{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:45:38Z","timestamp":1778575538240,"version":"3.51.4"},"reference-count":57,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KAIST-funded Global Singularity Research Program for 2019","award":["N11190176"],"award-info":[{"award-number":["N11190176"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.<\/jats:p>","DOI":"10.3390\/s20010130","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T10:28:43Z","timestamp":1577183323000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":139,"title":["Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning"],"prefix":"10.3390","volume":"20","author":[{"given":"Hyerim","family":"Lim","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea"}]},{"given":"Bumjoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea"}]},{"given":"Sukyung","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1097\/00005768-199908000-00002","article-title":"Ground reaction forces, bone characteristics, and tibial stress fracture in male runners","volume":"31","author":"Crossley","year":"1999","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1136\/bjsports-2015-094924","article-title":"Do runners who suffer injuries have higher vertical ground reaction forces than those who remain injury-free? A systematic review and meta-analysis","volume":"50","author":"Vrielink","year":"2016","journal-title":"Br. J. Sports Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.clinbiomech.2010.08.005","article-title":"The relationship between lower-extremity stress fractures and the ground reaction force: A systematic review","volume":"26","author":"Zadpoor","year":"2011","journal-title":"Clin. Biomech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1249\/MSS.0b013e31817ed272","article-title":"Risk factors and mechanisms of knee injury in runners","volume":"40","author":"Messier","year":"2008","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1177\/0363546506288753","article-title":"Knee angular impulse as a predictor of patellofemoral pain in runners","volume":"34","author":"Stefanyshyn","year":"2006","journal-title":"Am. J. Sports Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1016\/j.clinbiomech.2014.06.001","article-title":"Forward propulsion asymmetry is indicative of changes in plantarflexor coordination during walking in individuals with post-stroke hemiparesis","volume":"29","author":"Allen","year":"2014","journal-title":"Clin. Biomech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1161\/01.STR.0000204063.75779.8d","article-title":"Anterior-posterior ground reaction forces as a measure of paretic leg contribution in hemiparetic walking","volume":"37","author":"Bowden","year":"2006","journal-title":"Stroke"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1016\/j.apmr.2007.05.027","article-title":"Relationships between muscle activity and anteroposterior ground reaction forces in hemiparetic walking","volume":"88","author":"Turns","year":"2007","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Karatsidis, A., Bellusci, G., Schepers, H., de Zee, M., Andersen, M., and Veltink, P. (2017). Estimation of ground reaction forces and moments during gait using only inertial motion capture. Sensors, 17.","DOI":"10.3390\/s17010075"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1016\/j.jbiomech.2013.07.036","article-title":"Prediction of ground reaction forces during gait based on kinematics and a neural network model","volume":"46","author":"Oh","year":"2013","journal-title":"J. Biomech."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1590\/2446-4740.06817","article-title":"Prediction of 3D ground reaction forces during gait based on accelerometer data","volume":"34","author":"Leporace","year":"2018","journal-title":"Res. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.jbiomech.2018.06.006","article-title":"Estimation of vertical ground reaction force during running using neural network model and uniaxial accelerometer","volume":"76","author":"Ngoh","year":"2018","journal-title":"J. Biomech."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guo, Y., Storm, F., Zhao, Y., Billings, S., Pavic, A., Mazz\u00e0, C., and Guo, L.-Z. (2017). A new proxy measurement algorithm with application to the estimation of vertical ground reaction forces using wearable sensors. Sensors, 17.","DOI":"10.3390\/s17102181"},{"key":"ref_14","unstructured":"Johnson, W.R., Mian, A., Robinson, M.A., Verheul, J., Lloyd, D.G., and Alderson, J. (August, January 31). Multidimensional ground reaction forces predicted from a single sacrum-mounted accelerometer via deep learning. Proceedings of the ISB\/ASB 2019, Calgary, AB, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/2.485892","article-title":"Global optimization for neural network training","volume":"29","author":"Shang","year":"1996","journal-title":"Computer"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/S0377-2217(97)00292-0","article-title":"Global optimization for artificial neural networks: A tabu search application","volume":"106","author":"Sexton","year":"1998","journal-title":"Eur. J. Oper. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/TEVC.2004.826076","article-title":"On the computation of all global minimizers through particle swarm optimization","volume":"8","author":"Parsopoulos","year":"2004","journal-title":"IEEE Trans. Evolut. