{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:05:46Z","timestamp":1780085146748,"version":"3.54.0"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2016,12,2]],"date-time":"2016-12-02T00:00:00Z","timestamp":1480636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61402062"],"award-info":[{"award-number":["61402062"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Fundamental Research Funds for the Central Universities of China","award":["106112014CDJZR098801"],"award-info":[{"award-number":["106112014CDJZR098801"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.<\/jats:p>","DOI":"10.3390\/s16122048","type":"journal-article","created":{"date-parts":[[2016,12,2]],"date-time":"2016-12-02T10:36:37Z","timestamp":1480674997000},"page":"2048","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform"],"prefix":"10.3390","volume":"16","author":[{"given":"Huile","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education, Chongqing University, Chongqing 400044, China"},{"name":"School of Computer and Control, University of Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibo","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education, Chongqing University, Chongqing 400044, China"},{"name":"School of Software Engineering, Chongqing University, Chongqing 401331, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sazonov, E.S., Fulk, G., Sazonova, N., and Schuckers, S. (2009, January 3\u20136). Automatic Recognition of Postures and Activities in Stroke Patients. Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA.","DOI":"10.1109\/IEMBS.2009.5334908"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Iosifidis, A., Marami, E., Tefas, A., and Pitas, I. (2012, January 25\u201330). Eating and Drinking Activity Recognition Based on Discriminant Analysis of Fuzzy Distances and Activity Volumes. Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan.","DOI":"10.1109\/ICASSP.2012.6288350"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.artmed.2007.11.007","article-title":"Recognition of dietary activity events using on-body sensors","volume":"42","author":"Amft","year":"2008","journal-title":"Artif. Intell. Med."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hong, Y.J., Kim, I.J., Ahn, S.C., and Kim, H.G. (2008, January 13\u201315). Activity Recognition Using Wearable Sensors for Elder Care. Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking, Sanya, China.","DOI":"10.1109\/FGCN.2008.165"},{"key":"ref_5","unstructured":"Yu, X. (2008, January 7\u201310). Approaches and Principles of Fall Detection for Elderly and Patient. Proceedings of the 10th International Conference on e-Health Networking, Applications and Services (HealthCom 2008), Singapore."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2778","DOI":"10.1109\/TBME.2010.2049573","article-title":"Ambulatory monitoring of activities and motor symptoms in parkinson\u2019s disease","volume":"57","author":"Zwartjes","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3721","DOI":"10.3390\/s150203721","article-title":"Wearable sensor systems for infants","volume":"15","author":"Zhu","year":"2015","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TITB.2005.856863","article-title":"Activity classification using realistic data from wearable sensors","volume":"10","author":"Parkka","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1109\/TNSRE.2016.2519413","article-title":"The use of empirical mode decomposition-based algorithm and inertial measurement units to auto-detect daily living activities of healthy adults","volume":"24","author":"Ayachi","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_10","unstructured":"Wang, N., Ambikairajah, E., Celler, B.G., and Lovell, N.H. (April, January 31). Accelerometry Based Classification of Gait Patterns Using Empirical Mode Decomposition. Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.3233\/BME-151452","article-title":"The application of emd in activity recognition based on a single triaxial accelerometer","volume":"26","author":"Liao","year":"2015","journal-title":"Bio-Med. Mater. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ins.2016.01.020","article-title":"Complex activity recognition using time series pattern dictionary learned from ubiquitous sensors","volume":"340","author":"Liu","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1109\/TBME.1986.325697","article-title":"Short time fourier analysis of the electromyogram: Fast movements and constant contraction","volume":"12","author":"Hannaford","year":"1986","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","unstructured":"He, Z. (2010, January 10\u201312). Activity Recognition from Accelerometer Signals Based on Wavelet-ar Model. Proceedings of the 2010 IEEE International Conference on Progress in Informatics and Computing (PIC), Shanghai, China."