{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T22:32:50Z","timestamp":1764714770354,"version":"build-2065373602"},"reference-count":73,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2016,8,20]],"date-time":"2016-08-20T00:00:00Z","timestamp":1471651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572342","61303206"],"award-info":[{"award-number":["61572342","61303206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20151240","BK20161258"],"award-info":[{"award-number":["BK20151240","BK20161258"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2015M580470","2016M591920"],"award-info":[{"award-number":["2015M580470","2016M591920"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Priority Academic Program Development of Jiangsu Higer Education Institutions"},{"name":"Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As the size of smartphone touchscreens has become larger and larger in recent years, operability with a single hand is getting worse, especially for female users. We envision that user experience can be significantly improved if smartphones are able to recognize the current operating hand, detect the hand-changing process and then adjust the user interfaces subsequently. In this paper, we proposed, implemented and evaluated two novel systems. The first one leverages the user-generated touchscreen traces to recognize the current operating hand, and the second one utilizes the accelerometer and gyroscope data of all kinds of activities in the user\u2019s daily life to detect the hand-changing process. These two systems are based on two supervised classifiers constructed from a series of refined touchscreen trace, accelerometer and gyroscope features. As opposed to existing solutions that all require users to select the current operating hand or confirm the hand-changing process manually, our systems follow much more convenient and practical methods and allow users to change the operating hand frequently without any harm to the user experience. We conduct extensive experiments on Samsung Galaxy S4 smartphones, and the evaluation results demonstrate that our proposed systems can recognize the current operating hand and detect the hand-changing process with 94.1% and 93.9% precision and 94.1% and 93.7% True Positive Rates (TPR) respectively, when deciding with a single touchscreen trace or accelerometer-gyroscope data segment, and the False Positive Rates (FPR) are as low as 2.6% and 0.7% accordingly. These two systems can either work completely independently and achieve pretty high accuracies or work jointly to further improve the recognition accuracy.<\/jats:p>","DOI":"10.3390\/s16081314","type":"journal-article","created":{"date-parts":[[2016,8,22]],"date-time":"2016-08-22T10:40:33Z","timestamp":1471862433000},"page":"1314","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Recognizing the Operating Hand and the Hand-Changing Process for User Interface Adjustment on Smartphones"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6418-4684","authenticated-orcid":false,"given":"Hansong","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China"}]},{"given":"He","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Soochow 215000, China"}]},{"given":"Liusheng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei 230000, China"}]},{"given":"Yu-E","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Urban Rail Transportation, Soochow University, Soochow 215000, China"},{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210000, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,20]]},"reference":[{"key":"ref_1","unstructured":"iPhone. Available online: https:\/\/www.apple.com."},{"key":"ref_2","unstructured":"Nokia. Available online: http:\/\/www.microsoftstore.com."},{"key":"ref_3","unstructured":"Samsung. Available online: http:\/\/www.samsung.com."},{"key":"ref_4","unstructured":"Guo, H., Huang, H., Sun, Z., Huang, L., Zhu, Z., Wang, S., Wang, P., Xu, H., and Liu, H. (2015). Knowledge Science, Engineering and Management, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref_7","unstructured":"Safavian, S.R., and Landgrebe, D. (1990). A Survey of Decision Tree Classifier Methodology, NASA Technical Reports Server."},{"key":"ref_8","first-page":"124","article-title":"The alternating decision tree learning algorithm","volume":"Volume 99","author":"Freund","year":"1999","journal-title":"Sixteenth International Conference on Machine Learning"},{"key":"ref_9","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_11","unstructured":"Rish, I. (2001, January 4\u201310). An empirical study of the naive Bayes classifier. Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), Seattle, WA, USA."