{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T05:07:25Z","timestamp":1777439245393,"version":"3.51.4"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2014,3,21]],"date-time":"2014-03-21T00:00:00Z","timestamp":1395360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This article presents a study on analyzing body movements by using a single accelerometer sensor. The investigated categories of body movements belong to the Laban Effort Framework: Strong\u2014Light, Free\u2014Bound and Sudden\u2014Sustained. All body movements were represented by a set of activities used for data collection. The calculated accuracy of detecting the body movements was based on collecting data from a single wireless tri-axial accelerometer sensor. Ten healthy subjects collected data from three body locations (chest, wrist and thigh) simultaneously in order to analyze the locations comparatively. The data was then processed and analyzed using Machine Learning techniques. The wrist placement was found to be the best single location to record data for detecting Strong\u2014Light body movements using the Random Forest classifier. The wrist placement was also the best location for classifying Bound\u2014Free body movements using the SVM classifier. However, the data collected from the chest placement yielded the best results for detecting Sudden\u2014Sustained body movements using the Random Forest classifier. The study shows that the choice of the accelerometer placement should depend on the targeted type of movement. In addition, the choice of the classifier when processing data should also depend on the chosen location and the target movement.<\/jats:p>","DOI":"10.3390\/s140305725","type":"journal-article","created":{"date-parts":[[2014,3,21]],"date-time":"2014-03-21T12:06:20Z","timestamp":1395403580000},"page":"5725-5741","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Analyzing Body Movements within the Laban Effort Framework Using a Single Accelerometer"],"prefix":"10.3390","volume":"14","author":[{"given":"Basel","family":"Kikhia","sequence":"first","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miguel","family":"Gomez","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lara","family":"Jim\u00e9nez","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josef","family":"Hallberg","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8752-2375","authenticated-orcid":false,"given":"Niklas","family":"Karvonen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4549-6751","authenticated-orcid":false,"given":"K\u00e5re","family":"Synnes","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Electrical and Space Engineering, Lule\u00e5 University of Technology, Lule\u00e5 971 87, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,3,21]]},"reference":[{"key":"ref_1","unstructured":"Aggarwal, J.K., and Cai, Q. (1997, January 16). Human Motion Analysis: A Review, San Juan, Puerto Rico, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1068\/p5096","article-title":"Emotion perception from dynamic and static body expressions in point-light and full-light displays","volume":"33","author":"Atkinson","year":"2004","journal-title":"Perception"},{"key":"ref_3","unstructured":"Bartenieff, I., and Lewis, D. (2002). Body Movement: Coping with the Environment, Routledge. [1st ed.]."},{"key":"ref_4","unstructured":"Available online: http:\/\/laban-eurolab.org\/index.php?option=com_content&view=article&id=45&Itemid=22&lang=en."},{"key":"ref_5","unstructured":"Laban, R., and Lawrence, F.C. (1974). Effort: Economy in Body Movement, MacDonald and Evans. [2nd ed.]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mentis, H.M., and Johansson, C. Seeing movement qualities. Paris, France. 2013.","DOI":"10.1145\/2470654.2466462"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1016\/j.patrec.2008.08.002","article-title":"Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers","volume":"29","author":"Yang","year":"2008","journal-title":"Pattern Recognit. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9183","DOI":"10.3390\/s130709183","article-title":"Optimal Placement of Accelerometers for the Detection of Everyday Activities","volume":"13","author":"Cleland","year":"2013","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TBCAS.2011.2160540","article-title":"Sensor positioning for activity recognition using wearable accelerometers","volume":"5","author":"Atallah","year":"2011","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-540-24646-6_1","article-title":"Activity Recognition from User-Annotated Acceleration Data","volume":"Volume 3001","author":"Ferscha","year":"2004","journal-title":"Pervasive Computing, Lecture Notes in