NDLI: A comparison of encoding schemes for haptic object recognition using a biologically plausible spiking neural network

Content Provider	IEEE Xplore Digital Library
Author	Ratnasingam, Sivalogeswaran McGinnity, T.M.
Copyright Year	2011
Description	Author affiliation: Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Magee campus, University of Ulster, Londonderry, Northern Ireland, UK (Ratnasingam, Sivalogeswaran) \|\| Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Magee campus, University of Ulster Londonderry, Northern Ireland, UK (McGinnity, T.M.)
Abstract	In this paper a biologically inspired spiking neural network based haptic object recognition system is proposed. A number of different encoding schemes to convert the haptic measurements into spike train are proposed and investigated for haptic object recognition and compared with existing encoding schemes. The spiking neural network was trained using a supervised training approach that is based on the steepest descent algorithm. During the training, firing threshold of the hidden layer neurons were modified in such a way that the ability of the system in recognising different objects is maximised. A multiplexing scheme is used to convert the parallel spike train of the hidden layer into a serial stream. To convert the output spike train into a reliable feature that represents the shape of an object, moment of the spikes with respect to a reference time is calculated. A robot hand with three fingers that have the same number of degrees of freedom as the human fingers was used for testing the system. The hand was made to grasp different objects and the joint angles were recorded. These recorded angles were converted into spike train using different encoding schemes and applied as input to the network. Test results show that the performance of the system varies depending on the input encoding scheme and with the best encoring scheme the system can recognise 100% of 7 different objects.
Starting Page	3446
Ending Page	3453
File Size	637881
Page Count	8
File Format	PDF
ISBN	9781612844541
ISSN	21530858
e-ISBN	9781612844565
DOI	10.1109/IROS.2011.6094835
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Encoding Joints Haptic interfaces Thumb Robot sensing systems
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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