Subject-Adaptive Loose-Fitting Smart Garment Platform for Human Activity Recognition
The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human-computer-interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between gar...
Saved in:
| Published in | ACM transactions on sensor networks Vol. 19; no. 4; pp. 1 - 23 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
ACM
30.11.2023
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human-computer-interaction. 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, which enables posture recognition using a deep learning model, domain-adaptive 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’ 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. |
|---|---|
| AbstractList | The ability to recognize and detect changes in human posture is important in awide range of applications such as health care and human-computer interaction. 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-Long 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' 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. The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human–computer interaction. 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–Long 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’ 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. The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human-computer-interaction. 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, which enables posture recognition using a deep learning model, domain-adaptive 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’ 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. |
| Author | Wang, Chun H Lin, Qi Seneviratne, Aruna Peng, Shuhua Wu, Yuezhong Hassan, Mahbub Jia, Hong Hu, Wen Liu, Jun |
| Author_xml | – sequence: 1 givenname: Qi surname: Lin fullname: Lin, Qi email: qi.lin@unsw.edu.au organization: University of New South Wales – sequence: 2 givenname: Shuhua surname: Peng fullname: Peng, Shuhua email: shuhua.peng@unsw.edu.au organization: University of New South Wales – sequence: 3 givenname: Yuezhong surname: Wu fullname: Wu, Yuezhong email: yuezhong.wu@student.unsw.edu.au organization: University of New South Wales and Data61 CSIRO – sequence: 4 givenname: Jun surname: Liu fullname: Liu, Jun email: jun.liu@student.unsw.edu.au organization: University of New South Wales – sequence: 5 givenname: Hong surname: Jia fullname: Jia, Hong email: h.jia@unsw.edu.au organization: University of New South Wales – sequence: 6 givenname: Wen surname: Hu fullname: Hu, Wen email: wen.hu@unsw.edu.au organization: University of New South Wales and Data61 CSIRO – sequence: 7 givenname: Mahbub surname: Hassan fullname: Hassan, Mahbub email: mahbub.hassan@unsw.edu.au organization: University of New South Wales – sequence: 8 givenname: Aruna surname: Seneviratne fullname: Seneviratne, Aruna email: a.seneviratne@unsw.edu.au organization: University of New South Wales and Data61 CSIRO – sequence: 9 givenname: Chun H surname: Wang fullname: Wang, Chun H email: chun.h.wang@unsw.edu.au organization: University of New South Wales |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-517277$$DView record from Swedish Publication Index |
| BookMark | eNplkM9PwjAUxxuDiYDGu6fevDhdt3ZrjwsKmJBoBL02XdeSEtaSrtPw3zsy4KCX9yPv89775jsCA-usAuAWxY8IYfKUEooZzS7AEBESR5hm-eBcE3YFRk2zieM0xWk8BKtlW26UDFFRiV0w3wounGtUNDUhGLuGy1r4AGfC18oG-L4VQTtfwy7AeVsLCwvZbZmwhx9KurU1wTh7DS612Dbq5pjH4HP6sprMo8Xb7HVSLCKRIhwiSnBFBNFSI4oJQUwTXaKKKZ2kWiKVlFroXHVtqWiV0STJdJwkFWGIMsmydAwe-rvNj9q1Jd9508ndcycMfzZfBXd-zduWE5Qned7hUY9L75rGK82lCeIgOHhhthzF_OAgPzrY8fd_-NOD_-RdTwpZn6HT8BdQUXsF |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2025_3541385 crossref_primary_10_1109_JSEN_2023_3344581 |
