{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:55:45Z","timestamp":1778255745795,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,11]],"date-time":"2018-10-11T00:00:00Z","timestamp":1539216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010425","name":"Tri-Service General Hospital","doi-asserted-by":"publisher","award":["TSGH-NTUST-107-04"],"award-info":[{"award-number":["TSGH-NTUST-107-04"]}],"id":[{"id":"10.13039\/501100010425","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5) and both sides of the thigh, distal tibia (shank), and foot. The 20 subjects selected to participate in this study were separated into two groups: stroke patients (11) and patients with neurological disorders other than stroke (brain concussion, spinal injury, or brain hemorrhage) (9). The temporal parameters of gait were calculated using a wearable device, and various features and sensor configurations were examined to establish the ideal accuracy for classifying different groups. A comparison of the various methods and features for classifying the three groups revealed that a combination of time domain and gait temporal feature-based classification with the Multilayer Perceptron (MLP) algorithm outperformed the other methods of feature-based classification. The classification results of different sensor placements revealed that the sensor placed on the shank achieved higher accuracy than the other sensor placements (L5, foot, and thigh). The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. The results of this study indicate that the wearable IMU device is capable of differentiating between the gait patterns of healthy patients, patients with stroke, and patients with other neurological disorders. Moreover, the most favorable results were reported for the classification that used the combination of time domain and gait temporal features as the model input and the shank location for sensor placement.<\/jats:p>","DOI":"10.3390\/s18103397","type":"journal-article","created":{"date-parts":[[2018,10,12]],"date-time":"2018-10-12T02:58:04Z","timestamp":1539313084000},"page":"3397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":92,"title":["Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders"],"prefix":"10.3390","volume":"18","author":[{"given":"Wei-Chun","family":"Hsu","sequence":"first","affiliation":[{"name":"Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan"},{"name":"Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 10607, Taiwan"},{"name":"Department of Biomedical Engineering, National Defense Medical Center, Taipei 11490, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8814-1275","authenticated-orcid":false,"given":"Tommy","family":"Sugiarto","sequence":"additional","affiliation":[{"name":"Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 10607, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Jia","family":"Lin","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fu-Chi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0720-7584","authenticated-orcid":false,"given":"Zheng-Yi","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Taipei City Hospital Zhongxing Branch, Datong District, Taipei 10341, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-Tien","family":"Sun","sequence":"additional","affiliation":[{"name":"Division of Embedded System and SoC Technology, System Integration and Application Department, Information and Communication Research Laboratory, Industrial Technology Research Institute, Hsinchu 31057, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Lung","family":"Hsu","sequence":"additional","affiliation":[{"name":"Division of Embedded System and SoC Technology, System Integration and Application Department, Information and Communication Research Laboratory, Industrial Technology Research Institute, Hsinchu 31057, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan-Nien","family":"Chou","sequence":"additional","affiliation":[{"name":"Neurosurgery Department, Tri-Service General Hospital, Taipei 11490, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1109\/JBHI.2015.2419317","article-title":"Validation of an accelerometer to quantify a comprehensive battery of gait characteristics in healthy older adults and Parkinson\u2019s disease: Toward clinical and at home use","volume":"20","author":"Din","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.gaitpost.2014.07.007","article-title":"Accuracy, sensitivity and robustness of five different methods for the estimation of gait temporal parameters using a single inertial sensor mounted on the lower trunk","volume":"40","author":"Trojaniello","year":"2014","journal-title":"Gait Posture"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Godfrey, A., Del Din, S., Barry, G., Mathers, J.C., and Rochester, L. (2014, January 26\u201330). Within trial validation and reliability of a single tri-axial accelerometer for gait assessment. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944969"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1123\/jab.29.1.118","article-title":"Determination of gait events using an externally mounted shank accelerometer","volume":"29","author":"Sinclair","year":"2013","journal-title":"J. Appl. Biomech."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0021-9290(02)00008-8","article-title":"Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes","volume":"35","author":"Aminian","year":"2002","journal-title":"J. Biomech."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1682\/JRRD.2013.07.0162","article-title":"Assessment of gait stability, harmony, and symmetry in subjects with lower-limb amputation evaluated by trunk accelerations","volume":"51","author":"Brunelli","year":"2014","journal-title":"J. Rehab. Res. Dev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.ridd.2011.08.031","article-title":"Stability and harmony of gait in children with cerebral palsy","volume":"33","author":"Iosa","year":"2012","journal-title":"Rese. Dev. Disabil."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"S16","DOI":"10.1016\/j.gaitpost.2016.07.046","article-title":"Gait stability assessment in down and prader-willi syndrome children using inertial sensors","volume":"49","author":"Salatino","year":"2016","journal-title":"Gait Posture"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Weiss, A., Herman, T., Giladi, N., and Hausdorff, J.M. (2014). Objective assessment of fall risk in Parkinson\u2019s disease using a body-fixed sensor worn for 3 days. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0096675"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/TNSRE.2013.2265887","article-title":"A comprehensive assessment of gait accelerometry signals in time, frequency and time-frequency domains","volume":"22","author":"Sejdic","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehab. