Real-Time Slip Detection using Tactile Information
- Resource Type
- Conference
- Authors
- Phukan, Nabasmita; Kakoty, Nayan M.; Sharma, Manoj
- Source
- 2021 IEEE 9th Region 10 Humanitarian Technology Conference (R10-HTC) Humanitarian Technology Conference (R10-HTC), 2021 IEEE 9th Region 10. :01-05 Sep, 2021
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Support vector machines
Machine learning algorithms
Force
Grasping
Data gloves
Real-time systems
Robustness
Slip Detection
Tactile Information
Data Glove
Machine Learning Algorithms
- Language
- ISSN
- 2572-7621
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.