| Cites_doi | 10.1039/D0TA05129B 10.1109/TIM.2014.2343411 10.1145/3241539.3241548 10.1016/j.patcog.2017.10.033 10.1002/adfm.201700845 10.1145/3274783.3274845 10.1021/acsnano.5b05609 10.1007/978-3-540-24646-6_3 10.1021/acsnano.6b03813 10.1021/acsami.5b00695 10.1021/acsami.6b06012 10.1109/TMC.2017.2761744 10.1007/978-3-642-35395-6_30 10.1109/SURV.2012.110112.00192 10.1063/1.4742331 10.1109/MCOM.2016.7378439 10.1016/j.patrec.2018.02.010 10.1109/TITB.2010.2076822 10.5555/2832747.2832806 10.1002/adfm.201504717 10.1039/C6TC01925K 10.1007/978-3-540-68504-3_12 10.1109/COMST.2017.2731979 10.1145/3366423.3380091 10.1007/s10072-013-1507-5 10.1109/ISWC.2007.4373773 10.1021/acsami.8b15848 10.1039/C7NR05106A 10.1002/adfm.201501000 10.1021/acsnano.5b03851 10.1063/1.3663969 10.1109/SMC.2015.263 10.1145/2493988.2494355 10.1109/IPSN48710.2020.00-47 10.1145/2733373.2806333 10.1002/adma.201470083 10.4108/icst.mobicase.2014.257786 10.1109/CVPR.2015.7298878 |
| ContentType | Journal Article |
| Copyright | Association for Computing Machinery. |
| Copyright_xml | – notice: Association for Computing Machinery. |
| DBID | AAYXX CITATION ADTPV AOWAS DF2 |
| DOI | 10.1145/3584986 |
| DatabaseName | CrossRef SwePub SwePub Articles SWEPUB Uppsala universitet |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1550-4867 |
| EndPage | 23 |
| ExternalDocumentID | oai_DiVA_org_uu_517277 10_1145_3584986 3584986 |
| GroupedDBID | -DZ .4S .DC 23M 4.4 5GY 5VS 8US AAKMM AALFJ AAYFX AAYOK ABPPZ ACGFO ACM ADBCU ADL ADMLS ADPZR AEBYY AEGXH AENEX AENSD AFFNX AFWIH AFWXC AIAGR AIKLT ALMA_UNASSIGNED_HOLDINGS ARCSS ASPBG AVWKF BDXCO CCLIF CS3 D0L EBS EDO EJD FEDTE GUFHI HF~ HGAVV H~9 I07 LHSKQ P1C P2P RNS ROL TUS W7O XOL ZCA AAYXX AEFXT AEJOY AKRVB CITATION ADTPV AOWAS DF2 |
| ID | FETCH-LOGICAL-a314t-854d5a5fcf1845519f5fb1d9ef23fc1e2bfaf7eef2be8d68226f022d59189c963 |
| ISSN | 1550-4859 1550-4867 |
| IngestDate | Thu Aug 21 06:56:17 EDT 2025 Thu Apr 24 23:06:46 EDT 2025 Thu Jul 03 08:19:52 EDT 2025 Fri Feb 21 04:13:28 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | smart garment domain adaptation Strain sensor CNN-LSTM |
| Language | English |
| License | Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-a314t-854d5a5fcf1845519f5fb1d9ef23fc1e2bfaf7eef2be8d68226f022d59189c963 |
| OpenAccessLink | https://dl.acm.org/doi/10.1145/3584986 |
| PageCount | 23 |
| ParticipantIDs | swepub_primary_oai_DiVA_org_uu_517277 crossref_citationtrail_10_1145_3584986 crossref_primary_10_1145_3584986 acm_primary_3584986 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-11-30 |
| PublicationDateYYYYMMDD | 2023-11-30 |
| PublicationDate_xml | – month: 11 year: 2023 text: 2023-11-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationPlace | New York, NY |
| PublicationPlace_xml | – name: New York, NY |
| PublicationTitle | ACM transactions on sensor networks |
| PublicationTitleAbbrev | ACM TOSN |
| PublicationYear | 2023 |
| Publisher | ACM |
| Publisher_xml | – name: ACM |
| References | Thomas Plötz, Nils Y Hammerla, and Patrick L Olivier. 2011. Feature learning for activity recognition in ubiquitous computing. In Twenty-Second International Joint Conference on Artificial Intelligence. Oscar D Lara and Miguel A Labrador. 2012. A survey on human activity recognition using wearable sensors. IEEE communications surveys & tutorials 15, 3 (2012), 1192–1209. Jung Bin Kim, Jae-Kook Yoo, and Sungwook Yu. 2014. Neck–tongue syndrome precipitated by prolonged poor sitting posture. Neurological Sciences 35, 1 (2014), 121–122. Chaoyi Yan, Jiangxin Wang, Wenbin Kang, Mengqi Cui, Xu Wang, Ce Yao Foo, Kenji Jianzhi Chee, and Pooi See Lee. 2014. Graphene: Highly stretchable piezoresistive graphene–nanocellulose nanopaper for strain sensors (Adv. Mater. 13/2014). Advanced Materials 26, 13 (2014), 1950–1950. Sara Khalifa, Guohao Lan, Mahbub Hassan, Aruna Seneviratne, and Sajal K Das. 2017. Harke: Human activity recognition from kinetic energy harvesting data in wearable devices. IEEE Transactions on Mobile Computing 17, 6 (2017), 1353–1368. Jing Zhao, Congli He, Rong Yang, Zhiwen Shi, Meng Cheng, Wei Yang, Guibai Xie, Duoming Wang, Dongxia Shi, and Guangyu Zhang. 2012. Ultra-sensitive strain sensors based on piezoresistive nanographene films. Applied Physics Letters 101, 6 (2012), 063112. Mohammed Abuhamad, Ahmed Abusnaina, Dae Hun Nyang, and David Mohaisen. 2020. Sensor-based Continuous Authentication of Smartphones’ Users Using Behavioral Biometrics: A Contemporary Survey. IEEE Internet of Things Journal(2020). Xi Chen, Hang Li, Chenyi Zhou, Xue Liu, Di Wu, and Gregory Dudek. 