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zheng, H., Yang, M., Wang, H., and McClean, S. (2009). Machine learning and statistical approaches to support the discrimination of neuro-degenerative diseases based on gait analysis. Intelligent Patient Management, Springer.","DOI":"10.1007\/978-3-642-00179-6_4"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mannini, A., Trojaniello, D., Cereatti, A., and Sabatini, A.M. (2016). A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington\u2019s disease patients. Sensors, 16.","DOI":"10.3390\/s16010134"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1007\/s10439-013-0917-0","article-title":"Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors","volume":"42","author":"Zhang","year":"2014","journal-title":"Ann. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s40846-016-0178-0","article-title":"Stability and harmony of gait in patients with subacute stroke","volume":"36","author":"Iosa","year":"2016","journal-title":"J. Med. Biol. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1080\/17434440.2016.1198694","article-title":"Wearable inertial sensors for human movement analysis","volume":"13","author":"Iosa","year":"2016","journal-title":"Exp. Rev. Med. Dev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1378\/chest.117.5.1359","article-title":"Quantitating physical activity in copd using a triaxial accelerometer","volume":"117","author":"Steele","year":"2000","journal-title":"CHEST J."},{"key":"ref_17","unstructured":"Long, X., Yin, B., and Aarts, R.M. (2009, January 3\u20136). Single-accelerometer-based daily physical activity classification. Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.gaitpost.2004.08.008","article-title":"Stair climbing detection during daily physical activity using a miniature gyroscope","volume":"22","author":"Coley","year":"2005","journal-title":"Gait Posture"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.maturitas.2011.11.003","article-title":"Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: A systematic literature review of current knowledge and applications","volume":"71","author":"Taraldsen","year":"2012","journal-title":"Maturitas"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1249\/01.MSS.0000117158.14542.E7","article-title":"Validation of the RT3 triaxial accelerometer for the assessment of physical activity","volume":"36","author":"Rowlands","year":"2004","journal-title":"Med. Sci. Sport. Exer."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2371","DOI":"10.1038\/oby.2007.281","article-title":"Physical activity assessment with accelerometers: An evaluation against doubly labeled water","volume":"15","author":"Plasqui","year":"2007","journal-title":"Obesity"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.3390\/s100201154","article-title":"Machine learning methods for classifying human physical activity from on-body accelerometers","volume":"10","author":"Mannini","year":"2010","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.gaitpost.2017.07.030","article-title":"Novel methodology for estimating initial contact events from accelerometers positioned at different body locations","volume":"59","author":"Khandelwal","year":"2018","journal-title":"Gait Posture"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.1109\/JSEN.2017.2786587","article-title":"Optimal foot location for placing wearable imu sensors and automatic feature extraction for gait analysis","volume":"18","author":"Anwary","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/TBME.2012.2223465","article-title":"A novel approach to reducing number of sensing units for wearable gait analysis systems","volume":"60","author":"Salarian","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Carcreff, L., Gerber, C.N., Paraschiv-Ionescu, A., De Coulon, G., Newman, C.J., Armand, S., and Aminian, K. (2018). What is the best configuration of wearable sensors to measure spatiotemporal gait parameters in children with cerebral palsy?. Sensors, 18.","DOI":"10.3390\/s18020394"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"817","DOI":"10.3389\/fpsyg.2017.00817","article-title":"Inertial sensors to assess gait quality in patients with neurological disorders: A systematic review of technical and analytical challenges","volume":"8","author":"Vienne","year":"2017","journal-title":"Front. Psychol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2863","DOI":"10.1016\/j.jbiomech.2005.09.012","article-title":"Reliability of segmental accelerations measured using a new wireless gait analysis system","volume":"39","author":"Kavanagh","year":"2006","journal-title":"J. Biomech."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1109\/TBME.2004.827933","article-title":"Gait assessment in parkinson\u2019s disease: Toward an ambulatory system for long-term monitoring","volume":"51","author":"Salarian","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.gaitpost.2009.10.014","article-title":"Evaluation of gait symmetry after stroke: A comparison of current methods and recommendations for standardization","volume":"31","author":"Patterson","year":"2010","journal-title":"Gait Posture"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_32","unstructured":"Zhang, H. (2004, January 12\u201314). The optimality of naive bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference, Miami Beach, FL, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bishop, C., and Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TBME.2005.845241","article-title":"Support vector machines for automated gait classification","volume":"52","author":"Begg","year":"2005","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s11042-011-0786-1","article-title":"Automatic recognition of gait-related health problems in the elderly using machine learning","volume":"58","author":"Pogorelc","year":"2012","journal-title":"Multimedia Tools Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.clinbiomech.2018.03.002","article-title":"The implementation of inertial sensors for the assessment of temporal parameters of gait in the knee arthroplasty population","volume":"54","author":"Staes","year":"2018","journal-title":"Clin. Biomech."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.gaitpost.2005.12.017","article-title":"Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals","volume":"24","author":"Jasiewicz","year":"2006","journal-title":"Gait Posture"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1016\/j.measurement.2012.04.013","article-title":"Automatic diagnosis of neuro-degenerative diseases using gait dynamics","volume":"45","author":"Daliri","year":"2012","journal-title":"Measurement"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/10\/3397\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:24:52Z","timestamp":1760196292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/10\/3397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,11]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["s18103397"],"URL":"https:\/\/doi.org\/10.3390\/s18103397","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,11]]}}}