (2021) no. 4, pp. 119-130. http://geodesic.mathdoc.fr/item/VKAM_2021_37_4_a11/

[1] Chi, C., Sun, X., Xue, N., Li, T., Liu, C., “Recent Progress in Technologies for Tactile Sensors”, Sensors, 18:4 (2018), 948 | DOI

[2] Park, M., Bok, B.-G., Ahn, J.-H., Kim, M.-S., “Recent Advances in Tactile Sensing Technology”, Micromachines, 9:7 (2018), 321 | DOI

[3] Zou, L., Ge, C., Wang, Z., Cretu, E., Li, X., “Novel Tactile Sensor Technology and Smart Tactile Sensing Systems: A Review”, Sensors, 17:11 (2017), 2653 | DOI

[4] Heng, W., Pang, G., Xu, F., Huang, X., Pang, Z., Yang, G., “Flexible Insole Sensors with Stably Connected Electrodes for Gait Phase Detection”, Sensors, 19:23 (2019), 5197 | DOI

[5] Krestovnikov K., Cherskikh E., Zimuld-inov E., “Combined Capacitive Pressure and Proximity Sensor for Using in Robotic Systems”, Proceedings of 15th International Conference on Electromechanics and Robotics “Zavalishin’s Readings”, 2021, 513-522 | DOI

[6] Yerashov A. A., Krestovnikov K. D., Bykov A. N., “Razrabotka skhemotekhnicheskogo resheniya i konstruktsii yemkostnoy matritsy datchikov davleniya dlya primeneniya v robototekhnike”, Robototekhnika i tekhnicheskaya kibernetika, 4 (2020) (In Russian)

[7] Fragkiadakis, E., Dalakleidi, K. V., Ni-kita, K. S., “Design and Development of a Sitting Posture Recognition System”, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology So-ciety, 2019

[8] Roh, J., Park, H., Lee, K., Hyeong, J., Kim, S., Lee, B., “Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning”, Sensors, 18:2 (2018), 208 | DOI

[9] Lee, G., Park, J., Byun, J., Yang, J. C., Kwon, S. Y., Kim, C., Jang, C., Sim J.Y., Yook J. G., Park, S., “Parallel Signal Processing of a Wireless Pressure-Sensing Platform Combined with Machine-Learning-Based Cognition, Inspired by the Human Somatosensory System”, Advanced Materials, 2019, 1906269

[10] Matijevich E. S., Volgyesi P., Zelik K. E. A, “Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling”, Sensors, 21:2 (2021), 340 | DOI

[11] Jiang, Y., Deng, S., Sun, H., Qi, Y., “Unconstrained monitoring method for heartbeat signals measurement using pressure sensors array”, Sensors, 19:2 (2019), 368 | DOI

[12] Jung, M., Vishwanath, S. K., Kim, J., Ko, D.-K., Park, M.-J., Lim, S.-C., Jeon, S., “Transparent and Flexible Mayan-Pyramid-based Pressure Sensor using Facile-Transferred Indium tin Oxide for Bimodal Sensor Applications”, Scientific Reports, 9:1 (2019)

[13] Yao, H., Yang, W., Cheng, W., Tan, Y. J., See, H. H., Li, S., Ali H.P.A., Lim B. Z.H., Liu Z., Tee, B. C. K., “Near–hysteresis-free soft tactile electronic skins for wearables and reliable machine learning”, Proceedings of the National Academy of Sci-ences, 117:41 (2020), 202010989

[14] Vesy DL-3102, Rezhim dostupa (In Russian) (data obrashcheniya – 20.03.2021) http://dl.demcom.ru/dl3102.html

[15] Bolstad W.M., Curran J.M., Introduction to Bayesian statistics, John Wiley Sons, 2016

[16] Smith C., Koning M., Decision Trees and Random Forests: A Visual Introduction for Beginners, Independently published, 2017

[17] Mirjalili S., Faris H., Aljarah I., Evolutionary Machine Learning Techniques, Springer, 2019

[18] Montgomery D., Peck E., Vining G., Introduction to Linear Regression Analysis, 2012

[19] Rokach L., Data Mining with Decision Trees, 2014

[20] Garson D., Partial Least Squares: Regression and Structural Equation Models, 2016

[21] Goodfellow I, Bengio Y, Courville A., Deep Learning, 2018