Abstract
In all prior applications, it is assumed that the just noticeable difference (JND) for the kinesthetic force stimulus is independent of rate of change of the stimulus. In this work, we study how the JND is affected over the rate of change of the stimulus. This study has a possible application in better design of a haptic data compression algorithm. We design an experimental set up where users are subjected to a continuous haptic force which starts increasing or decreasing from a fixed reference force value, and record the haptic responses. A machine learning classifier- SVM is trained using the recorded haptic responses to estimate the best fit linear decision boundary between the JND and the rate of change of the stimulus. Our results show that the JND decreases for faster change in the stimulus. We also demonstrate an asymmetric behavior of perception between increasing and decreasing cases.
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Notes
- 1.
The data is divided into 10 equal parts which have the same proportions of the perceived (+1)and nonperceived (\(-1\)) labels. Nine parts are used to train the classifier, and the remaining part is used to evaluate the performance of the prediction. Thus, the classifier is trained and tested 10 times to find the average prediction accuracy.
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Acknowledgment
Partial financial supports from Bharti Centre for Communication in IIT Bombay, DST, and DEITY are gratefully acknowledged.
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Bhardwaj, A., Chaudhuri, S. (2014). Does Just Noticeable Difference Depend on the Rate of Change of Kinesthetic Force Stimulus?. In: Auvray, M., Duriez, C. (eds) Haptics: Neuroscience, Devices, Modeling, and Applications. EuroHaptics 2014. Lecture Notes in Computer Science(), vol 8618. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44193-0_6
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DOI: https://doi.org/10.1007/978-3-662-44193-0_6
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