2020. FiDo: Ubiquitous Fine-Grained WiFi-based Localization for Unlabelled Users via Domain Adaptation. In Proceedings of The Web Conference 2020. 23–33. Babak Moradi, Mohammad Aghapour, and Afshin Shirbandi. 2019. Compare of Machine Learning And Deep Learning Approaches for Human Activity Recognition. Technical Report. EasyChair. Jonathan Lester, Blake Hannaford, and Gaetano Borriello. 2004. ?Are You With Me??–Using Accelerometers to Determine if Two Devices are Carried by the Same Person. In International Conference on Pervasive Computing. Springer, 33–50. Holger Harms, Oliver Amft, et al. 2010. Estimating posture-recognition performance in sensing garments using geometric wrinkle modeling. IEEE Transactions on Information Technology in Biomedicine 14, 6(2010), 1436–1445. Guido Gioberto, James Coughlin, Kaila Bibeau, and Lucy E Dunne. 2013. Detecting bends and fabric folds using stitched sensors. In Proceedings of the 2013 International Symposium on Wearable Computers. ACM, 53–56. Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, and Lisha Hu. 2019. Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters 119 (2019), 3–11. Xinming Li, Tingting Yang, Yao Yang, Jia Zhu, Li Li, Fakhr E Alam, Xiao Li, Kunlin Wang, Huanyu Cheng, Cheng-Te Lin, et al. 2016. Large-Area Ultrathin Graphene Films by Single-Step Marangoni Self-Assembly for Highly Sensitive Strain Sensing Application. Advanced Functional Materials 26, 9 (2016), 1322–1329. Alex Hanuska, Bharath Chandramohan, Laura Bellamy, Pauline Burke, Rajiv Ramanathan, and Vijay Balakrishnan. 2016. Smart clothing market analysis. Technical Report. Technical Report. Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz. 2013. A public domain dataset for human activity recognition using smartphones.. In Esann. Yong Lin, Shuqi Liu, Song Chen, Yong Wei, Xuchu Dong, and Lan Liu. 2016. A highly stretchable and sensitive strain sensor based on graphene–elastomer composites with a novel double-interconnected network. Journal of Materials Chemistry C 4, 26 (2016), 6345–6352. Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge L Reyes-Ortiz. 2012. Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In International workshop on ambient assisted living. Springer, 216–223. Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882(2014). Suranga Seneviratne, Yining Hu, Tham Nguyen, Guohao Lan, Sara Khalifa, Kanchana Thilakarathna, Mahbub Hassan, and Aruna Seneviratne. 2017. A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials 19, 4 (2017), 2573–2620. S. Sridhar, P. Misra, G. S. Gill, and J. Warrior. 2016. Cheepsync: a time synchronization service for resource constrained bluetooth le advertisers. IEEE Communications Magazine 54, 1 (2016), 136–143. Ali Kiaghadi, Morgan Baima, Jeremy Gummeson, Trisha Andrew, and Deepak Ganesan. 2018. Fabric as a Sensor: Towards Unobtrusive Sensing of Human Behavior with Triboelectric Textiles. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems. ACM, 199–210. Shuying Wu, Shuhua Peng, Zhao Jun Han, Hongwei Zhu, and Chun H Wang. 2018. Ultrasensitive and stretchable strain sensors based on mazelike vertical graphene network. ACS applied materials & interfaces 10, 42 (2018), 36312–36322. Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Ye Yuan, Hongfei Xue, Chen Song, Xin Ma, Dimitrios Koutsonikolas, et al. 2018. Towards environment independent device free human activity recognition. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. 289–304. Shuying Wu, Raj B Ladani, Jin Zhang, Kamran Ghorbani, Xuehua Zhang, Adrian P Mouritz, Anthony J Kinloch, and Chun H Wang. 2016. Strain sensors with adjustable sensitivity by tailoring the microstructure of graphene aerogel/PDMS nanocomposites. ACS applied materials & interfaces 8, 37 (2016), 24853–24861. Yuqing Chen and Yang Xue. 2015. A deep learning approach to human activity recognition based on single accelerometer. In 2015 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 1488–1492. Tingting Yang, Wen Wang, Hongze Zhang, Xinming Li, Jidong Shi, Yijia He, Quan-shui Zheng, Zhihong Li, and Hongwei Zhu. 2015. Tactile sensing system based on arrays of graphene woven microfabrics: electromechanical behavior and electronic skin application. ACS nano 9, 11 (2015), 