{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T20:05:14Z","timestamp":1780085114865,"version":"3.54.0"},"reference-count":22,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of the Department of Education","award":["17ZB0101"],"award-info":[{"award-number":["17ZB0101"]}]},{"name":"the Natural Science Foundation of the Department of Education","award":["2013-2541\/001-011"],"award-info":[{"award-number":["2013-2541\/001-011"]}]},{"name":"the Natural Science Foundation of the Department of Education","award":["cdjgb2022282"],"award-info":[{"award-number":["cdjgb2022282"]}]},{"name":"the Natural Science Foundation of the Department of Education","award":["202102123022"],"award-info":[{"award-number":["202102123022"]}]},{"name":"the EU Erasmus Mundus project FUSION-featured Europe and south Asia mobility network","award":["17ZB0101"],"award-info":[{"award-number":["17ZB0101"]}]},{"name":"the EU Erasmus Mundus project FUSION-featured Europe and south Asia mobility network","award":["2013-2541\/001-011"],"award-info":[{"award-number":["2013-2541\/001-011"]}]},{"name":"the EU Erasmus Mundus project FUSION-featured Europe and south Asia mobility network","award":["cdjgb2022282"],"award-info":[{"award-number":["cdjgb2022282"]}]},{"name":"the EU Erasmus Mundus project FUSION-featured Europe and south Asia mobility network","award":["202102123022"],"award-info":[{"award-number":["202102123022"]}]},{"name":"the Talent Cultivation and Teaching Reform Project of Chengdu University","award":["17ZB0101"],"award-info":[{"award-number":["17ZB0101"]}]},{"name":"the Talent Cultivation and Teaching Reform Project of Chengdu University","award":["2013-2541\/001-011"],"award-info":[{"award-number":["2013-2541\/001-011"]}]},{"name":"the Talent Cultivation and Teaching Reform Project of Chengdu University","award":["cdjgb2022282"],"award-info":[{"award-number":["cdjgb2022282"]}]},{"name":"the Talent Cultivation and Teaching Reform Project of Chengdu University","award":["202102123022"],"award-info":[{"award-number":["202102123022"]}]},{"name":"the Second Batch of Industry\u2013University Cooperative Education Project, Ministry of Education","award":["17ZB0101"],"award-info":[{"award-number":["17ZB0101"]}]},{"name":"the Second Batch of Industry\u2013University Cooperative Education Project, Ministry of Education","award":["2013-2541\/001-011"],"award-info":[{"award-number":["2013-2541\/001-011"]}]},{"name":"the Second Batch of Industry\u2013University Cooperative Education Project, Ministry of Education","award":["cdjgb2022282"],"award-info":[{"award-number":["cdjgb2022282"]}]},{"name":"the Second Batch of Industry\u2013University Cooperative Education Project, Ministry of Education","award":["202102123022"],"award-info":[{"award-number":["202102123022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human motion recognition based on wearable devices plays a vital role in pervasive computing. Smartphones have built-in motion sensors that measure the motion of the device with high precision. In this paper, we propose a human lower limb motion capture and recognition approach based on a Smartphone. We design a motion logger to record five categories of limb activities (standing up, sitting down, walking, going upstairs, and going downstairs) using two motion sensors (tri-axial accelerometer, tri-axial gyroscope). We extract the motion features and select a subset of features as a feature vector from the frequency domain of the sensing data using Fast Fourier Transform (FFT). We classify and predict human lower limb motion using three supervised learning algorithms: Na\u00efve Bayes (NB), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANNs). We use 670 lower limb motion samples to train and verify these classifiers using the 10-folder cross-validation technique. Finally, we design and implement a live detection system to validate our motion detection approach. The experimental results show that our low-cost approach can recognize human lower limb activities with acceptable accuracy. On average, the recognition rate of NB, KNN, and ANNs are 97.01%, 96.12%, and 98.21%, respectively.<\/jats:p>","DOI":"10.3390\/s22145273","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T01:57:11Z","timestamp":1657850231000},"page":"5273","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Human Lower Limb Motion Capture and Recognition Based on Smartphones"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4294-1581","authenticated-orcid":false,"given":"Lin-Tao","family":"Duan","sequence":"first","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"},{"name":"School of Computer Science, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Lawo","sequence":"additional","affiliation":[{"name":"International Graduate School for Dynamics in Logistics, Bremen University, 28359 Bremen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Guo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai-Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu University, Chengdu 610106, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TMC.2020.3019341","article-title":"Context-Aware and Energy-Aware Video Streaming on Smartphones","volume":"21","author":"Chen","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MPRV.2011.13","article-title":"LifeMap: A Smartphone-Based Context Provider for Location-Based Services","volume":"10","author":"Chon","year":"2011","journal-title":"IEEE Pervasive Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s00779-015-0885-5","article-title":"Noninvasive Stress Recognition Considering the Current Activity","volume":"19","author":"Sysoev","year":"2015","journal-title":"Pers. Ubiquit. