{"title":"Real-Time Slip Detection using Tactile Information","authors":"Nabasmita Phukan, N. M. Kakoty, Manoj Sharma","doi":"10.1109/R10-HTC53172.2021.9641576","DOIUrl":null,"url":null,"abstract":"Slip detection is of paramount importance for stabilized grasping of objects by a prosthetic hand. This paper presents a real-time slip detection framework using a data glove customized with force sensors. The data glove can acquire grasping force with a root mean square error (RMSE) of ±0.21 Newton. A finite state machine (FSA) algorithm was implemented for estimating the instances of slip occurrence as features. Support Vector Machine (SVM) with polynomial and radial basis function (RBF) kernel, k-nearest neighbor (k-NN), Naive Bayes (NB) and Random Forest algorithms were evaluated for detection of slip. An average accuracy of 94% and 98% was achieved using polynomial and RBF kernel SVM respectively. Further NB, k-NN and Random Forest algorithms resulted into an average accuracy of 96 %, 99 % and 100 % respectively. These experimental results show that the proposed framework is very useful for slip detection using tactile force information. It demonstrated robustness of FSA with machine learning algorithms for real-time slip detection and thereby holds promise for stabilized grasping by a prosthetic hand.","PeriodicalId":117626,"journal":{"name":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/R10-HTC53172.2021.9641576","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Slip detection is of paramount importance for stabilized grasping of objects by a prosthetic hand. This paper presents a real-time slip detection framework using a data glove customized with force sensors. The data glove can acquire grasping force with a root mean square error (RMSE) of ±0.21 Newton. A finite state machine (FSA) algorithm was implemented for estimating the instances of slip occurrence as features. Support Vector Machine (SVM) with polynomial and radial basis function (RBF) kernel, k-nearest neighbor (k-NN), Naive Bayes (NB) and Random Forest algorithms were evaluated for detection of slip. An average accuracy of 94% and 98% was achieved using polynomial and RBF kernel SVM respectively. Further NB, k-NN and Random Forest algorithms resulted into an average accuracy of 96 %, 99 % and 100 % respectively. These experimental results show that the proposed framework is very useful for slip detection using tactile force information. It demonstrated robustness of FSA with machine learning algorithms for real-time slip detection and thereby holds promise for stabilized grasping by a prosthetic hand.