10867–10875. Qi Lin, Shuhua Peng, Yuezhong Wu, Jun Liu, Wen Hu, Mahbub Hassan, Aruna Seneviratne, and Chun H Wang. 2020. E-Jacket: Posture Detection with Loose-Fitting Garment using a Novel Strain Sensor. In 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 49–60. Xue-Wen Fu, Zhi-Min Liao, Jian-Xin Zhou, Yang-Bo Zhou, Han-Chun Wu, Rui Zhang, Guangyin Jing, Jun Xu, Xiaosong Wu, Wanlin Guo, et al. 2011. Strain dependent resistance in chemical vapor deposition grown graphene. Applied Physics Letters 99, 21 (2011), 213107. Qiang Liu, Ji Chen, Yingru Li, and Gaoquan Shi. 2016. High-performance strain sensors with fish-scale-like graphene-sensing layers for full-range detection of human motions. ACS nano 10, 8 (2016), 7901–7906. Emilio Sardini, Mauro Serpelloni, and Viviane Pasqui. 2014. Wireless wearable T-shirt for posture monitoring during rehabilitation exercises. IEEE Transactions on Instrumentation and Measurement 64, 2(2014), 439–448. Jianbo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiao Li Li, and Shonali Krishnaswamy. 2015. Deep convolutional neural networks on multichannel time series for human activity recognition. In Twenty-Fourth International Joint Conference on Artificial Intelligence. Yu Ra Jeong, Heun Park, Sang Woo Jin, Soo Yeong Hong, Sang-Soo Lee, and Jeong Sook Ha. 2015. Highly stretchable and sensitive strain sensors using fragmentized graphene foam. Advanced Functional Materials 25, 27 (2015), 4228–4236. Zhihui Zeng, Seyed Ismail Seyed Shahabadi, Boyang Che, Youfang Zhang, Chenyang Zhao, and Xuehong Lu. 2017. Highly stretchable, sensitive strain sensors with a wide linear sensing region based on compressed anisotropic graphene foam/polymer nanocomposites. Nanoscale 9, 44 (2017), 17396–17404. Ming Zeng, Le T Nguyen, Bo Yu, Ole J Mengshoel, Jiang Zhu, Pang Wu, and Joy Zhang. 2014. Convolutional neural networks for human activity recognition using mobile sensors. In 6th International Conference on Mobile Computing, Applications and Services. IEEE, 197–205. Shuhua Peng, Shuying Wu, Yuyan Yu, Philippe Blanloeuil, and Chun H Wang. 2020. Nano-Toughening of Transparent Wearable Sensors with High Sensitivity and Wide Linear Sensing Range. Journal of Materials Chemistry A(2020). Christoph Obermair, Wolfgang Reitberger, Alexander Meschtscherjakov, Michael Lankes, and Manfred Tscheligi. 2008. perFrames: Persuasive picture frames for proper posture. In International conference on persuasive technology. Springer, 128–139. Holger Harms, Oliver Amft, Daniel Roggen, and Gerhard Tröster. 2009. Rapid prototyping of smart garments for activity-aware applications. Journal of Ambient Intelligence and Smart Environments 1, 2(2009), 87–101. Wenchao Jiang and Zhaozheng Yin. 2015. Human activity recognition using wearable sensors by deep convolutional neural networks. In Proceedings of the 23rd ACM international conference on Multimedia. Acm, 1307–1310. My Duyen Ho, Yunzhi Ling, Lim Wei Yap, Yan Wang, Dashen Dong, Yunmeng Zhao, and Wenlong Cheng. 2017. Percolating network of ultrathin gold nanowires and silver nanowires toward “invisible” wearable sensors for detecting emotional expression and apexcardiogram. Advanced Functional Materials 27, 25 (2017), 1700845. Qiang Liu, Miao Zhang, Liang Huang, Yingru Li, Ji Chen, Chun Li, and Gaoquan Shi. 2015. High-quality graphene ribbons prepared from graphene oxide hydrogels and their application for strain sensors. ACS nano 9, 12 (2015), 12320–12326. Juan C Nunez, Raul Cabido, Juan J Pantrigo, Antonio S Montemayor, and Jose F Velez. 2018. Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. Pattern Recognition 76(2018), 80–94. Corinne Mattmann, Oliver Amft, Holger Harms, Gerhard Troster, and Frank Clemens. 2007. Recognizing upper body postures using textile e_1_3_2_26_2 e_1_3_2_27_2 e_1_3_2_28_2 Anguita Davide (e_1_3_2_6_2) 2013; 3 e_1_3_2_29_2 e_1_3_2_41_2 e_1_3_2_40_2 e_1_3_2_20_2 e_1_3_2_43_2 Harms Holger (e_1_3_2_13_2) 2009; 1 e_1_3_2_21_2 e_1_3_2_42_2 e_1_3_2_22_2 e_1_3_2_45_2 e_1_3_2_23_2 e_1_3_2_44_2 e_1_3_2_24_2 e_1_3_2_25_2 e_1_3_2_46_2 Anguita Davide (e_1_3_2_5_2) 2012 Plötz Thomas (e_1_3_2_34_2) 2011 Abuhamad Mohammed (e_1_3_2_3_2) 2020; 8 e_1_3_2_9_2 e_1_3_2_15_2 e_1_3_2_38_2 e_1_3_2_8_2 e_1_3_2_16_2 e_1_3_2_37_2 e_1_3_2_7_2 e_1_3_2_17_2 e_1_3_2_18_2 e_1_3_2_39_2 e_1_3_2_19_2 e_1_3_2_30_2 e_1_3_2_32_2 e_1_3_2_10_2 e_1_3_2_11_2 e_1_3_2_4_2 e_1_3_2_12_2 e_1_3_2_33_2 e_1_3_2_36_2 e_1_3_2_2_2 e_1_3_2_14_2 e_1_3_2_35_2 Entrepreneurship Pantas, T. S. C. for (e_1_3_2_31_2) 2022 |
