{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T09:50:52Z","timestamp":1780912252856,"version":"3.54.1"},"reference-count":75,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents the validation of a marker-less motion capture system used to evaluate the upper limb stress of subjects using exoskeletons for locomotion. The system fuses the human skeletonization provided by commercial 3D cameras with forces exchanged by the user to the ground through upper limbs utilizing instrumented crutches. The aim is to provide a low cost, accurate, and reliable technology useful to provide the trainer a quantitative evaluation of the impact of assisted gait on the subject without the need to use an instrumented gait lab. The reaction forces at the upper limbs\u2019 joints are measured to provide a validation focused on clinically relevant quantities for this application. The system was used simultaneously with a reference motion capture system inside a clinical gait analysis lab. An expert user performed 20 walking tests using instrumented crutches and force platforms inside the observed volume. The mechanical model was applied to data from the system and the reference motion capture, and numerical simulations were performed to assess the internal joint reaction of the subject\u2019s upper limbs. A comparison between the two results shows a root mean square error of less than 2% of the subject\u2019s body weight.<\/jats:p>","DOI":"10.3390\/s20143899","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T09:30:49Z","timestamp":1594719049000},"page":"3899","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Validation of Marker-Less System for the Assessment of Upper Joints Reaction Forces in Exoskeleton Users"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5098-6395","authenticated-orcid":false,"given":"Simone","family":"Pasinetti","sequence":"first","affiliation":[{"name":"Department of Mechanical and Industrial Engineering (DIMI), University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5530-6136","authenticated-orcid":false,"given":"Cristina","family":"Nuzzi","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering (DIMI), University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Covre","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering (DII), University of Trento, 38123 Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Luchetti","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering (DII), University of Trento, 38123 Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2110-6360","authenticated-orcid":false,"given":"Luca","family":"Maule","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering (DII), University of Trento, 38123 Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6497-5876","authenticated-orcid":false,"given":"Mauro","family":"Serpelloni","sequence":"additional","affiliation":[{"name":"Department of Information Engineering (DII), University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2301-876X","authenticated-orcid":false,"given":"Matteo","family":"Lancini","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering (DIMI), University of Brescia, 25123 Brescia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17018","DOI":"10.1038\/nrdp.2017.18","article-title":"Traumatic spinal cord injury","volume":"3","author":"Ahuja","year":"2017","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1186\/s12984-017-0338-7","article-title":"Robotic assisted gait as a tool for rehabilitation of individuals with spinal cord injury: A systematic review","volume":"14","author":"Holanda","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_3","first-page":"8228","article-title":"Healthcare sensor system exploiting instrumented crutches for force measurement during assisted gait of exoskeleton users","volume":"16","author":"Lancini","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.proeng.2014.11.745","article-title":"Wireless instrumented crutches for force and tilt monitoring in lower limb rehabilitation","volume":"87","author":"Sardini","year":"2014","journal-title":"Procedia Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3369","DOI":"10.1109\/TIM.2015.2465751","article-title":"Wireless instrumented crutches for force and movement measurements for gait monitoring","volume":"64","author":"Sardini","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lancini, M., Serpelloni, M., and Pasinetti, S. (2015, January 18\u201319). Instrumented crutches to measure the internal forces acting on upper limbs in powered exoskeleton users. Proceedings of the 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), Gallipoli, Italy.","DOI":"10.1109\/IWASI.2015.7184960"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TBME.2007.901024","article-title":"OpenSim: Open-source software to create and analyze dynamic simulations of movement","volume":"54","author":"Delp","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Meyer, J., Kuderer, M., M\u00fcller, J., and Burgard, W. (June, January 31). Online marker labeling for fully automatic skeleton tracking in optical motion capture. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907690"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"105476","DOI":"10.1155\/2010\/105476","article-title":"Marker-Based Human motion capture in multiview sequences","volume":"2010","author":"Casas","year":"2010","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.jbi.2016.07.009","article-title":"Comparison between passive vision-based system and a wearable inertial-based system for estimating temporal gait parameters related to the GAITRite electronic walkway","volume":"62","author":"Fontecha","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/JSEN.2011.2146246","article-title":"Sensors-based wearable systems for monitoring of human movement and falls","volume":"12","author":"Shany","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1109\/JSEN.2008.2012212","article-title":"Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier","volume":"9","author":"Shi","year":"2009","journal-title":"IEEE Sens. