{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T07:43:12Z","timestamp":1769758992689,"version":"3.49.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2014,12,8]],"date-time":"2014-12-08T00:00:00Z","timestamp":1417996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Appl. Percept."],"published-print":{"date-parts":[[2015,1,9]]},"abstract":"<jats:p>In this article, we identify adaptive sampling strategies for haptic signals. Our approach relies on experiments wherein we record the response of several users to haptic stimuli. We then learn different classifiers to predict the user response based on a variety of causal signal features. The classifiers that have good prediction accuracy serve as candidates to be used in adaptive sampling. We compare the resultant adaptive samplers based on their rate-distortion tradeoff using synthetic as well as natural data. For our experiments, we use a haptic device with a maximum force level of 3 N and 10 users. Each user is subjected to several piecewise constant haptic signals and is required to click a button whenever he perceives a change in the signal. For classification, we not only use classifiers based on level crossings and Weber\u2019s law but also random forests using a variety of causal signal features. The random forest typically yields the best prediction accuracy and a study of the importance of variables suggests that the level crossings and Weber\u2019s classifier features are most dominant. The classifiers based on level crossings and Weber\u2019s law have good accuracy (more than 90%) and are only marginally inferior to random forests. The level crossings classifier consistently outperforms the one based on Weber\u2019s law even though the gap is small. Given their simple parametric form, the level crossings and Weber\u2019s law--based classifiers are good candidates to be used for adaptive sampling. We study their rate-distortion performance and find that the level crossing sampler is superior. For example, for haptic signals obtained while exploring various rendered objects, for an average sampling rate of 10 samples per second, the level crossings adaptive sampler has a mean square error about 3dB less than the Weber sampler.<\/jats:p>","DOI":"10.1145\/2670533","type":"journal-article","created":{"date-parts":[[2014,12,16]],"date-time":"2014-12-16T13:39:54Z","timestamp":1418737194000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Design and Analysis of Predictive Sampling of Haptic Signals"],"prefix":"10.1145","volume":"11","author":[{"given":"Amit","family":"Bhardwaj","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering Indian Institute of Technology Bombay, Mumbai"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subhasis","family":"Chaudhuri","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Indian Institute of Technology Bombay, Mumbai"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Onkar","family":"Dabeer","sequence":"additional","affiliation":[{"name":"School of Technology and Computer Science Tata Institute of Fundamental Research, Mumbai"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,12,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/9.24201"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1141897.1141901"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2004.1302436"},{"key":"e_1_2_1_4_1","volume-title":"Does just noticeable difference depend on rate of change of kinesthetic force stimulus&quest","author":"Bhardwaj Amit","year":"2014"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/HAPTICS.2014.6775501"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.beproc.2005.11.006"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_1_8_1","unstructured":"Subhasis Chaudhuri. 2014. 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