{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:30:45Z","timestamp":1772166645120,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T00:00:00Z","timestamp":1699056000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T00:00:00Z","timestamp":1699056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["4162287727 - SFB 1410"],"award-info":[{"award-number":["4162287727 - SFB 1410"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["4162287727 - SFB 1410"],"award-info":[{"award-number":["4162287727 - SFB 1410"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004869","name":"Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004869","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    In order to grasp and transport an object, grip and load forces must be scaled according to the object\u2019s properties (such as weight). To select the appropriate grip and load forces, the object weight is estimated based on experience or, in the case of robots, usually by use of image recognition. We propose a new approach that makes a robot\u2019s weight estimation less dependent on prior learning and, thereby, allows it to successfully grasp a wider variety of objects. This study evaluates whether it is feasible to predict an object\u2019s weight class in a replacement task based on the time series of upper body angles of the active arm or on object velocity profiles. Furthermore, we wanted to investigate how prediction accuracy is affected by (i) the length of the time series and (ii) different cross-validation (CV) procedures. To this end, we recorded and analyzed the movement kinematics of 12 participants during a replacement task. The participants\u2019 kinematics were recorded by an optical motion tracking system while transporting an object, 80 times in total from varying starting positions to a predefined end position on a table. The object\u2019s weight was modified (made lighter and heavier) without changing the object\u2019s visual appearance. Throughout the experiment, the object\u2019s weight (light\/heavy) was randomly changed without the participant\u2019s knowledge. To predict the object\u2019s weight class, we used a discrete cosine transform to smooth and compress the time series and a support vector machine for supervised learning from the achieved discrete cosine transform parameters. Results showed good prediction accuracy (up to\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$95\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>95<\/mml:mn>\n                            <mml:mo>%<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , depending on the CV procedure and the length of the time series). Even at the beginning of a movement (after only 300\u00a0ms), we were able to predict the object weight reliably (within a classification rate of\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$88-94\\%$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>88<\/mml:mn>\n                            <mml:mo>-<\/mml:mo>\n                            <mml:mn>94<\/mml:mn>\n                            <mml:mo>%<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ).\n                  <\/jats:p>","DOI":"10.1186\/s40708-023-00209-4","type":"journal-article","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:03Z","timestamp":1699102923000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting object properties based on movement kinematics"],"prefix":"10.1186","volume":"10","author":[{"given":"Lena","family":"Kopnarski","sequence":"first","affiliation":[]},{"given":"Laura","family":"Lippert","sequence":"additional","affiliation":[]},{"given":"Julian","family":"Rudisch","sequence":"additional","affiliation":[]},{"given":"Claudia","family":"Voelcker-Rehage","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,4]]},"reference":[{"issue":"8","key":"209_CR1","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1016\/S0893-6080(96)00035-4","volume":"9","author":"RC Miall","year":"1996","unstructured":"Miall RC, Wolpert DM (1996) Forward models for physiological motor control. Neural Netw 9(8):1265\u20131279","journal-title":"Neural Netw"},{"issue":"1","key":"209_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s00221-011-2695-y","volume":"212","author":"J Hermsd\u00f6rfer","year":"2011","unstructured":"Hermsd\u00f6rfer J, Li Y, Randerath J, Goldenberg G, Eidenm\u00fcller S (2011) Anticipatory scaling of grip