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1109\/JSEN.2013.2294351","article-title":"SIMPLE-Use\u2014Sensor Set for Wearable Movement and Interaction Research","volume":"14","author":"Neuhaeuser","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1109\/JSEN.2011.2148708","article-title":"A wearable inertial sensor node for body motion analysis","volume":"12","author":"Kan","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.sna.2007.08.028","article-title":"Wireless MEMS inertial sensor system for golf swing dynamics","volume":"141","author":"King","year":"2008","journal-title":"Sens. Actuators A Phys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11933","DOI":"10.3390\/s120911933","article-title":"A highly miniaturized, wireless inertial measurement unit for characterizing the dynamics of pitched baseballs and softballs","volume":"12","author":"McGinnis","year":"2012","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1109\/TIM.2014.2359813","article-title":"Integration of MEMS inertial and pressure sensors for vertical trajectory determination","volume":"64","author":"Zihajehzadeh","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","unstructured":"Antifakos, S., and Schiele, B. (2002, January 7\u201310). Bridging the gap between virtual and physical games using wearable sensors. Proceedings of the Sixth International Symposium on Wearable Computers, Seattle, WA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, W., and Fu, L. (2011, January 30\u201331). Mirror therapy with an exoskeleton upper-limb robot based on IMU measurement system. Proceedings of the 2011 IEEE International Symposium on Medical Measurements and Applications, Bari, Italy.","DOI":"10.1109\/MeMeA.2011.5966732"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cifuentes, C., Braidot, A., Rodr\u00edguez, L., Frisoli, M., Santiago, A., and Frizera, A. (2012, January 25\u201327). Development of a wearable ZigBee sensor system for upper limb rehabilitation robotics. Proceedings of the 2012 4th IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Rome, Italy.","DOI":"10.1109\/BioRob.2012.6290926"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.gaitpost.2007.03.018","article-title":"The reliability of using accelerometer and gyroscope for gait event identification on persons with dropped foot","volume":"27","author":"Lau","year":"2008","journal-title":"Gait Posture"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/TIM.2017.2677679","article-title":"Wearable biometric performance measurement system for combat sports","volume":"66","author":"Saponara","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3577","DOI":"10.1109\/TIM.2015.2459532","article-title":"UWB-aided inertial motion capture for lower body 3-D dynamic activity and trajectory tracking","volume":"64","author":"Zihajehzadeh","year":"2015","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3709","DOI":"10.1109\/TIM.2011.2135070","article-title":"A novel hierarchical information fusion method for three-dimensional upper limb motion estimation","volume":"60","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TIM.2016.2642658","article-title":"Improving the accuracy of human body orientation estimation with wearable IMU sensors","volume":"66","author":"Ahmed","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"27738","DOI":"10.3390\/s151127738","article-title":"A neural network-based gait phase classification method using sensors equipped on lower limb exoskeleton robots","volume":"15","author":"Jung","year":"2015","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/7333.928571","article-title":"A reliable gait phase detection system","volume":"9","author":"Pappas","year":"2001","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.robot.2014.10.012","article-title":"Recognition of gait cycle phases using wearable sensors","volume":"75","author":"Mohammed","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, D.-X., Wu, X., Du, W., Wang, C., and Xu, T. (2016). Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors. Sensors, 16.","DOI":"10.3390\/s16101579"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/j.orthres.2005.02.006","article-title":"On the influence of soft tissue coverage in the determination of bone kinematics using skin markers","volume":"23","author":"Taylor","year":"2005","journal-title":"J. Orthop. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1080\/21681163.2013.834800","article-title":"Markerless motion capture systems for tracking of persons in forensic biomechanics: An overview","volume":"2","author":"Yang","year":"2014","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Colyer, S.L., Evans, M., Cosker, D.P., and Salo, A.I.T. (2018). A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System. Sports Med. Open, 4.","DOI":"10.1186\/s40798-018-0139-y"},{"key":"ref_34","first-page":"516","article-title":"Vision-based body tracking: Turning Kinect into a clinical tool","volume":"11","author":"Morrison","year":"2016","journal-title":"Disabil. Rehabil. Assist. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pasinetti, S., Hassan, M.M., Eberhardt, J., Lancini, M., Docchio, F., and Sansoni, G. (2019). Performance Analysis of the PMD Camboard Picoflexx Time-of-Flight Camera for Markerless Motion Capture Applications. IEEE Trans. Instrum. Meas., 1\u201316.","DOI":"10.1109\/TIM.2018.2889233"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shotton, J., Fitzgibbon, A., Blake, A., Kipman, A., Finocchio, M., Moore, B., and Sharp, T. (2011, January 20\u201325). Real-Time Human Pose Recognition in Parts from a Single Depth Image. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995316"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zennaro, S., Munaro, M., Milani, S., Zanuttigh, P., Bernardi, A., Ghidoni, S., and Menegatti, E. (July, January 29). Performance evaluation of the 1st and 2nd generation Kinect for multimedia applications. Proceedings of the 2015 IEEE International Conference on Multimedia and Expo (ICME), Turin, Italy.","DOI":"10.1109\/ICME.2015.7177380"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ciabattoni, L., Ferracuti, F., Iarlori, S., Longhi, S., and Romeo, L. (2016, January 9\u201311). A novel computer vision based e-rehabilitation system: From gaming to therapy support. Proceedings of the 2016 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2016.7430515"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fankhauser, P., Bloesch, M., Rodriguez, D., Kaestner, R., Hutter, M., and Siegwart, R. (2015, January 27\u201331). Kinect v2 for mobile robot navigation: Evaluation and modeling. Proceedings of the 2015 International Conference on Advanced Robotics (ICAR), Istanbul, Turkey.","DOI":"10.1109\/ICAR.2015.7251485"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/TIM.2015.2498560","article-title":"Static and Dynamic Hand Gesture Recognition in Depth Data Using Dynamic Time Warping","volume":"65","author":"Plouffe","year":"2016","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nuzzi, C., Pasinetti, S., Pagani, R., Franco, D., and Sansoni, G. (2019, January 9\u201313). Hand gesture recognition for collaborative workstations: A smart command system prototype. Proceedings of the International Conference on Image Analysis and Processing, Trento, Italy.","DOI":"10.1007\/978-3-030-30754-7_33"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.robot.2015.10.004","article-title":"OpenPTrack: Open source multi-camera calibration and people tracking for RGB-D camera networks","volume":"75","author":"Munaro","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"24297","DOI":"10.3390\/s150924297","article-title":"Leveraging Two Kinect Sensors for Accurate Full-Body Motion Capture","volume":"15","author":"Gao","year":"2015","journal-title":"Sensors"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1109\/JBHI.2016.2558540","article-title":"Automated Analysis and Quantification of Human Mobility Using a Depth Sensor","volume":"21","author":"Leightley","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1186\/1743-0003-11-108","article-title":"Systematic review of Kinect applications in elderly care and stroke rehabilitation","volume":"11","author":"Webster","year":"2014","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1016\/j.jbiomech.2014.07.017","article-title":"Suitability of Kinect for measuring whole body movement patterns during exergaming","volume":"47","author":"Stegenga","year":"2014","journal-title":"J. Biomech."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1186\/s12984-017-0270-x","article-title":"Markerless motion capture systems as training device in neurological rehabilitation: A systematic review of their use, application, target population and efficacy","volume":"14","author":"Knippenberg","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Leightley, D., Darby, J., Li, B., McPhee, J.S., and Yap, M.H. (2013, January 13\u201316). Human Activity Recognition for Physical Rehabilitation. Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK.","DOI":"10.1109\/SMC.2013.51"},{"key":"ref_49","unstructured":"Theofanidis, M., Lioulemes, A., and Makedon, F. (July, January 29). A Motion and Force Analysis System for Human Upper-limb Exercises. Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Corfu Island, Greece."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.gaitpost.2013.09.018","article-title":"Validity and reliability of the Kinect within functional assessment activities: Comparison with standard stereophotogrammetry","volume":"39","author":"Jansen","year":"2014","journal-title":"Gait Posture"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"21382","DOI":"10.1109\/ACCESS.2017.2759801","article-title":"Rule-Based Human Motion Tracking for Rehabilitation Exercises: Realtime Assessment, Feedback, and Guidance","volume":"5","author":"Zhao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jbiomech.2018.01.008","article-title":"An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept","volume":"69","author":"Capecci","year":"2018","journal-title":"J. Biomech."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Abbondanza, P., Giancola, S., Sala, R., and Tarabini, M. (2017, January 14\u201315). Accuracy of the Microsoft Kinect System in the Identification of the Body Posture. Proceedings of the Wireless Mobile Communication and Healthcare, Vienna, Austria.","DOI":"10.1007\/978-3-319-58877-3_37"},{"key":"ref_54","unstructured":"Cecco, M.D., Fornaser, A., Tomasin, P., Zanetti, M., Guandalini, G., Ianes, P.G., Pilla, F., Nollo, G., Valente, M., and Pisoni, T. (2017, January 12\u201315). Augmented Reality to Enhance the Clinician\u2019s Observation During Assessment of Daily Living Activities. Proceedings of the Augmented Reality, Virtual Reality, and Computer Graphics 4th International Conference, Ugento, Italy."