},{"key":"ref_15","unstructured":"Fleury, A., Noury, N., and Vacher, M. (2009, January 24\u201328). A Wavelet-Based Pattern Recognition Algorithm to Classify Postural Transitions in Humans. Proceedings of the 2009 17th European Signal Processing Conference, Scotland, UK."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. London A"},{"key":"ref_17","unstructured":"Huang, N. (2001). Computer Implemented Empirical Mode Decomposition Apparatus, Method and Article of Manufacture for Two-Dimensional Signals. (6311130), U.S. Patent."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1785\/0120010285","article-title":"Signatures of the seismic source in emd-based characterization of the 1994 northridge, california, earthquake recordings","volume":"93","author":"Zhang","year":"2003","journal-title":"Bull. Seismol. Soc. Am."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1061\/(ASCE)0733-9399(2003)129:8(861)","article-title":"Hilbert-huang transform analysis of dynamic and earthquake motion recordings","volume":"129","author":"Zhang","year":"2003","journal-title":"J. Eng. Mech."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.soildyn.2005.03.004","article-title":"Characterizing and quantifying earthquake-induced site nonlinearity","volume":"26","author":"Zhang","year":"2006","journal-title":"Soil Dyn. Earthq. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.jappgeo.2016.03.024","article-title":"Analysis of natural mineral earthquake and blast based on hilbert-huang transform","volume":"128","author":"Li","year":"2016","journal-title":"J. Appl. Geophys."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1109\/78.747779","article-title":"A four-parameter atomic decomposition of chirplets","volume":"47","author":"Bultan","year":"1999","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4816","DOI":"10.1073\/pnas.95.9.4816","article-title":"Engineering analysis of biological variables: An example of blood pressure over 1 day","volume":"95","author":"Huang","year":"1998","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7845","DOI":"10.1029\/1999JB900445","article-title":"Empirical mode skeletonization of deep crustal seismic data: Theory and applications","volume":"105","author":"Vasudevan","year":"2000","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1061\/(ASCE)0733-9399(2004)130:1(85)","article-title":"Hilbert-huang based approach for structural damage detection","volume":"130","author":"Yang","year":"2004","journal-title":"J. Eng. Mech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1049\/iet-rsn.2010.0342","article-title":"Empirical mode decomposition-based monopulse processor for enhanced radar tracking in the presence of high-power interference","volume":"5","author":"Elgamel","year":"2011","journal-title":"IET Radar Sonar Navig."},{"key":"ref_27","first-page":"965","article-title":"Machine fault diagnosis by envelope capture based on the emd and hilbert transform","volume":"32","author":"Zhao","year":"2002","journal-title":"J. Ocean Univ. Qingdao"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"056126","DOI":"10.1103\/PhysRevE.71.056126","article-title":"Empirical mode decomposition and correlation properties of long daily ozone records","volume":"71","year":"2005","journal-title":"Phys. Rev. E"},{"key":"ref_29","unstructured":"Logan, B., Healey, J., Philipose, M., Tapia, E.M., and Intille, S. (2007, January 16\u201319). A long-Term Evaluation of Sensing Modalities for Activity Recognition. Proceedings of the International conference on Ubiquitous computing, Innsbruck, Austria."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004, January 18\u201323). Activity Recognition from User-Annotated Acceleration Data. Proceedings of the International Conference on Pervasive Computing, Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tapia, E.M., Intille, S.S., and Larson, K. (2004, January 18\u201323). Activity Recognition in the Home Using Simple and Ubiquitous Sensors. Proceedings of the International Conference on Pervasive Computing, Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_10"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Stikic, M., Huynh, T., Van Laerhoven, K., and Schiele, B. (February, January 30). Adl Recognition Based on the Combination of Rfid and Accelerometer Sensing. Proceedings of the 2008 Second International Conference on Pervasive Computing Technologies for Healthcare, Tampere, Finland.","DOI":"10.4108\/ICST.PERVASIVEHEALTH2008.2795"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1109\/TBME.2014.2307069","article-title":"Feature selection and activity recognition system using a single triaxial accelerometer","volume":"61","author":"Gupta","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Qi, X., Zhou, G., Li, Y., and Peng, G. (2012, January 4\u20137). Radiosense: Exploiting