{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:59:41Z","timestamp":1750309181428,"version":"3.41.0"},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"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":["GetMobile: Mobile Comp. and Comm."],"published-print":{"date-parts":[[2024,1,8]]},"abstract":"<jats:p>Recent advances in fabric-based sensors have made it possible to densely instrument plush toys without altering their aesthetic or tactile appeal, unlike traditional sensors whose rigid components can negatively impact the interactive experience. This innovation opens a new realm of interaction possibilities, allowing for the detection of nuanced gestures and movements that are crucial for understanding behavior, enhancing engagement, and potentially monitoring cognitive functions in therapeutic contexts.<\/jats:p>","DOI":"10.1145\/3640087.3640094","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T05:07:08Z","timestamp":1704776828000},"page":"21-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Fabric Pressure Sensors for Fine-Grained Interaction Detection in Plush Toys"],"prefix":"10.1145","volume":"27","author":[{"given":"Ali","family":"Kiaghadi","sequence":"first","affiliation":[{"name":"University of Massachusetts, Amherst, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyedeh","family":"Zohreh Homayounfar","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trisha","family":"Andrew","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepak","family":"Ganesan","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"C. Day and A. Midbjer. 2007. Environment and children: Passive lessons from the everyday environment. Architectural.","DOI":"10.4324\/9780080550978"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1149\/1945-7111\/abdc65"},{"volume-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 3(3).","author":"Kiaghadi A.","key":"e_1_2_1_3_1","unstructured":"A. Kiaghadi, S.Z. Homayounfar, J. Gummeson, T. Andrew, and D. Ganesan. Sept. 2019. Phyjama: Physiological sensing via fiber-enhanced pyjamas. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 3(3)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"volume-title":"International Conference on Machine Learning, PMLR, 448--456","author":"Ioffe S.","key":"e_1_2_1_5_1","unstructured":"S. Ioffe and C. Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, PMLR, 448--456."},{"key":"e_1_2_1_6_1","unstructured":"V. Nair and G.E. Hinton. 2010. Rectified linear units improve restricted Boltzmann machines. ICML."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2946140"},{"volume-title":"23rd International Conference on Pattern Recognition (ICPR), IEEE, 2464--2469","author":"Teerapittayanon S.","key":"e_1_2_1_8_1","unstructured":"S. Teerapittayanon, B. McDanel, and H.T. Kung. 2016. Branchynet: Fast inference via early exiting from deep neural networks. 23rd International Conference on Pattern Recognition (ICPR), IEEE, 2464--2469."},{"key":"e_1_2_1_9_1","unstructured":"https:\/\/www.nordicsemi.com\/Products\/nRF52840 Multiprotocol Bluetooth 5.2 SoC supporting Bluetooth Low Energy Bluetooth mesh NFC Thread and Zigbee."},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"D.E. Rumelhart G.E. Hinton and R.J. Williams. 1985. Technical Report California Univ. San Diego La Jolla Inst. for Cognitive Science. Learning internal representations by error propagation.","DOI":"10.21236\/ADA164453"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431160412331269698"},{"volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785--794","author":"Chen T.","key":"e_1_2_1_12_1","unstructured":"T. Chen and C. Guestrin. 2016. Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785--794."},{"issue":"3","key":"e_1_2_1_13_1","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman N.S.","year":"1992","unstructured":"N.S. Altman. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3):175--185.","journal-title":"The American Statistician"}],"container-title":["GetMobile: Mobile Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640087.3640094","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3640087.3640094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:54:02Z","timestamp":1750287242000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640087.3640094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":13,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,1,8]]}},"alternative-id":["10.1145\/3640087.3640094"],"URL":"https:\/\/doi.org\/10.1145\/3640087.3640094","relation":{},"ISSN":["2375-0529","2375-0537"],"issn-type":[{"type":"print","value":"2375-0529"},{"type":"electronic","value":"2375-0537"}],"subject":[],"published":{"date-parts":[[2024,1,8]]},"assertion":[{"value":"2024-01-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}