| References_xml | – reference: Jung Bin Kim, Jae-Kook Yoo, and Sungwook Yu. 2014. Neck–tongue syndrome precipitated by prolonged poor sitting posture. Neurological Sciences 35, 1 (2014), 121–122. – reference: Shuhua Peng, Shuying Wu, Yuyan Yu, Philippe Blanloeuil, and Chun H Wang. 2020. Nano-Toughening of Transparent Wearable Sensors with High Sensitivity and Wide Linear Sensing Range. Journal of Materials Chemistry A(2020). – reference: IEEE 802 Working Group et al. 2011. IEEE Standard for Local and Metropolitan Area Networks?Part 15.4: Low-Rate Wireless Personal Area Networks (LR-WPANs). IEEE Std 802(2011), 4–2011. – reference: Alex Hanuska, Bharath Chandramohan, Laura Bellamy, Pauline Burke, Rajiv Ramanathan, and Vijay Balakrishnan. 2016. Smart clothing market analysis. Technical Report. Technical Report. – reference: Xi Chen, Hang Li, Chenyi Zhou, Xue Liu, Di Wu, and Gregory Dudek. 2020. FiDo: Ubiquitous Fine-Grained WiFi-based Localization for Unlabelled Users via Domain Adaptation. In Proceedings of The Web Conference 2020. 23–33. – reference: Xinming Li, Tingting Yang, Yao Yang, Jia Zhu, Li Li, Fakhr E Alam, Xiao Li, Kunlin Wang, Huanyu Cheng, Cheng-Te Lin, et al. 2016. Large-Area Ultrathin Graphene Films by Single-Step Marangoni Self-Assembly for Highly Sensitive Strain Sensing Application. Advanced Functional Materials 26, 9 (2016), 1322–1329. – reference: Emilio Sardini, Mauro Serpelloni, and Viviane Pasqui. 2014. Wireless wearable T-shirt for posture monitoring during rehabilitation exercises. IEEE Transactions on Instrumentation and Measurement 64, 2(2014), 439–448. – reference: Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Ye Yuan, Hongfei Xue, Chen Song, Xin Ma, Dimitrios Koutsonikolas, et al. 2018. Towards environment independent device free human activity recognition. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. 289–304. – reference: Yong Lin, Shuqi Liu, Song Chen, Yong Wei, Xuchu Dong, and Lan Liu. 2016. A highly stretchable and sensitive strain sensor based on graphene–elastomer composites with a novel double-interconnected network. Journal of Materials Chemistry C 4, 26 (2016), 6345–6352. – reference: Qi Lin, Shuhua Peng, Yuezhong Wu, Jun Liu, Wen Hu, Mahbub Hassan, Aruna Seneviratne, and Chun H Wang. 2020. E-Jacket: Posture Detection with Loose-Fitting Garment using a Novel Strain Sensor. In 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 49–60. – reference: Qiang Liu, Ji Chen, Yingru Li, and Gaoquan Shi. 2016. High-performance strain sensors with fish-scale-like graphene-sensing layers for full-range detection of human motions. ACS nano 10, 8 (2016), 7901–7906. – reference: Mohammed Abuhamad, Ahmed Abusnaina, Dae Hun Nyang, and David Mohaisen. 2020. Sensor-based Continuous Authentication of Smartphones’ Users Using Behavioral Biometrics: A Contemporary Survey. IEEE Internet of Things Journal(2020). – reference: Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge L Reyes-Ortiz. 2012. Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In International workshop on ambient assisted living. Springer, 216–223. – reference: Ali Kiaghadi, Morgan Baima, Jeremy Gummeson, Trisha Andrew, and Deepak Ganesan. 2018. Fabric as a Sensor: Towards Unobtrusive Sensing of Human Behavior with Triboelectric Textiles. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems. ACM, 199–210. – reference: Shuying Wu, Shuhua Peng, Zhao Jun Han, Hongwei Zhu, and Chun H Wang. 2018. Ultrasensitive and stretchable strain sensors based on mazelike vertical graphene network. ACS applied materials & interfaces 10, 42 (2018), 36312–36322. – reference: Zhihui Zeng, Seyed Ismail Seyed Shahabadi, Boyang Che, Youfang Zhang, Chenyang Zhao, and Xuehong Lu. 2017. Highly stretchable, sensitive strain sensors with a wide linear sensing region based on compressed anisotropic graphene foam/polymer nanocomposites. Nanoscale 9, 44 (2017), 17396–17404. – reference: Guido Gioberto, James Coughlin, Kaila Bibeau, and Lucy E Dunne. 