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"M\u00fcller, B., Ilg, W., Giese, M.A., and Ludolph, N. (2017). Validation of enhanced kinect sensor based motion capturing for gait assessment. PLoS ONE, 12.","DOI":"10.1101\/098863"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Geerse, D.J., Coolen, B.H., and Roerdink, M. (2015). Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait Assessments. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0139913"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"274","DOI":"10.3109\/03091902.2014.909540","article-title":"Comparative abilities of Microsoft Kinect and Vicon 3D motion capture for gait analysis","volume":"38","author":"Pfister","year":"2014","journal-title":"J. Med. Eng. Technol."},{"key":"ref_58","unstructured":"Steward, J., Lichti, D.D., Chow, D., Ferber, R., and Osis, S.T. (2015, January 17\u201321). Performance Assessment and Calibration of the Kinect 2.0 Time-of-Flight Range Camera for Use in Motion Capture Applications. Proceedings of the FIG Working week, Sofia, Bulgaria."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Otte, K., Kayser, B., Mansow-Model, S., Verrel, J., Paul, F., Brandt, A.U., and Schmitz-H\u00fcbsch, T. (2016). Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0166532"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.robot.2017.09.007","article-title":"Automatic graph based spatiotemporal extrinsic calibration of multiple Kinect V2 ToF cameras","volume":"98","author":"Fornaser","year":"2017","journal-title":"Robot. Auton. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wei, T., Lee, B., Qiao, Y., Kitsikidis, A., Dimitropoulos, K., and Grammalidis, N. (2015, January 8\u201310). Experimental study of skeleton tracking abilities from microsoft kinect non-frontal views. Proceedings of the 2015 3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON), Lisbon, Portugal.","DOI":"10.1109\/3DTV.2015.7169367"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Hicks, J.L., Uchida, T.K., Seth, A., Rajagopal, A., and Delp, S.L. (2015). Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement. J. Biomech. Eng., 137.","DOI":"10.1115\/1.4029304"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.jbiomech.2007.10.001","article-title":"The influence of simulation model complexity on the estimation of internal loading in gymnastics landings","volume":"41","author":"Mills","year":"2008","journal-title":"J. Biomech."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Seth, A., Hicks, J.L., Uchida, T.K., Habib, A., Dembia, C.L., Dunne, J.J., Ong, C.F., DeMers, M.S., Rajagopal, A., and Millard, M. (2018). OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput. Biol., 14.","DOI":"10.1371\/journal.pcbi.1006223"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TIE.2015.2497662","article-title":"Robust Real-Time Bio-Kinematic Movement Tracking Using Multiple Kinects for Tele-Rehabilitation","volume":"63","author":"Pathirana","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"65","DOI":"10.5772\/62415","article-title":"Multiple Kinect Sensor Fusion for Human Skeleton Tracking Using Kalman Filtering","volume":"13","author":"Moon","year":"2016","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_67","unstructured":"Li, S., Pathirana, P.N., and Caelli, T. (2014, January 26\u201330). Multi-kinect skeleton fusion for physical rehabilitation monitoring. Proceedings of the EMBC 2014, 36th Annual international conference of the IEEE engineering in medicine and biology society, Chicago, IL, USA."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"16589","DOI":"10.3390\/s150716589","article-title":"An Ambulatory System for Gait Monitoring Based on Wireless Sensorized Insoles","volume":"15","author":"Fontecha","year":"2015","journal-title":"Sensors"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Winter, D.A. (2009). Biomechanics and Motor Control of Human Movement, John Wiley&Sons. [4th ed.].","DOI":"10.1002\/9780470549148"},{"key":"ref_70","unstructured":"Lund, M.E., Andersen, M.S., de Zee, M., and Rasmussen, J. (2011, January 3\u20137). Functional Scaling of Musculoskeletal Models. Proceedings of the Congress of the International Society of Biomechanics, ISB, Brussels, Belgium."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/0167-9457(91)90046-Z","article-title":"A gait analysis data collection and reduction technique","volume":"10","author":"Davis","year":"1991","journal-title":"Hum. Mov. Sci."},{"key":"ref_72","unstructured":"Alvarez, M., Torricelli, D., del-Ama, A., Fern\u00e1ndez, D.P., Gonzalez-Vargas, J., Moreno, J., Gil-Agudo, A., and Pons, J. (July, January 27). Simultaneous estimation of human and exoskeleton motion: A simplified protocol. Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), Zurich, Switzerland."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Mantovani, G., and Lamontagne, M. (2017). How Different Marker Sets Affect Joint Angles in Inverse Kinematics Framework. J. Biomech. Eng., 139.","DOI":"10.1115\/1.4034708"},{"key":"ref_74","first-page":"353","article-title":"Gait analysis: Normal and pathological function","volume":"9","author":"Burnfield","year":"2010","journal-title":"J. Sports Sci. Med."},{"key":"ref_75","unstructured":"Welch, G., and Bishop, G. (2020, July 10). An Introduction to the Kalman Filter. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.336.5576&rep=rep1&type=pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3899\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:59Z","timestamp":1760176259000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/14\/3899"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,13]]},"references-count":75,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["s20143899"],"URL":"https:\/\/doi.org\/10.3390\/s20143899","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,13]]}}}