Computer Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/10.554760","article-title":"A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity","volume":"44","author":"Bouten","year":"2002","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6075","DOI":"10.3390\/s120506075","article-title":"A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee","volume":"12","author":"Viqueira","year":"2012","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gjoreski, H., Lustrek, M., and Gams, M. (2011, January 25\u201328). Accelerometer Placement for Posture Recognition and Fall Detection. Nottingham, UK.","DOI":"10.1109\/IE.2011.11"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Atallah, L., Lo, B., King, R., and Yang, G.Z. (2010, January 7\u20139). Sensor placement for Activity Recognition Using Wearable Accelerometers. Singapore, Singapore.","DOI":"10.1109\/BSN.2010.23"},{"key":"ref_15","unstructured":"Ravi, N., Dandekar, N., Mysore, P., and Littman, M.L. Activity Recognition from Accelerometer Data. Pittsburgh, PA, USA."},{"key":"ref_16","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_17","unstructured":"Fagerberg, P., St\u00e5hl, A., and H\u00f6\u00f6k, K. Designing gestures for affective input: an analysis of shape, effort and valence. Norrk\u00f6ping, Sweden."},{"key":"ref_18","unstructured":"Camurri, A., Hashimoto, S., Suzuki, K., and Trocca, R. (1999, January 12\u201315). Kansei analysis of dance performance. Tokyo, Japan."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1002\/dev.10088","article-title":"The development of \u201croughness\u201d in the play fighting of rats: A Laban Movement Analysis perspective","volume":"42","author":"Foroud","year":"2003","journal-title":"Dev. Psychobiol."},{"key":"ref_20","unstructured":"Veltink, P.H., Bussmann, H.B.J., Koelma, F., Franken, H.M., Martens, W.L.J., and Van Lummel, R.C. (1993, January 28\u201331). The feasibility of posture and movement detection by accelerometry. San Diego, CA, USA."},{"key":"ref_21","unstructured":"Olgun, D.O., and Pentland, A.S. Human Activity Recognition: Accuracy Across Common Locations for Wearable Sensors. Montreux, Switzerland."},{"key":"ref_22","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_23","first-page":"858","article-title":"Fall Detection by Wearable Sensor and One-Class SVM Algorithm","volume":"Volume 345","author":"Huang","year":"2006","journal-title":"Intelligent Computing in Signal Processing and Pattern Recognition, Lecture Notes in Control and Information Science"},{"key":"ref_24","unstructured":"Zhang, T., Wang, J., Liu, P., and Hou, J. (2006). Fall Detection by Embedding an Accelerometer in Cellphone and Using Summary KFD Algorithm. Int. J. Comput. Sci. Netw. Secur., 277\u2013284."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1249\/MSS.0b013e3181a24536","article-title":"Detection of Type, Duration, and Intensity of Activity using an Accelerometer","volume":"41","author":"Bonomi","year":"2009","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TBME.2008.2006190","article-title":"A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities","volume":"56","author":"Preece","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"S481","DOI":"10.1097\/00005768-200009001-00007","article-title":"The Utility of the Digi-Walker Step Counter to Assess Daily Physical Activity Patterns","volume":"32","author":"Welk","year":"2000","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_29","unstructured":"Janecek, A.G., Gansterer, W.N., Demel, M.A., and Ecker, G.F. On the Relationship Between Feature Selection and Classification Accuracy. Antwerp, Belgium."},{"key":"ref_30","unstructured":"Witten, I.H., and Frank, E. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. [3rd ed.]."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/32.345828","article-title":"Machine Learning Approaches to Estimating Software Development Effort","volume":"21","author":"Srinivasan","year":"1995","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_32","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_33","first-page":"197","article-title":"Fall detection and activity recognition with machine learning","volume":"33","year":"2009","journal-title":"Informatica"},{"key":"ref_34","unstructured":"Mirchevska, V., Lu\u0161trek, M., and Gams, M. Combining machine learning and expert knowledge for classifying human posture. Portoro\u017e, Slovenia."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/3\/5725\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:09:29Z","timestamp":1760216969000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/3\/5725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,3,21]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2014,3]]}},"alternative-id":["s140305725"],"URL":"https:\/\/doi.org\/10.3390\/s140305725","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,3,21]]}}}