forces when lifting objects of everyday life. Exp Brain Res 212(1):19\u201331","journal-title":"Exp Brain Res"},{"issue":"1","key":"209_CR3","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00426-005-0032-4","volume":"71","author":"AF de Hamilton","year":"2007","unstructured":"de Hamilton AF, Joyce DW, Flanagan JR, Frith CD, Wolpert DM (2007) Kinematic cues in perceptual weight judgement and their origins in box lifting. Psychol Res 71(1):13\u201321. https:\/\/doi.org\/10.1007\/s00426-005-0032-4","journal-title":"Psychol Res"},{"issue":"4","key":"209_CR4","doi-asserted-by":"publisher","first-page":"92","DOI":"10.3390\/robotics8040092","volume":"8","author":"T Aujeszky","year":"2019","unstructured":"Aujeszky T, Korres G, Eid M, Khorrami F (2019) Estimating weight of unknown objects using active thermography. Robotics 8(4):92. https:\/\/doi.org\/10.3390\/robotics8040092","journal-title":"Robotics"},{"issue":"11","key":"209_CR5","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.1016\/j.robot.2013.04.003","volume":"61","author":"A Chibani","year":"2013","unstructured":"Chibani A, Amirat Y, Mohammed S, Matson E, Hagita N, Barreto M (2013) Ubiquitous robotics: recent challenges and future trends. Robot Auton Syst 61(11):1162\u20131172. https:\/\/doi.org\/10.1016\/j.robot.2013.04.003","journal-title":"Robot Auton Syst"},{"issue":"6","key":"209_CR6","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1109\/TCST.2014.2300696","volume":"22","author":"G Buizza Avanzini","year":"2014","unstructured":"Buizza Avanzini G, Ceriani NM, Zanchettin AM, Rocco P, Bascetta L (2014) Safety control of industrial robots based on a distributed distance sensor. IEEE Trans Control Syst Technol 22(6):2127\u20132140. https:\/\/doi.org\/10.1109\/TCST.2014.2300696","journal-title":"IEEE Trans Control Syst Technol"},{"key":"209_CR7","unstructured":"Standley T, Chen D, Sener O, Savarese S (2017) image2mass: Estimating the Mass of an Object from Its Image, 10"},{"issue":"1","key":"209_CR8","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/BF00247522","volume":"71","author":"RS Johansson","year":"1988","unstructured":"Johansson RS, Westling G (1988) Coordinated isometric muscle commands adequately and erroneously programmed for the weight during lifting task with precision grip. Exp Brain Res 71(1):59\u201371. https:\/\/doi.org\/10.1007\/BF00247522","journal-title":"Exp Brain Res"},{"issue":"3","key":"209_CR9","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/BF00229824","volume":"83","author":"AM Gordon","year":"1991","unstructured":"Gordon AM, Forssberg H, Johansson RS, Westling G (1991) Visual size cues in the programming of manipulative forces during precision grip. Exp Brain Res 83(3):477\u2013482. https:\/\/doi.org\/10.1007\/BF00229824","journal-title":"Exp Brain Res"},{"issue":"4","key":"209_CR10","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s00221-006-0406-x","volume":"173","author":"A-M Brouwer","year":"2006","unstructured":"Brouwer A-M, Georgiou I, Glover S, Castiello U (2006) Adjusting reach to lift movements to sudden visible changes in target\u2019s weight. Exp Brain Res 173(4):629\u2013636. https:\/\/doi.org\/10.1007\/s00221-006-0406-x","journal-title":"Exp Brain Res"},{"issue":"2","key":"209_CR11","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1123\/mcj.14.2.211","volume":"14","author":"R Rein","year":"2010","unstructured":"Rein R, Button C, Davids K, Summers J (2010) Cluster analysis of movement patterns in multiarticular actions: a tutorial. Mot Control 14(2):211\u2013239","journal-title":"Mot Control"},{"issue":"2","key":"209_CR12","doi-asserted-by":"publisher","first-page":"55","DOI":"10.3182\/20120215-3-AT-3016.00009","volume":"45","author":"A Baca","year":"2012","unstructured":"Baca A (2012) Methods for recognition and classification of human motion patterns-a prerequisite for intelligent devices assisting in sports activities. IFAC Proc Vol 45(2):55\u201361","journal-title":"IFAC Proc Vol"},{"key":"209_CR13","doi-asserted-by":"crossref","unstructured":"Shetty S, Rao Y (2016) Svm based machine learning approach to identify Parkinson\u2019s disease using gait analysis. In: 2016 International Conference on Inventive Computation Technologies (ICICT), vol. 2, pp. 1\u20135 . IEEE","DOI":"10.1109\/INVENTIVE.2016.7824836"},{"issue":"1","key":"209_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10723-021-09595-7","volume":"20","author":"WS