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.asoc.2015.11.038","article-title":"Global optimization of open pit mining complexes with uncertainty","volume":"40","author":"Goodfellow","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1162\/neco.1989.1.4.425","article-title":"Learning in Artificial Neural Networks: A Statistical Perspective","volume":"1","author":"White","year":"1989","journal-title":"Neural. Comput."},{"key":"ref_20","first-page":"233","article-title":"Nonparametric error estimation methods for evaluating and validating artificial neural network prediction models","volume":"Volume 3","author":"Twomey","year":"1993","journal-title":"Intelligent Engineering Systems Through Artificial Neural Networks"},{"key":"ref_21","unstructured":"Zurada, J.M., Malinowski, A., and Cloete, I. (June, January 30). Sensitivity Analysis for Minimization of Input Data Dimension for Feedforward Neural Network. Proceedings of the IEEE International Symposium on Circuits and Systems\u2014ISCAS\u201994, London, UK."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1061\/(ASCE)1084-0699(2009)14:3(286)","article-title":"Investigation of Internal Functioning of the Radial-Basis-Function Neural Network River Flow Forecasting Models","volume":"14","author":"Fernando","year":"2009","journal-title":"J. Hydrol. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1007\/s11517-018-1802-7","article-title":"Predicting athlete ground reaction forces and moments from motion capture","volume":"56","author":"Johnson","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_24","first-page":"2861","article-title":"Compliant leg behaviour explains basic dynamics of walking and running","volume":"273","author":"Geyer","year":"2006","journal-title":"Proc. Biol. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.jbiomech.2013.09.012","article-title":"Compliant bipedal model with the center of pressure excursion associated with oscillatory behavior of the center of mass reproduces the human gait dynamics","volume":"47","author":"Jung","year":"2014","journal-title":"J. Biomech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1016\/j.jbiomech.2011.02.072","article-title":"Leg stiffness increases with speed to modulate gait frequency and propulsion energy","volume":"44","author":"Kim","year":"2011","journal-title":"J. Biomech."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.jbiomech.2013.09.011","article-title":"Resonance-based oscillations could describe human gait mechanics under various loading conditions","volume":"47","author":"Lee","year":"2014","journal-title":"J. Biomech."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"011013","DOI":"10.1115\/1.3005147","article-title":"A simple mass-spring model with roller feet can induce the ground reactions observed in human walking","volume":"131","author":"Whittington","year":"2009","journal-title":"J. Biomech. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jbiomech.2018.01.031","article-title":"Kinematics of lower limbs during walking are emulated by springy walking model with a compliantly connected, off-centered curvy foot","volume":"71","author":"Lim","year":"2018","journal-title":"J. Biomech."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.jbiomech.2019.05.020","article-title":"A bipedal compliant walking model generates periodic gait cycles with realistic swing dynamics","volume":"91","author":"Lim","year":"2019","journal-title":"J. Biomech."},{"key":"ref_31","unstructured":"Drillis, R., Contini, R., and Bluestein, M. (1969). Body Segment Parameters, New York University, School of Engineering and Science."},{"key":"ref_32","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in pytorch. Proceedings of the the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vathsangam, H., Emken, B.A., Schroeder, E.T., Spruijt-Metz, D., and Sukhatme, G.S. (2010). Energy Estimation of Treadmill Walking using On-body Accelerometers and Gyroscopes. IEEE Eng Med Bio, 6497\u20136501.","DOI":"10.1109\/IEMBS.2010.5627365"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.gaitpost.2007.11.001","article-title":"Predicting lower limb joint kinematics using wearable motion sensors","volume":"28","author":"Findlow","year":"2008","journal-title":"Gait Posture"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1115\/1.1427703","article-title":"Energetics of actively powered locomotion using the simplest walking model","volume":"124","author":"Kuo","year":"2002","journal-title":"J. Biomech. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3717","DOI":"10.1242\/jeb.205.23.3717","article-title":"Mechanical work for step-to-step transitions is a major determinant of the metabolic cost of human walking","volume":"205","author":"Donelan","year":"2002","journal-title":"J. Exp. Biol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1126\/science.aal5054","article-title":"Human-in-the-loop optimization of exoskeleton assistance during walking","volume":"356","author":"Zhang","year":"2017","journal-title":"Science"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.jbiomech.2010.08.024","article-title":"A gravitational impulse model predicts collision impulse and mechanical work during a step-to-step transition","volume":"44","author":"Yeom","year":"2011","journal-title":"J. Biomech."