},{"key":"ref_12","unstructured":"Zhang, H. (2004, January 17\u201319). The optimality of naive Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, Miami Beach, FL, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/72.159058","article-title":"Multilayer perceptron, fuzzy sets, and classification","volume":"3","author":"Pal","year":"1992","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_14","first-page":"40","article-title":"Feature selection using a multilayer perceptron","volume":"2","author":"Ruck","year":"1990","journal-title":"J. Neural Netw. Comput."},{"key":"ref_15","unstructured":"Laaksonen, J., and Oja, E. (1996, January 3\u20136). Classification with learning k-nearest neighbors. Proceedings of the IEEE International Conference on Neural Networks, Washington, DC, USA."},{"key":"ref_16","first-page":"961","article-title":"A fast K nearest neighbors classification algorithm","volume":"87","author":"Pan","year":"2004","journal-title":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci."},{"key":"ref_17","unstructured":"Ravi, N., Dandekar, N., Mysore, P., and Littman, M.L. (2005, January 9\u201313). Activity recognition from accelerometer data. Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence (AAAI), Pittsburgh, PA, USA."},{"key":"ref_18","unstructured":"Bao, L., and Intille, S.S. (2004). Pervasive Computing, Springer."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e130","DOI":"10.2196\/jmir.2208","article-title":"Classification accuracies of physical activities using smartphone motion sensors","volume":"14","author":"Wu","year":"2012","journal-title":"J. Med. Internet Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s11036-008-0112-y","article-title":"An activity recognition system for mobile phones","volume":"14","year":"2009","journal-title":"Mob. Netw. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Eren, H., Makinist, S., Akin, E., and Yilmaz, A. (2012, January 3\u20137). Estimating driving behavior by a smartphone. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Alcala de Henares, Spain.","DOI":"10.1109\/IVS.2012.6232298"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.datak.2010.01.004","article-title":"An unsupervised approach to activity recognition and segmentation based on object-use fingerprints","volume":"69","author":"Gu","year":"2010","journal-title":"Data Knowl. Eng."},{"key":"ref_24","unstructured":"Balakrishnama, S., and Ganapathiraju, A. (1998). Linear Discriminant Analysis\u2014A Brief Tutorial, Institute for Signal and Information Processing."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Haeb-Umbach, R., and Ney, H. (1992, January 23\u201326). Linear discriminant analysis for improved large vocabulary continuous speech recognition. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-92), San Francisco, CA, USA.","DOI":"10.1109\/ICASSP.1992.225984"},{"key":"ref_26","unstructured":"Ye, J., Janardan, R., and Li, Q. (2004, January 13\u201318). Two-dimensional linear discriminant analysis. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_27","unstructured":"Jolliffe, I. (2002). Principal Component Analysis, Wiley Online Library."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"Volume 2","author":"Abdi","year":"2010","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M. (2007). Information Retrieval for Music and Motion, Springer-Verlag.","DOI":"10.1007\/978-3-540-74048-3"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1007\/s10115-004-0154-9","article-title":"Exact indexing of dynamic time warping","volume":"7","author":"Keogh","year":"2005","journal-title":"Knowl. Inf. Syst."},{"key":"ref_32","unstructured":"Berndt, D.J., and Clifford, J. (1994, January 25). Using Dynamic Time Warping to Find Patterns in Time Series. Proceedings of the Knowledge Discovery in Databases (KDD), Seattle, WA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"961","DOI":"10.3233\/IFS-141378","article-title":"Image segmentation by generalized hierarchical fuzzy C-means algorithm","volume":"28","author":"Zheng","year":"2015","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.ins.2014.10.040","article-title":"A rapid learning algorithm for vehicle classification","volume":"295","author":"Wen","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1109\/TNNLS.2014.2342533","article-title":"Incremental support vector learning for ordinal regression","volume":"26","author":"Gu","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_36","first-page":"1","article-title":"Structural minimax probability machine","volume":"1","author":"Gu","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.neunet.2015.03.013","article-title":"Incremental learning for \u03bd-support vector regression","volume":"67","author":"Gu","year":"2015","journal-title":"Neural Netw."},{"key":"ref_38","first-page":"1","article-title":"A robust regularization path algorithm for \u03bd-support vector classification","volume":"1","author":"Gu","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TIFS.2014.2381872","article-title":"Segmentation-based image copy-move forgery detection scheme","volume":"10","author":"Li","year":"2015","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1002\/sec.864","article-title":"Steganalysis of least significant bit matching using multi-order differences","volume":"7","author":"Xia","year":"2014","journal-title":"Secur. Commun. Netw."