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/JSEN.2016.2628346","article-title":"A Survey on Activity Detection and Classification Using Wearable Sensors","volume":"17","author":"Cornacchia","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Khimraj, P.K., Shukla, K.P., Vijayvargiya, A., and Kumar, R. (2020, January 21\u201322). Human Activity Recognition Using Accelerometer and Gyroscope Data from Smartphones. Proceedings of the International Conference on Emerging Trends in Communication, Control and Computing, Lakshmangarh, India.","DOI":"10.1109\/ICONC345789.2020.9117456"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Testoni, A., and Di Felice, M. (2017, January 27\u201329). A Software Architecture for Generic Human Activity Recognition from Smartphone Sensor Data. Proceedings of the IEEE International Workshop on Measurement and Networking, Naples, Italy.","DOI":"10.1109\/IWMN.2017.8078368"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6000104","DOI":"10.1109\/LSENS.2021.3133887","article-title":"FFT Spectrum Spread with Machine Learning (ML) Analysis of Triaxial Acceleration from Shirt Pocket and Torso for Sensing Coughs While Walking","volume":"6","author":"Vyas","year":"2022","journal-title":"IEEE Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/TNSRE.2017.2745418","article-title":"Using Inertial Sensors to Automatically Detect and Segment Activities of Daily Living in People with Parkinson\u2019s Disease","volume":"26","author":"Nguyen","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2946","DOI":"10.1109\/TCSVT.2017.2716819","article-title":"First-Person Daily Activity Recognition with Manipulated Object Proposals and Non-Linear Feature Fusion","volume":"28","author":"Wang","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hamdi, M.M., Awad, M., Abdelhameed, M.M., and Tolbah, F. (2014, January 11\u201313). Lower Limb Motion Tracking Using IMU Sensor Network. Proceedings of the Cairo International Biomedical Engineering Conference, Giza, Egypt.","DOI":"10.1109\/CIBEC.2014.7020957"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4271","DOI":"10.1109\/JSEN.2019.2895289","article-title":"A Two-Dimensional Feature Space-Based Approach for Human Locomotion Recognition","volume":"19","author":"Chinimilli","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Filios, G., Nikoletseas, S., and Pavlopoulou, C. (2015, January 2\u20136). Efficient Parameterized Methods for Physical Activity Detection Using Only Smartphone Sensors. Proceedings of the 13th ACM International Symposium on Mobility Management and Wireless Access, Cancun, Mexico.","DOI":"10.1145\/2810362.2810372"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Anjum, A., and Ilyas, M.U. (2013, January 11\u201314). Activity Recognition Using Smartphone Sensors. Proceedings of the Consumer Communications and Networking Conference, Las Vegas, NV, USA.","DOI":"10.1109\/CCNC.2013.6488584"},{"key":"ref_14","unstructured":"Belman, A.K., Wang, L., Iyengar, S.S., Sniatala, P., Wright, R., Dora, R., Baldwin, J., Jin, Z.P., and Phoha, V.V. (2019). Insights from BB-MAS-A Large Dataset for Typing, Gait and Swipes of the Same Person on Desktop, Tablet and Phone. arXiv."},{"key":"ref_15","unstructured":"Belman, A.K., Wang, L., Iyengar, S.S., Sniatala, P., Wright, R., Dora, R., Baldwin, J., Jin, Z.P., and Phoha, V.V. (2019). SU-AIS BB-MAS (Syracuse University and Assured Information Security\u2014Behavioral Biometrics Multi-device and multi-Activity data from Same users) Dataset. IEEE Dataport."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Maurer, U., Smailagic, A., Siewiorek, D.P., and Deisher, M. (2006, January 3\u20135). Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions. Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks, Cambridge, MA, USA.","DOI":"10.21236\/ADA534437"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, H., and Liu, H. (2019, January 20\u201322). Research on Human Motion Recognition System Based on MEMS Sensor Network. Proceedings of the 4th Advanced Information Technology, Electronic and Automation Control Conference, Chengdu, China.","DOI":"10.1109\/IAEAC47372.2019.8997727"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TITB.2007.899496","article-title":"Detection of Daily Activities and Sports with Wearable Sensors in Controlled and Uncontrolled Conditions","volume":"12","author":"Ermes","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4528","DOI":"10.1109\/JSEN.2019.2898891","article-title":"Activity-Aware Fall Detection and Recognition Based on Wearable Sensors","volume":"19","author":"Hussain","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/JSEN.2017.2782492","article-title":"Human Activity Classification in Smartphones Using Accelerometer and Gyroscope Sensors","volume":"18","author":"Jain","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TBME.2021.3090051","article-title":"Inter-Patient Atrial Flutter Classification Using FFT-Based Features and a Low-Variance Stacking Classifier","volume":"69","author":"Besler","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4566","DOI":"10.1109\/JSEN.2016.2545708","article-title":"A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone","volume":"16","author":"Wang","year":"2016","journal-title":"IEEE Sens. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5273\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:50:19Z","timestamp":1760140219000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5273"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,14]]},"references-count":22,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145273"],"URL":"https:\/\/doi.org\/10.3390\/s22145273","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,14]]}}}