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1109\/TSMCB.2008.927722","article-title":"Subject Recognition Based on Ground Reaction Force Measurements of Gait Signals","volume":"38","author":"Moustakidis","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.1038\/s41598-019-38748-8","article-title":"Explaining the unique nature of individual gait patterns with deep learning","volume":"9","author":"Horst","year":"2019","journal-title":"Sci. Rep. UK"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.gaitpost.2013.08.028","article-title":"An individual-specific gait pattern prediction model based on generalized regression neural networks","volume":"39","author":"Luu","year":"2014","journal-title":"Gait Posture"},{"key":"ref_42","first-page":"794","article-title":"Prediction of Joint Kinetics Based on Joint Kinematics Using Artificial Neural Networks","volume":"36","author":"Mundt","year":"2018","journal-title":"ISBS Proc. Arch."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4422","DOI":"10.1016\/j.eswa.2013.11.003","article-title":"Human lower extremity joint moment prediction: A wavelet neural network approach","volume":"41","author":"Ardestani","year":"2014","journal-title":"Expert. Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chapman, R.M., Torchia, M.T., Bell, J.E., and Van Citters, D.W. (2019). Assessing Shoulder Biomechanics of Healthy Elderly Individuals During Activities of Daily Living Using Inertial Measurement Units: High Maximum Elevation Is Achievable but Rarely Used. J. Biomech. Eng., 141.","DOI":"10.1115\/1.4042433"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.gaitpost.2010.11.024","article-title":"Minimal detectable change for gait variables collected during treadmill walking in individuals post-stroke","volume":"33","author":"Kesar","year":"2011","journal-title":"Gait Posture"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s12283-015-0185-3","article-title":"Quantifying the effects of load carriage and fatigue under load on sacral kinematics during countermovement vertical jump with IMU-based method","volume":"19","author":"McGinnis","year":"2016","journal-title":"Sports Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"218","DOI":"10.3389\/fphys.2018.00218","article-title":"Estimation of vertical ground reaction forces and sagittal knee kinematics during running using three inertial sensors","volume":"9","author":"Wouda","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Thiel, D.V., Shepherd, J., Espinosa, H.G., Kenny, M., Fischer, K., Worsey, M., Matsuo, A., and Wada, T. (2018, January 26\u201329). Predicting ground reaction forces in sprint running using a shank mounted inertial measurement unit. Proceedings of the 12th Conference of the International Sports Engineering Association, Brisbane, Australia.","DOI":"10.3390\/proceedings2060199"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Stetter, B.J., Ringhof, S., Krafft, F.C., Sell, S., and Stein, T. (2019). Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning. Sensors, 19.","DOI":"10.3390\/s19173690"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1249\/MSS.0b013e31829efcf7","article-title":"Forefoot Strikers Exhibit Lower Running-Induced Knee Loading than Rearfoot Strikers","volume":"45","author":"Kulmala","year":"2013","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1111\/sms.13228","article-title":"Kinetic risk factors of running-related injuries in female recreational runners","volume":"28","author":"Napier","year":"2018","journal-title":"Scand. J. Med. Sci. Sports"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2407","DOI":"10.1007\/s00586-013-2845-y","article-title":"Asymmetrical gait in adolescents with idiopathic scoliosis","volume":"22","author":"Yang","year":"2013","journal-title":"Eur. Spine J."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Gonzalez, I., Fontecha, J., Hervas, R., and Bravo, J. (2016). Estimation of Temporal Gait Events from a Single Accelerometer Through the Scale-Space Filtering Idea. J. Med. Syst., 40.","DOI":"10.1007\/s10916-016-0612-4"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.gaitpost.2016.08.012","article-title":"Gait event detection in laboratory and real life settings: Accuracy of ankle and waist sensor based methods","volume":"50","author":"Storm","year":"2016","journal-title":"Gait Posture"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.gaitpost.2017.06.019","article-title":"A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms","volume":"57","author":"Caldas","year":"2017","journal-title":"Gait Posture"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.gaitpost.2012.02.019","article-title":"An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data","volume":"36","author":"McCamley","year":"2012","journal-title":"Gait Posture"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Forsman, P.M., Toppila, E.M., and Haeggstrom, E.O. (2009, January 3\u20136). Wavelet Analysis to Detect Gait Events. Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA.","DOI":"10.1109\/IEMBS.2009.5333137"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:45:15Z","timestamp":1760190315000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,24]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010130"],"URL":"https:\/\/doi.org\/10.3390\/s20010130","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,24]]}}}