{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T15:40:09Z","timestamp":1764603609105,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T00:00:00Z","timestamp":1574035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U54 MD012397 -01A1"],"award-info":[{"award-number":["U54 MD012397 -01A1"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"R&amp;D program of Ministry of Trade, Industry &amp; Energy","award":["N015800182"],"award-info":[{"award-number":["N015800182"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait signifies the walking pattern of an individual. It may be normal or abnormal, depending on the health condition of the individual. This paper considers the development of a gait sensor network system that uses a pair of wireless inertial measurement unit (IMU) sensors to monitor the gait cycle of a user. The sensor information is used for determining the normality of movement of the leg. The sensor system places the IMU sensors on one of the legs to extract the three-dimensional angular motions of the hip and knee joints while walking. The wearable sensor is custom-made at San Diego State University with wireless data transmission capability. The system enables the user to collect gait data at any site, including in a non-laboratory environment. The paper also presents the mathematical calculations to decompose movements experienced by a pair of IMUs into individual and relative three directional hip and knee joint motions. Further, a new approach of gait pattern classification based on the phase difference angles between hip and knee joints is presented. The experimental results show a potential application of the classification method in the areas of smart detection of abnormal gait patterns.<\/jats:p>","DOI":"10.3390\/s19225024","type":"journal-article","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T11:18:48Z","timestamp":1574075928000},"page":"5024","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Identification of Gait Motion Patterns Using Wearable Inertial Sensor Network"],"prefix":"10.3390","volume":"19","author":[{"given":"Kee S.","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Q","family":"Lee","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute, ICT, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusuf","family":"Ozturk","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Apoorva","family":"Gaidhani","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeremiah A.","family":"Cox","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Taj-Eldin, M., Ryan, C., O\u2019Flynn, B., and Galvin, P. (2018). A Review of Wearable Solutions for Physiological and Emotional Monitoring for Use by People with Autism Spectrum Disorder and Their Caregivers. Sensors (Basel), 18.","DOI":"10.3390\/s18124271"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3362","DOI":"10.3390\/s140203362","article-title":"Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications","volume":"14","year":"2014","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mannini, A., Trojaniello, D., Cereatti, A., and Sabatini, A.M. (2016, January 21). A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington\u2019s Disease Patients, Available online: https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4732167\/.","DOI":"10.3390\/s16010134"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.3390\/s120202255","article-title":"Gait Analysis Using Wearable Sensors","volume":"12","author":"Tao","year":"2012","journal-title":"Sensors"},{"key":"ref_5","unstructured":"(2019, November 15). Gait Disorders in the Elderly Geriatrics. Available online: https:\/\/www.msdmanuals.com\/professional\/geriatrics\/gait-disorders-in-the-elderly\/gait-disorders-in-the-elderly."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1016\/j.apmr.2011.11.029","article-title":"Gait Differs Between Unilateral and Bilateral Knee Osteoarthritis","volume":"93","author":"Creaby","year":"2012","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9321","DOI":"10.3390\/s130709321","article-title":"Three Dimensional Gait Analysis Using Wearable Acceleration and Gyro Sensors Based on Quaternion Calculations","volume":"13","author":"Tadano","year":"2013","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6891","DOI":"10.3390\/s140406891","article-title":"IMU-Based Joint Angle Measurement for Gait Analysis","volume":"14","author":"Seel","year":"2014","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1016\/j.medengphy.2008.09.005","article-title":"Direct measurement of human movement by accelerometry","volume":"30","author":"Godfrey","year":"2008","journal-title":"Med. Eng. Phys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITB.2005.856864","article-title":"Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring","volume":"10","author":"Karantonis","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Anwary, A., Yu, H., and Vassallo, M. (2018). An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors. Sensors, 18.","DOI":"10.3390\/s18020676"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6819","DOI":"10.3390\/s140406819","article-title":"Detection of Freezing of Gait in Parkinson Disease: Preliminary Results","volume":"14","author":"Coste","year":"2014","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Filippeschi, A., Schmitz, N., Miezal, M., Bleser, G., Ruffaldi, E., and Stricker, D. (2017). Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion. Sensors, 17.","DOI":"10.3390\/s17061257"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1186\/1743-0003-11-136","article-title":"Validation of the angular measurements of a new inertial-measurement-unit based rehabilitation system: Comparison with state-of-the-art gait analysis","volume":"11","author":"Leardini","year":"2014","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s12984-017-0241-2","article-title":"Auto detection and segmentation of daily living activities during a Timed up and Go task in people with Parkinson\u2019s disease using multiple inertial sensors","volume":"14","author":"Nguyen","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gaidhani, A., Moon, K., Ozturk, Y., Lee, S., and Youm, W. (2017). Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion. Sensors, 17.","DOI":"10.3390\/s17122932"},{"key":"ref_17","unstructured":"Cestari Soto, M. (2017). Variable-Stiffness Joints with Embedded Force Sensor for High-Performance Wearable Gait Exoskeletons. [Ph.D. Thesis, Universidad Polit\u00e9cnica de Madrid]."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.gaitpost.2014.02.001","article-title":"Summary measures for clinical gait analysis: A literature review","volume":"39","author":"Cimolin","year":"2014","journal-title":"Gait Posture"},{"key":"ref_19","unstructured":"The Gait Cycle (2009). Orthopaedic Surgery Review, Thieme."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/S0004-9514(14)60486-4","article-title":"Observation and analysis of hemiplegic gait: Stance phase","volume":"39","author":"Moseley","year":"1993","journal-title":"Aust. J. Physiother."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S0004-9514(14)60487-6","article-title":"Observation and analysis of hemiplegic gait: Swing phase","volume":"39","author":"Moore","year":"1993","journal-title":"Aust. J. Physiother."},{"key":"ref_22","first-page":"113","article-title":"Gait Analysis in Normal and Hemiplegic Patients Using Accelerometers (Gait & Motion Analysis)","volume":"1","author":"Kim","year":"2004","journal-title":"Proc. Asian Pac. Conf. Biomech.: Emerg. Sci. Technol. Biomech."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rossi, S., Colazza, A., Petrarca, M., Castelli, E., and Cappa, P. (2012). Gait analysis of hemiplegic and healthy subjects in different leg loading conditions. Gait Posture, 35.","DOI":"10.1016\/j.gaitpost.2011.09.080"},{"key":"ref_24","first-page":"99","article-title":"Gait Analysis in Cerebral Palsy Using Vicon System","volume":"7","author":"Tugui","year":"2012","journal-title":"Proc. Manuf. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Das, D., and Chakrabarty, A. (2015, January 15\u201316). Human gait based gender identification system using Hidden Markov Model and Support Vector Machines. Proceedings of the International Conference on Computing, Communication & Automation, Noida, India.","DOI":"10.1109\/CCAA.2015.7148386"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yoo, J.-H., Hwang, D., and Nixon, M.S. (2005). Gender Classification in Human Gait Using Support Vector Machine. Advanced Concepts for Intelligent Vision Systems Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/11558484_18"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/5024\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:35:23Z","timestamp":1760189723000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/5024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,18]]},"references-count":26,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19225024"],"URL":"https:\/\/doi.org\/10.3390\/s19225024","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,11,18]]}}}