Low","year":"2022","unstructured":"Low WS, Chan CK, Chuah JH, Tee YK, Hum YC, Salim MIM, Lai KW (2022) A review of machine learning network in human motion biomechanics. J Grid Comput 20(1):1\u201337","journal-title":"J Grid Comput"},{"key":"209_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106494","volume":"94","author":"E Balaji","year":"2020","unstructured":"Balaji E, Brindha D, Balakrishnan R (2020) Supervised machine learning based gait classification system for early detection and stage classification of parkinson\u2019s disease. Appl Soft Comput 94:106494","journal-title":"Appl Soft Comput"},{"issue":"1","key":"209_CR16","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.gaitpost.2006.01.003","volume":"25","author":"F Dobson","year":"2007","unstructured":"Dobson F, Morris ME, Baker R, Graham HK (2007) Gait classification in children with cerebral palsy: a systematic review. Gait & Posture 25(1):140\u2013152","journal-title":"Gait & Posture"},{"issue":"2","key":"209_CR17","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.gaitpost.2011.09.009","volume":"35","author":"K Kaczmarczyk","year":"2012","unstructured":"Kaczmarczyk K, Wit A, Krawczyk M, Zaborski J, Gajewski J (2012) Associations between gait patterns, brain lesion factors and functional recovery in stroke patients. Gait & Posture 35(2):214\u2013217","journal-title":"Gait & Posture"},{"issue":"2","key":"209_CR18","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s10919-007-0045-3","volume":"32","author":"D Janssen","year":"2008","unstructured":"Janssen D, Sch\u00f6llhorn WI, Lubienetzki J, F\u00f6lling K, Kokenge H, Davids K (2008) Recognition of emotions in gait patterns by means of artificial neural nets. J Nonverbal Behav 32(2):79\u201392","journal-title":"J Nonverbal Behav"},{"key":"209_CR19","doi-asserted-by":"crossref","unstructured":"Horst F, Janssen D, Beckmann H, Sch\u00f6llhorn WI (2020) Can individual movement characteristics across different throwing disciplines be identified in high-performance decathletes? Front Psychol 11","DOI":"10.3389\/fpsyg.2020.02262"},{"key":"209_CR20","doi-asserted-by":"crossref","unstructured":"Hemeren P, Veto P, Thill S, Li C, Sun J (2021) Kinematic-based classification of social gestures and grasping by humans and machine learning techniques. Front Robot AI 308","DOI":"10.3389\/frobt.2021.699505"},{"issue":"1","key":"209_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep37036","volume":"6","author":"A Cavallo","year":"2016","unstructured":"Cavallo A, Koul A, Ansuini C, Capozzi F, Becchio C (2016) Decoding intentions from movement kinematics. Sci Rep 6(1):1\u20138","journal-title":"Sci Rep"},{"issue":"3","key":"209_CR22","doi-asserted-by":"publisher","first-page":"55","DOI":"10.3390\/biomimetics4030055","volume":"4","author":"HH Bekemeier","year":"2019","unstructured":"Bekemeier HH, Maycock JW, Ritter HJ (2019) What does a hand-over tell?-individuality of short motion sequences. Biomimetics 4(3):55","journal-title":"Biomimetics"},{"key":"209_CR23","volume-title":"Hybrid societies\u2014humans interacting with embodied technologies","author":"L Kopnarski","year":"2023","unstructured":"Kopnarski L, Lippert L, Voelcker-Rehage C, Potts D, Rudisch J (2023) Predicting object weights from giver\u2019s kinematics in handover actions. In: Meyer B, Thomas U, Kanoun O (eds) Hybrid societies\u2014humans interacting with embodied technologies, vol 1. Springer, Switzerland"},{"key":"209_CR24","unstructured":"Vicon M.S (2022) Plug-in Gait Reference Guide - Nexus 2.14 Documentation\u2014Vicon Documentation . https:\/\/docs.vicon.com\/display\/Nexus214\/Plug-in+Gait+Reference+Guide"},{"key":"209_CR25","doi-asserted-by":"publisher","unstructured":"Plonka G, Potts D, Steidl G, Tasche M (2018) Numerical Fourier Analysis, 1st edn. Applied and Numerical Harmonic Analysis. Birkh\u00e4user. https:\/\/doi.org\/10.1007\/978-3-030-04306-3","DOI":"10.1007\/978-3-030-04306-3"},{"issue":"3","key":"209_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"C-C Chang","year":"2011","unstructured":"Chang C-C, Lin C-J (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):1\u201327","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"209_CR27","doi-asserted-by":"publisher","unstructured":"Cunado D, Nixon MS, Carter JN (1997) Using gait as a biometric, via phase-weighted magnitude spectra. In: Big\u00fcn, J., Chollet, G., Borgefors, G. (eds.) Audio- and