Wireless Communication Patterns for Body Sensor Network Activity Recognition. Proceedings of the 2012 IEEE 33rd Real-Time Systems Symposium (RTSS), San Juan, PR, USA.","DOI":"10.1109\/RTSS.2012.62"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hu\u1ef3nh, T., Blanke, U., and Schiele, B. (2007, January 20\u201321). Scalable Recognition of Daily Activities with Wearable Sensors. Proceedings of the International Symposium on Location-and Context-Awareness, Oberpfaffenhofen, Germany.","DOI":"10.1007\/978-3-540-75160-1_4"},{"key":"ref_38","unstructured":"UCI Machine Learning Repository Irvine, CA: University of California, School of Information and Computer Science. Available online: http:\/\/archive.ics.uci.edu\/ml\/datasets\/PAMAP2+Physical+Activity+Monitoring."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 18\u201322). Introducing a New Benchmarked Dataset for Activity Monitoring. Proceedings of the 2012 16th International Symposium on Wearable Computers, Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Patel, S., Mancinelli, C., Healey, J., Moy, M., and Bonato, P. (2009, January 3\u20135). Using Wearable Sensors to Monitor Physical Activities of Patients with Copd: A Comparison of Classifier Performance. Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, Berkeley, CA, USA.","DOI":"10.1109\/BSN.2009.53"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (September, January 30). Introducing a Modular Activity Monitoring System. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091360"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2007). Data Streams: Models and Algorithms, Springer.","DOI":"10.1007\/978-0-387-47534-9"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window size impact in human activity recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_44","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 2007 11th IEEE International Symposium on Wearable Computers, Boston, MA, USA.","DOI":"10.1109\/ISWC.2007.4373774"},{"key":"ref_45","unstructured":"Berchtold, M., Budde, M., Schmidtke, H., and Beigl, M. (2010). Advances in Artificial Intelligence, Springer."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1109\/TITB.2012.2206602","article-title":"A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation","volume":"16","author":"Lin","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lubina, P., and Rudzki, M. (2015, January 25\u201327). Artificial Neural Networks in Accelerometer-Based Human Activity Recognition. Proceedings of the 2015 22nd International Conference Mixed Design of Integrated Circuits & Systems (MIXDES), Torun, Poland.","DOI":"10.1109\/MIXDES.2015.7208482"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fang, H., and He, L. (2012, January 11\u201313). Bp Neural Network for Human Activity Recognition in Smart Home. Proceedings of the 2012 International Conference on Computer Science and Service System, Nanjing, China.","DOI":"10.1109\/CSSS.2012.262"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., McClelland, J.L., and Group, P.R. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cogniton, MIT Press.","DOI":"10.7551\/mitpress\/5236.001.0001"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jang, Y., Song, Y., Noh, H.W., and Kim, S. (2015, January 25\u201329). A Basic Study of Activity Type Detection and Energy Expenditure Estimation for Children and Youth in Daily Life Using 3-axis Accelerometer and 3-stage Cascaded Artificial Neural Network. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318988"},{"key":"ref_52","unstructured":"Wen, X., Zhou, L., Wang, D., and Xiong, X. (2001). Application Design of MATLAB Neural Network, Science Press. (In Chinese)."},{"key":"ref_53","unstructured":"Demuth, H.B., and Beale, M.H. (1997). MATLAB: Neural Network Toolbox: User\u2019s Guide, Math Works. [2nd ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 6\u20139). Creating and Benchmarking a New Dataset for Physical Activity Monitoring. Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, Crete, Greece.","DOI":"10.1145\/2413097.2413148"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Xu, H.L., Chai, Y., Lin, W.L., Jiang, F., and Qi, S.H. (2015, January 17\u201318). An Activity Recognition Algorithm Based on Multi-Feature Fuzzy Cluster. Proceedings of the 2015 Chinese Intelligent Systems Conference, Yangzhou, China.","DOI":"10.1007\/978-3-662-48365-7_37"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/12\/2048\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:27:51Z","timestamp":1760210871000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/12\/2048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,2]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["s16122048"],"URL":"https:\/\/doi.org\/10.3390\/s16122048","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,2]]}}}