2013. Detecting bends and fabric folds using stitched sensors. In Proceedings of the 2013 International Symposium on Wearable Computers. ACM, 53–56. – reference: Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882(2014). – reference: Holger Harms, Oliver Amft, Daniel Roggen, and Gerhard Tröster. 2009. Rapid prototyping of smart garments for activity-aware applications. Journal of Ambient Intelligence and Smart Environments 1, 2(2009), 87–101. – reference: Corinne Mattmann, Oliver Amft, Holger Harms, Gerhard Troster, and Frank Clemens. 2007. Recognizing upper body postures using textile strain sensors. In 2007 11th IEEE international symposium on wearable computers. IEEE, 29–36. – reference: Sara Khalifa, Guohao Lan, Mahbub Hassan, Aruna Seneviratne, and Sajal K Das. 2017. Harke: Human activity recognition from kinetic energy harvesting data in wearable devices. IEEE Transactions on Mobile Computing 17, 6 (2017), 1353–1368. – reference: Oscar D Lara and Miguel A Labrador. 2012. A survey on human activity recognition using wearable sensors. IEEE communications surveys & tutorials 15, 3 (2012), 1192–1209. – reference: Christoph Obermair, Wolfgang Reitberger, Alexander Meschtscherjakov, Michael Lankes, and Manfred Tscheligi. 2008. perFrames: Persuasive picture frames for proper posture. In International conference on persuasive technology. Springer, 128–139. – reference: Juan C Nunez, Raul Cabido, Juan J Pantrigo, Antonio S Montemayor, and Jose F Velez. 2018. Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. Pattern Recognition 76(2018), 80–94. – reference: Chaoyi Yan, Jiangxin Wang, Wenbin Kang, Mengqi Cui, Xu Wang, Ce Yao Foo, Kenji Jianzhi Chee, and Pooi See Lee. 2014. Graphene: Highly stretchable piezoresistive graphene–nanocellulose nanopaper for strain sensors (Adv. Mater. 13/2014). Advanced Materials 26, 13 (2014), 1950–1950. – reference: Wenchao Jiang and Zhaozheng Yin. 2015. Human activity recognition using wearable sensors by deep convolutional neural networks. In Proceedings of the 23rd ACM international conference on Multimedia. Acm, 1307–1310. – reference: Ming Zeng, Le T Nguyen, Bo Yu, Ole J Mengshoel, Jiang Zhu, Pang Wu, and Joy Zhang. 2014. Convolutional neural networks for human activity recognition using mobile sensors. In 6th International Conference on Mobile Computing, Applications and Services. IEEE, 197–205. – reference: My Duyen Ho, Yunzhi Ling, Lim Wei Yap, Yan Wang, Dashen Dong, Yunmeng Zhao, and Wenlong Cheng. 2017. Percolating network of ultrathin gold nanowires and silver nanowires toward “invisible” wearable sensors for detecting emotional expression and apexcardiogram. Advanced Functional Materials 27, 25 (2017), 1700845. – reference: Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. 2015. Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2625–2634. – reference: Holger Harms, Oliver Amft, et al. 2010. Estimating posture-recognition performance in sensing garments using geometric wrinkle modeling. IEEE Transactions on Information Technology in Biomedicine 14, 6(2010), 1436–1445. – reference: Babak Moradi, Mohammad Aghapour, and Afshin Shirbandi. 2019. Compare of Machine Learning And Deep Learning Approaches for Human Activity Recognition. Technical Report. EasyChair. – reference: Jung Jin Park, Woo Jin Hyun, Sung Cik Mun, Yong Tae Park, and O Ok Park. 2015. Highly stretchable and wearable graphene strain sensors with controllable sensitivity for human motion monitoring. ACS applied materials & interfaces 7, 11 (2015), 6317–6324. – reference: Xue-Wen Fu, Zhi-Min Liao, Jian-Xin Zhou, Yang-Bo Zhou, Han-Chun Wu, Rui Zhang, Guangyin Jing, Jun Xu, Xiaosong Wu, Wanlin Guo, et al. 2011. Strain dependent resistance in chemical vapor deposition grown graphene. Applied Physics Letters 99, 21 (2011), 213107. – reference: Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge Luis Reyes-Ortiz. 2013. A public domain dataset for human activity recognition using smartphones.. In Esann. – reference: Jonathan Lester, Blake Hannaford, and Gaetano Borriello. 2004. ?Are You With Me??