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Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between garments and human body. Herein we present a method to detect and recognize human posture with casual loose-fitting smart garments integrated with highly sensitive, stretchable, optical transparent, and low-cost strain sensors. By attaching these sensors to an off-the-shelf casual jacket, we developed a smart loose-fitting sensing garment that enables posture recognition using a deep learning model, domain-adaptive Convolutional Neural Networks\u2013Long Short-Term Memory (CNN-LSTM). This deep learning model overcame the noise and variation due to the complex interaction between loose-fitting garments and human body. Considering that users\u2019 labeled data are usually not available in the training stage, an additional domain discriminator path on the conventional CNN-LSTM model has been introduced to further improve the adaptability. To evaluate the potential of this loose-fitting smart garment, three case studies were conducted under realistic conditions: recognitions of human activities, stationary postures with random hand movements and slouch. Our results demonstrate the potential of the proposed smart garment system for practical applications.<\/jats:p>","DOI":"10.1145\/3584986","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T11:48:38Z","timestamp":1676893718000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Subject-adaptive Loose-fitting Smart Garment Platform for Human Activity Recognition"],"prefix":"10.1145","volume":"19","author":[{"given":"Qi","family":"Lin","sequence":"first","affiliation":[{"name":"Uppsala University"}]},{"given":"Shuhua","family":"Peng","sequence":"additional","affiliation":[{"name":"University of New South Wales"}]},{"given":"Yuezhong","family":"Wu","sequence":"additional","affiliation":[{"name":"University of New South Wales and Data61 CSIRO"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"University of New South Wales"}]},{"given":"Hong","family":"Jia","sequence":"additional","affiliation":[{"name":"University of New South Wales"}]},{"given":"Wen","family":"Hu","sequence":"additional","affiliation":[{"name":"University of New South Wales and Data61 CSIRO"}]},{"given":"Mahbub","family":"Hassan","sequence":"additional","affiliation":[{"name":"University of New South Wales"}]},{"given":"Aruna","family":"Seneviratne","sequence":"additional","affiliation":[{"name":"University of New South Wales and Data61 CSIRO"}]},{"given":"Chun H.","family":"Wang","sequence":"additional","affiliation":[{"name":"University of New South Wales"}]}],"member":"320","published-online":{"date-parts":[[2023,5,16]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1","article-title":"Ieee draft standard for local and metropolitan area networks - part 15.4: Low rate wireless personal area networks (lr-wpans) - amendment 6: Tv white space between 54 mhz and 862 mhz physical layer","unstructured":"Ieee draft standard for local and metropolitan area networks - part 15.4: Low rate wireless personal area networks (lr-wpans) - amendment 6: Tv white space between 54 mhz and 862 mhz physical layer. 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