Video-based Biometric Person Authentication. Lecture Notes in Computer Science, pp. 93\u2013102. Springer, Berlin, Heidelberg . https:\/\/doi.org\/10.1007\/BFb0015984","DOI":"10.1007\/BFb0015984"},{"issue":"5","key":"209_CR28","doi-asserted-by":"publisher","first-page":"353","DOI":"10.3758\/BF03337021","volume":"9","author":"JE Cutting","year":"1977","unstructured":"Cutting JE, Kozlowski LT (1977) Recognizing friends by their walk: gait perception without familiarity cues. Bull Psychon Soc 9(5):353\u2013356. https:\/\/doi.org\/10.3758\/BF03337021","journal-title":"Bull Psychon Soc"},{"issue":"4","key":"209_CR29","doi-asserted-by":"publisher","first-page":"255","DOI":"10.5405\/jmbe.806","volume":"31","author":"YC Lin","year":"2011","unstructured":"Lin YC, Yang B-S, Lin YT, Yang YT (2011) Human recognition based on kinematics and kinetics of gait. J Med Biol Eng 31(4):255\u2013263. https:\/\/doi.org\/10.5405\/jmbe.806","journal-title":"J Med Biol Eng"},{"issue":"4","key":"209_CR30","doi-asserted-by":"publisher","first-page":"667","DOI":"10.3758\/BF03193523","volume":"67","author":"NF Troje","year":"2005","unstructured":"Troje NF, Westhoff C, Lavrov M (2005) Person identification from biological motion: effects of structural and kinematic cues. Perception & Psychophysics 67(4):667\u2013675. https:\/\/doi.org\/10.3758\/BF03193523","journal-title":"Perception & Psychophysics"},{"issue":"10","key":"209_CR31","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1167\/jov.22.10.3","volume":"22","author":"A Bosco","year":"2022","unstructured":"Bosco A, Bertini C, Filippini M, Foglino C, Fattori P (2022) Machine learning methods detect arm movement impairments in a patient with parieto-occipital lesion using only early kinematic information. J Vis 22(10):3. https:\/\/doi.org\/10.1167\/jov.22.10.3","journal-title":"J Vis"},{"issue":"2","key":"209_CR32","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1080\/00222899709600828","volume":"29","author":"J Shim","year":"1997","unstructured":"Shim J, Carlton LG (1997) Perception of kinematic characteristics in the motion of lifted weight. J Mot Behav 29(2):131\u2013146. https:\/\/doi.org\/10.1080\/00222899709600828","journal-title":"J Mot Behav"},{"key":"209_CR33","doi-asserted-by":"crossref","unstructured":"Bednarik R, Kinnunen T, Mihaila A, Fr\u00e4nti P (2005) Eye-Movements as a biometric. In: Proceedings of the 14th Scandinavian Conference on Image Analysis. SCIA\u201905, pp. 780\u2013789. Springer, Berlin, Heidelberg","DOI":"10.1007\/11499145_79"},{"issue":"9","key":"209_CR34","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.1080\/17470218.2014.993664","volume":"68","author":"C Girges","year":"2015","unstructured":"Girges C, Spencer J, O\u2019Brien J (2015) Categorizing identity from facial motion. Quart J Exp Psychol 68(9):1832\u20131843. https:\/\/doi.org\/10.1080\/17470218.2014.993664","journal-title":"Quart J Exp Psychol"},{"key":"209_CR35","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1037\/0096-1523.13.2.155","volume":"13","author":"GP Bingham","year":"1987","unstructured":"Bingham GP (1987) Kinematic form and scaling: further investigations on the visual perception of lifted weight. J Exp Psychol Human Percept Performance 13:155\u2013177. https:\/\/doi.org\/10.1037\/0096-1523.13.2.155","journal-title":"J Exp Psychol Human Percept Performance"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-023-00209-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-023-00209-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-023-00209-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:44Z","timestamp":1699102964000},"score":1,"resource":{"primary":{"URL":"https:\/\/braininformatics.springeropen.com\/articles\/10.1186\/s40708-023-00209-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,4]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["209"],"URL":"https:\/\/doi.org\/10.1186\/s40708-023-00209-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2727820\/v1","asserted-by":"object"}]},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,4]]},"assertion":[{"value":"23 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Ethics Committee of the Chemnitz University of Technology, Germany, Faculty of Behavioral and Social Sciences, on July 12, 2019\u2014number V-343-17-CVR-SFB_A01-24062019.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"29"}}