–Using Accelerometers to Determine if Two Devices are Carried by the Same Person. In International Conference on Pervasive Computing. Springer, 33–50. – reference: Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, and Lisha Hu. 2019. Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters 119 (2019), 3–11. – reference: Yu Ra Jeong, Heun Park, Sang Woo Jin, Soo Yeong Hong, Sang-Soo Lee, and Jeong Sook Ha. 2015. Highly stretchable and sensitive strain sensors using fragmentized graphene foam. Advanced Functional Materials 25, 27 (2015), 4228–4236. – reference: Thomas Plötz, Nils Y Hammerla, and Patrick L Olivier. 2011. Feature learning for activity recognition in ubiquitous computing. In Twenty-Second International Joint Conference on Artificial Intelligence. – reference: Suranga Seneviratne, Yining Hu, Tham Nguyen, Guohao Lan, Sara Khalifa, Kanchana Thilakarathna, Mahbub Hassan, and Aruna Seneviratne. 2017. A survey of wearable devices and challenges. IEEE Communications Surveys & Tutorials 19, 4 (2017), 2573–2620. – reference: Shuying Wu, Raj B Ladani, Jin Zhang, Kamran Ghorbani, Xuehua Zhang, Adrian P Mouritz, Anthony J Kinloch, and Chun H Wang. 2016. Strain sensors with adjustable sensitivity by tailoring the microstructure of graphene aerogel/PDMS nanocomposites. ACS applied materials & interfaces 8, 37 (2016), 24853–24861. – reference: Jianbo Yang, Minh Nhut Nguyen, Phyo Phyo San, Xiao Li Li, and Shonali Krishnaswamy. 2015. Deep convolutional neural networks on multichannel time series for human activity recognition. In Twenty-Fourth International Joint Conference on Artificial Intelligence. – reference: Qiang Liu, Miao Zhang, Liang Huang, Yingru Li, Ji Chen, Chun Li, and Gaoquan Shi. 2015. High-quality graphene ribbons prepared from graphene oxide hydrogels and their application for strain sensors. ACS nano 9, 12 (2015), 12320–12326. – reference: Yuqing Chen and Yang Xue. 2015. A deep learning approach to human activity recognition based on single accelerometer. In 2015 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 1488–1492. – reference: Jing Zhao, Congli He, Rong Yang, Zhiwen Shi, Meng Cheng, Wei Yang, Guibai Xie, Duoming Wang, Dongxia Shi, and Guangyu Zhang. 2012. Ultra-sensitive strain sensors based on piezoresistive nanographene films. Applied Physics Letters 101, 6 (2012), 063112. – reference: Tingting Yang, Wen Wang, Hongze Zhang, Xinming Li, Jidong Shi, Yijia He, Quan-shui Zheng, Zhihong Li, and Hongwei Zhu. 2015. Tactile sensing system based on arrays of graphene woven microfabrics: electromechanical behavior and electronic skin application. ACS nano 9, 11 (2015), 10867–10875. – reference: S. Sridhar, P. Misra, G. S. Gill, and J. Warrior. 2016. Cheepsync: a time synchronization service for resource constrained bluetooth le advertisers. IEEE Communications Magazine 54, 1 (2016), 136–143. – ident: e_1_3_2_33_2 doi: 10.1039/D0TA05129B – volume-title: Smart clothing market analysis year: 2022 ident: e_1_3_2_31_2 – ident: e_1_3_2_35_2 doi: 10.1109/TIM.2014.2343411 – ident: e_1_3_2_17_2 doi: 10.1145/3241539.3241548 – volume-title: Proceedings of the 22nd International Joint Conference on Artificial Intelligence year: 2011 ident: e_1_3_2_34_2 – ident: e_1_3_2_29_2 doi: 10.1016/j.patcog.2017.10.033 – ident: e_1_3_2_14_2 doi: 10.1002/adfm.201700845 – ident: e_1_3_2_19_2 doi: 10.1145/3274783.3274845 – ident: e_1_3_2_27_2 doi: 10.1021/acsnano.5b05609 – ident: e_1_3_2_22_2 doi: 10.1007/978-3-540-24646-6_3 – ident: e_1_3_2_26_2 doi: 10.1021/acsnano.6b03813 – volume: 8 start-page: 65 issue: 1 year: 2020 ident: e_1_3_2_3_2 article-title: Sensor-based continuous authentication of smartphones’ users using behavioral biometrics: A contemporary survey publication-title: IEEE IoT J. – ident: e_1_3_2_32_2 doi: 10.1021/acsami.5b00695 – ident: e_1_3_2_39_2 doi: 10.1021/acsami.6b06012 – ident: e_1_3_2_18_2 doi: 10.1109/TMC.2017.2761744 – ident: e_1_3_2_4_2 – start-page: 216 volume-title: Ambient Assisted Living and Home Care: 4th International Workshop (IWAAL’12) year: 2012 ident: e_1_3_2_5_2 doi: 10.1007/978-3-642-35395-6_30 – ident: e_1_3_2_21_2 doi: 10.1109/SURV.2012.110112.00192 – ident: e_1_3_2_46_2 doi: 10.1063/1.4742331 – ident: e_1_3_2_37_2 doi: 