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sae-Bae, N., Ahmed, K., Isbister, K., and Memon, N. (2012, January 5\u201310). Biometric-rich gestures: A novel approach to authentication on multi-touch devices. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), Austin, TX, USA.","DOI":"10.1145\/2207676.2208543"},{"key":"ref_42","unstructured":"Shahzad, M., Liu, A.X., and Samuel, A. (October, January 30). Secure unlocking of mobile touch screen devices by simple gestures: you can see it but you can not do it. Proceedings of the 19th Annual International Conference on Mobile Computing and Networking, Miami, FL, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"De Luca, A., Hang, A., Brudy, F., Lindner, C., and Hussmann, H. (2012, January 5\u201310). Touch me once and I know it\u2019s you!: Implicit authentication based on touch screen patterns. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), Austin, TX, USA.","DOI":"10.1145\/2207676.2208544"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Seo, H., Kim, E., and Kim, H.K. (2012). A novel biometric identification based on a users input pattern analysis for intelligent mobile devices. Int. J. Adv. Robot. Syst., 9.","DOI":"10.5772\/51319"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Vu, T., Baid, A., Gao, S., Gruteser, M., Howard, R., Lindqvist, J., Spasojevic, P., and Walling, J. (2012, January 22\u201326). Distinguishing users with capacitive touch communication. Proceedings of the 18th Annual International Conference on Mobile Computing and Networking (Mobicom), Istanbul, Turkey.","DOI":"10.1145\/2348543.2348569"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Goel, M., Wobbrock, J., and Patel, S. (2012, January 7\u201310). GripSense: Using built-in sensors to detect hand posture and pressure on commodity mobile phones. Proceedings of the 25th ACM Uist Symposium (UIST), Cambridge, MA, USA.","DOI":"10.1145\/2380116.2380184"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/TIFS.2012.2225048","article-title":"Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication","volume":"8","author":"Frank","year":"2013","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_48","unstructured":"Bo, C., Zhang, L., Li, X.Y., Huang, Q., and Wang, Y. (October, January 30). Silentsense: Silent user identification via touch and movement behavioral biometrics. Proceedings of the 19th Annual International Conference on Mobile Computing  Networking, Miami, FL, USA."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zheng, N., Bai, K., Huang, H., and Wang, H. (2014, January 21\u201324). You are how you touch: User verification on smartphones via tapping behaviors. Proceedings of the IEEE 22nd International Conference on Network Protocols (ICNP), Raleigh, NC, USA.","DOI":"10.1109\/ICNP.2014.43"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Shi, W., Yang, J., Jiang, Y., Yang, F., and Xiong, Y. (2011, January 10\u201312). Senguard: Passive user identification on smartphones using multiple sensors. Proceedings of the IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Wuhan, China.","DOI":"10.1109\/WiMOB.2011.6085412"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Feng, T., Liu, Z., Kwon, K.A., Shi, W., Carbunar, B., Jiang, Y., and Nguyen, N. (2012, January 13\u201315). Continuous mobile authentication using touchscreen gestures. Proceedings of the IEEE Conference on Technologies for Homeland Security (HST), Waltham, MA, USA.","DOI":"10.1109\/THS.2012.6459891"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Miluzzo, E., Varshavsky, A., Balakrishnan, S., and Choudhury, R.R. (2012, January 25\u201329). Tapprints: Your finger taps have fingerprints. Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys), Lake District, UK.","DOI":"10.1145\/2307636.2307666"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xu, Z., Bai, K., and Zhu, S. (2012, January 16\u201318). Taplogger: Inferring user inputs on smartphone touchscreens using on-board motion sensors. Proceedings of the Fifth ACM Conference on Security and Privacy in Wireless and Mobile Networks (WISEC), Tucson, AZ, USA.","DOI":"10.1145\/2185448.2185465"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.pmcj.2012.08.003","article-title":"A smartphone-based fall detection system","volume":"8","author":"Abbate","year":"2012","journal-title":"Pervasive Mob. Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13638-016-0603-7","article-title":"SmartLoc: Sensing landmarks silently for smartphone-based metropolitan localization","volume":"2016","author":"Bo","year":"2016","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Dai, J., Teng, J., Bai, X., Shen, Z., and Xuan, D. (2010, January 22\u201325). Mobile phone based drunk driving detection. Proceedings of the 4th International Conference on Pervasive Computing Technologies for Healthcare, Munich, Germany.","DOI":"10.4108\/ICST.PERVASIVEHEALTH2010.8901"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Bhoraskar, R., Vankadhara, N., Raman, B., and Kulkarni, P. (2012, January 3\u20137). Wolverine: Traffic and road condition estimation using smartphone sensors. Proceedings of the Fourth International Conference on Communication Systems and Networks (COMSNETS), Bangalore, India.","DOI":"10.1109\/COMSNETS.2012.6151382"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A survey on human activity recognition using wearable sensors","volume":"15","author":"Lara","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Parate, A., Chiu, M.C., Chadowitz, C., Ganesan, D., and Kalogerakis, E. (2014, January 25\u201329). Risq: Recognizing smoking gestures with inertial sensors on a wristband. Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, Lake District, UK.","DOI":"10.1145\/2594368.2594379"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ladha, C., Hammerla, N.Y., Olivier, P., and Pl\u00f6tz, T. (2013, January 8\u201312). ClimbAX: Skill assessment for climbing enthusiasts. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, Zurich, Switzerland.","DOI":"10.1145\/2493432.2493492"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tapia, E.M., Intille, S.S., Haskell, W., Larson, K., Wright, J., King, A., and Friedman, R. (2007, January 11\u201313). Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. Proceedings of the 11th IEEE International Symposium on Wearable Computers, Boston, MA, USA.","DOI":"10.1109\/ISWC.2007.4373774"},{"key":"ref_62","unstructured":"Bo, C., Jian, X., Li, X.Y., Mao, X., Wang, Y., and Li, F. (October, January 30). You\u2019re driving and texting: detecting drivers using personal smart phones by leveraging inertial sensors. Proceedings of the 19th Annual International Conference on Mobile Computing  Networking, Miami, FL, USA."},{"key":"ref_63","unstructured":"Song, H., Liu, H., and Chen, D. (2011, January 10\u201312). An automatic gui adjustment method for mobile computing. Proceedings of the IEEE International Conference on Computer Science and Automation Engineering (CSAE), Shanghai, China."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hu, P., Shen, G., Li, L., and Lu, D. (2013, January 25\u201328). ViRi: View it right. Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services, Taipei, Taiwan.","DOI":"10.1145\/2462456.2464454"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"He, S., Liu, Y., and Zhou, H. (2015, January 7\u201311). Optimizing smartphone power consumption through dynamic resolution scaling. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, Paris, France.","DOI":"10.1145\/2789168.2790117"},{"key":"ref_66","unstructured":"Komine, S., and Nakanishi, M. (2013). Human Interface and the Management of Information, Springer."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Alonso-R\u00edos, D., Raneburger, D., Popp, R., Kaindl, H., and Falb, J. (2014, January 24\u201328). A user study on tailoring GUIs for smartphones. Proceedings of the 29th Annual ACM Symposium on Applied Computing, Gyeongju, Korea.","DOI":"10.1145\/2554850.2555085"},{"key":"ref_68","unstructured":"Smartisan. Available online: http:\/\/www.smartisan.com."},{"key":"ref_69","unstructured":"Baidu Input Method. Available online: http:\/\/srf.baidu.com."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"22616","DOI":"10.3390\/s150922616","article-title":"Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches","volume":"15","author":"Rawassizadeh","year":"2015","journal-title":"Sensors"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Ben Abdesslem, F., Phillips, A., and Henderson, T. (2009, January 16\u201321). Less is more: Energy-efficient mobile sensing with senseless. Proceedings of the 1st ACM Workshop on Networking, Systems, and Applications for Mobile Handhelds, Barcelona, Spain.","DOI":"10.1145\/1592606.1592621"},{"key":"ref_72","unstructured":"K\u00f6nig, I., Memon, A.Q., and David, K. (2013, January 27\u201330). Energy consumption of the sensors of Smartphones. Proceedings of the Tenth International Symposium on Wireless Communication Systems (ISWCS 2013), Ilmenau, Germany."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Hao, T., Xing, G., and Zhou, G. (2013, January 11\u201315). iSleep: Unobtrusive sleep quality monitoring using smartphones. Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, Roma, Italy.","DOI":"10.1145\/2517351.2517359"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/8\/1314\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:28:51Z","timestamp":1760210931000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/8\/1314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,20]]},"references-count":73,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2016,8]]}},"alternative-id":["s16081314"],"URL":"https:\/\/doi.org\/10.3390\/s16081314","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2016,8,20]]}}}