10.1109/MCOM.2016.7378439 – ident: e_1_3_2_38_2 doi: 10.1016/j.patrec.2018.02.010 – ident: e_1_3_2_12_2 doi: 10.1109/TITB.2010.2076822 – start-page: 1 ident: e_1_3_2_2_2 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 publication-title: IEEE P802.15.4m/D4, October 2013 – ident: e_1_3_2_42_2 doi: 10.5555/2832747.2832806 – ident: e_1_3_2_23_2 doi: 10.1002/adfm.201504717 – ident: e_1_3_2_25_2 doi: 10.1039/C6TC01925K – ident: e_1_3_2_30_2 doi: 10.1007/978-3-540-68504-3_12 – ident: e_1_3_2_36_2 doi: 10.1109/COMST.2017.2731979 – ident: e_1_3_2_7_2 doi: 10.1145/3366423.3380091 – ident: e_1_3_2_20_2 doi: 10.1007/s10072-013-1507-5 – ident: e_1_3_2_28_2 doi: 10.1109/ISWC.2007.4373773 – ident: e_1_3_2_40_2 doi: 10.1021/acsami.8b15848 – ident: e_1_3_2_45_2 doi: 10.1039/C7NR05106A – ident: e_1_3_2_15_2 doi: 10.1002/adfm.201501000 – ident: e_1_3_2_43_2 doi: 10.1021/acsnano.5b03851 – ident: e_1_3_2_10_2 doi: 10.1063/1.3663969 – ident: e_1_3_2_8_2 doi: 10.1109/SMC.2015.263 – ident: e_1_3_2_11_2 doi: 10.1145/2493988.2494355 – ident: e_1_3_2_24_2 doi: 10.1109/IPSN48710.2020.00-47 – ident: e_1_3_2_16_2 doi: 10.1145/2733373.2806333 – volume: 1 start-page: 87 issue: 2 year: 2009 ident: e_1_3_2_13_2 article-title: Rapid prototyping of smart garments for activity-aware applications publication-title: J. Amb. Intell. Smart Environ. – ident: e_1_3_2_41_2 doi: 10.1002/adma.201470083 – ident: e_1_3_2_44_2 doi: 10.4108/icst.mobicase.2014.257786 – volume: 3 start-page: 3 year: 2013 ident: e_1_3_2_6_2 article-title: A public domain dataset for human activity recognition using smartphones publication-title: Esann – ident: e_1_3_2_9_2 doi: 10.1109/CVPR.2015.7298878 |
| SSID | ssj0033430 |
| Score | 2.3698773 |
| Snippet | The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human-computer-interaction.... The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human–computer interaction.... The ability to recognize and detect changes in human posture is important in awide range of applications such as health care and human-computer interaction.... |
| SourceID | swepub crossref acm |
| SourceType | Open Access Repository Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | CNN-LSTM Computing methodologies domain adaptation Human-centered computing Machine learning smart garment Strain sensor Ubiquitous and mobile computing |
| SubjectTermsDisplay | Computing methodologies -- Machine learning Human-centered computing -- Ubiquitous and mobile computing |
| Title | Subject-Adaptive Loose-Fitting Smart Garment Platform for Human Activity Recognition |
| URI | https://dl.acm.org/doi/10.1145/3584986 https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-517277 |
| Volume | 19 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Nb9MwFLegu8ABwQBRvuQDcEGGJrab5FhtwIRaBKxDu0V2Yq-VtmTqksv-ep6_krSbxMclSi3bUv1-fn7Pee_3EHqTKFZyTTmZgnQJK-AtlZkmWouSMRmV3JI9L75Nj07Y11N-2ocE2eySRn4orm_NK_kfqUIbyNVkyf6DZLtJoQHeQb7wBAnD869kDLveXKMQUYpLGwI0r-srRfTaBTMfX8CY91_Exn7w_34uGmOh2sBCd3c_K3ztiJ8hjMgLKdDSHixMDYlQUNx-WbgCvxcmqFz4eGeRzx0XwY91r2v9RfSqXbW97m-tzm_V9ar2Z6Yd3LoMkWp4CRHTQH7Y600-ISz15N5q2OaqbXTKNhuAig00ZzQ4gl0G8k3lzgwPBgWTKbuNPnvnWOuCDV3qNc_9wLtoLwaXIh6hvdnhYn4czm1Kma1M0_0Vl2Jthn70Q40FU1xsWTBb_LLWJlk-RA-8M4FnDhmP0B1V7aP7A4rJx2i5ixG8hRFsMYI9RnDACIYHthjBASN4gJEn6OTzp-XBEfGVNIigEWtIymFHCq4LDQ492MiZ5hr2YaZ0THURqVhqoRMFP6VKyykYjVMNxl3JsyjNCtjFT9Goqiv1DGGqFQUvV0yUSBmjUkaJApdVJ0JPuGTlGO3DGuWXjislLPoYvQtrlheefN7UQDnPd6QzRrjrGOa40eWtW_Sug6FIP1z_muX15ixv25wbqzx5_uepXqB7PZpfolGzadUrMDIb-dqj4zfqDH6b |
| linkProvider | EBSCOhost |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Subject-adaptive+Loose-fitting+Smart+Garment+Platform+for+Human+Activity+Recognition&rft.jtitle=ACM+transactions+on+sensor+networks&rft.au=Lin%2C+Qi&rft.au=Peng%2C+Shuhua&rft.au=Wu%2C+Yuezhong&rft.au=Liu%2C+Jun&rft.date=2023-11-30&rft.issn=1550-4859&rft.eissn=1550-4867&rft.volume=19&rft.issue=4&rft.spage=1&rft.epage=23&rft_id=info:doi/10.1145%2F3584986&rft.externalDBID=n%2Fa&rft.externalDocID=10_1145_3584986 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1550-4859&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1550-4859&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1550-4859&client=summon |