{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T17:34:30Z","timestamp":1775756070230,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T00:00:00Z","timestamp":1717545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010190","name":"United States Army Combat Capabilities and Development Command","doi-asserted-by":"publisher","award":["W15QKN-17-9-1025"],"award-info":[{"award-number":["W15QKN-17-9-1025"]}],"id":[{"id":"10.13039\/100010190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92\u00b0 vs. 3.32\u00b0 at the ankle, 8.78\u00b0 vs. 12.54\u00b0 at the knee, and 5.48\u00b0 vs. 9.67\u00b0 at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35\u00b0 vs. 5.55\u00b0 at the ankle, 20.29\u00b0 vs. 20.71\u00b0 at the knee, and 14.86\u00b0 vs. 13.55\u00b0 at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.<\/jats:p>","DOI":"10.3390\/s24113657","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T05:59:42Z","timestamp":1717567182000},"page":"3657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0689-9531","authenticated-orcid":false,"given":"David","family":"Hollinger","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2516-0292","authenticated-orcid":false,"suffix":"Jr.","given":"Mark C.","family":"Schall","sequence":"additional","affiliation":[{"name":"Department of Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA"}]},{"given":"Howard","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Industrial & Systems Engineering and Engineering Management, University of Alabama-Huntsville, Huntsville, AL 35899, USA"}]},{"given":"Michael","family":"Zabala","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"210816","DOI":"10.1109\/ACCESS.2020.3037715","article-title":"Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey","volume":"8","author":"Demrozi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Coker, J., Chen, H., Schall, M.C., Gallagher, S., and Zabala, M. (2021). EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee. Sensors, 21.","DOI":"10.3390\/s21113622"},{"key":"ref_3","unstructured":"McGhan, C., Nasir, A., and Atkins, E. (2012). Infotech@Aerospace 2012, American Institute of Aeronautics and Astronautics."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cervantes, C., De Mesa, M., Ramos, J., Singer, S., Del Carmen, D.J., and Cajote, R.D. (2023, January 31). Multi-Stage Hybrid-CNN Transformer Model for Human Intent-Prediction. Proceedings of the TENCON 2023\u20142023 IEEE Region 10 Conference (TENCON), Chiang Mai, Thailand.","DOI":"10.1109\/TENCON58879.2023.10322395"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.bspc.2019.02.011","article-title":"A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration","volume":"51","author":"Bi","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tang, G., Wang, H., and Tian, Y. (2017, January 26\u201327). sEMG-Based Estimation of Knee Joint Angles and Motion Intention Recognition. Proceedings of the 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China.","DOI":"10.1109\/IHMSC.2017.199"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107192","DOI":"10.1016\/j.compbiomed.2023.107192","article-title":"Improving performance of human action intent recognition: Analysis of gait recognition machine learning algorithms and optimal combination with inertial measurement units","volume":"163","author":"Liu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"78","DOI":"10.3389\/frobt.2018.00078","article-title":"Fusion of Bilateral Lower-Limb Neuromechanical Signals Improves Prediction of Locomotor Activities","volume":"5","author":"Hu","year":"2018","journal-title":"Front. Robot. AI"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Gurchiek, R.D., Cheney, N., and McGinnis, R.S. (2019). Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques. Sensors, 19.","DOI":"10.20944\/preprints201911.0006.v1"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1109\/JSEN.2020.3019016","article-title":"Wearable Sensors for Real-Time Kinematics Analysis in Sports: A Review","volume":"21","author":"Rana","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Weizman, Y., Tirosh, O., Fuss, F.K., Tan, A.M., and Rutz, E. (2022). Recent State of Wearable IMU Sensors Use in People Living with Spasticity: A Systematic Review. Sensors, 22.","DOI":"10.3390\/s22051791"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Prathivadi, Y., Wu, J., Bennett, T.R., and Jafari, R. (2014, January 2\u20135). Robust activity recognition using wearable IMU sensors. Proceedings of the 2014 IEEE SENSORS, Valencia, Spain.","DOI":"10.1109\/ICSENS.2014.6985041"},{"key":"ref_14","unstructured":"(2023, July 10). Exoskeletons Need to React Faster than Physiological Responses to Improve Standing Balance. Available online: https:\/\/www.science.org\/doi\/10.1126\/scirobotics.adf1080."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.robot.2014.08.012","article-title":"A survey of sensor fusion methods in wearable robotics","volume":"73","author":"Novak","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Farrell, M.T., and Herr, H. (September, January 30). A method to determine the optimal features for control of a powered lower-limb prostheses. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091493"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kazemimoghadam, M., and Fey, N.P. (2021). Continuous Classification of Locomotion in Response to Task Complexity and Anticipatory State. Front. Bioeng. Biotechnol., 9.","DOI":"10.3389\/fbioe.2021.628050"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kazemimoghadam, M., and Fey, N.P. (2020). Biomechanical Signals of Varied Modality and Location Contribute Differently to Recognition of Transient Locomotion. Sensors, 20.","DOI":"10.3390\/s20185390"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Niswander, W., Wang, W., and Kontson, K. (2020). Optimization of IMU Sensor Placement for the Measurement of Lower Limb Joint Kinematics. Sensors, 20.","DOI":"10.3390\/s20215993"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1109\/TMRB.2020.3011841","article-title":"Continuous Prediction of Joint Angular Positions and Moments: A Potential Control Strategy for Active Knee-Ankle Prostheses","volume":"2","author":"Dey","year":"2020","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Molinaro, D.D., Kang, I., Camargo, J., and Young, A.J. (December, January 29). Biological Hip Torque Estimation using a Robotic Hip Exoskeleton. Proceedings of the 2020 8th IEEE RAS\/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA.","DOI":"10.1109\/BioRob49111.2020.9224334"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1109\/TMRB.2023.3260261","article-title":"The Influence of Gait Phase on Predicting Lower-Limb Joint Angles","volume":"5","author":"Hollinger","year":"2023","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3389\/fbioe.2020.00041","article-title":"Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network","volume":"8","author":"Mundt","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"29973","DOI":"10.1109\/ACCESS.2019.2900591","article-title":"Intelligent Prediction of Human Lower Extremity Joint Moment: An Artificial Neural Network Approach","volume":"7","author":"Xiong","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jbiomech.2013.09.032","article-title":"Statistical method for prediction of gait kinematics with Gaussian process regression","volume":"47","author":"Yun","year":"2014","journal-title":"J. Biomech."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"056027","DOI":"10.1088\/1741-2560\/11\/5\/056027","article-title":"Within-socket myoelectric prediction of continuous ankle kinematics for control of a powered transtibial prosthesis","volume":"11","author":"Farmer","year":"2014","journal-title":"J. Neural Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rokhmanova, N., Kuchenbecker, K.J., Shull, P.B., Ferber, R., and Halilaj, E. (2022). Predicting knee adduction moment response to gait retraining with minimal clinical data. PLoS Comput. Biol., 18.","DOI":"10.1371\/journal.pcbi.1009500"},{"key":"ref_28","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"},{"key":"ref_29","unstructured":"Dey, S., Yoshida, T., Foerster, R.H., Ernst, M., Schmalz, T., Carnier, R.M., and Schilling, A.F. (2021). A hybrid approach for dynamically training a torque prediction model for devising a human-machine interface control strategy. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103386","DOI":"10.1016\/j.apergo.2021.103386","article-title":"Classifying diverse manual material handling tasks using a single wearable sensor","volume":"93","author":"Porta","year":"2021","journal-title":"Appl. Ergon."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103126","DOI":"10.1016\/j.autcon.2020.103126","article-title":"Effective inertial sensor quantity and locations on a body for deep learning-based worker\u2019s motion recognition","volume":"113","author":"Kim","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qiu, Z., Peng, D., Wang, F., Wang, Z., Qiu, S., Shi, X., and Chu, Q. (2023). Prediction of Joint Angles Based on Human Lower Limb Surface Electromyography. Sensors, 23.","DOI":"10.3390\/s23125404"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.artint.2013.10.003","article-title":"Algorithm runtime prediction: Methods & evaluation","volume":"206","author":"Hutter","year":"2014","journal-title":"Artif. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1260\/2040-2295.4.4.555","article-title":"Measurement of lower limb joint kinematics using inertial sensors during stair ascent and descent in healthy older adults and stroke survivors","volume":"4","author":"Laudanski","year":"2013","journal-title":"J. Healthc. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ramachandran, A.K., Pedley, J.S., Moeskops, S., Oliver, J.L., Myer, G.D., and Lloyd, R.S. (2024). Changes in Lower Limb Biomechanics Across Various Stages of Maturation and Implications for ACL Injury Risk in Female Athletes: A Systematic Review. Sports Med.","DOI":"10.1007\/s40279-024-02022-3"},{"key":"ref_36","first-page":"2159","article-title":"Falls in the Elderly","volume":"61","author":"Fuller","year":"2000","journal-title":"Am. Fam. Physician"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, H., Schall, M.C., Martin, S.M., and Fethke, N.B. (2023). Drift-Free Joint Angle Calculation Using Inertial Measurement Units without Magnetometers: An Exploration of Sensor Fusion Methods for the Elbow and Wrist. Sensors, 23.","DOI":"10.3390\/s23167053"},{"key":"ref_38","unstructured":"Segal, M. (2003). Machine Learning Benchmarks and Random Forest Regression, Center for Bioinformatics & Molecular Biostatistics, University of California. Technical Report."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"279","DOI":"10.4300\/JGME-D-12-00156.1","article-title":"Using Effect Size\u2014Or Why the P Value Is Not Enough","volume":"4","author":"Sullivan","year":"2012","journal-title":"J. Grad. Med. Educ."},{"key":"ref_40","first-page":"320","article-title":"Sensor Positioning for Activity Recognition Using Wearable Accelerometers","volume":"5","author":"Atallah","year":"2011","journal-title":"IEEE J. Mag."},{"key":"ref_41","unstructured":"Garreis, S.R. (2019). Optimal Control under Uncertainty: Theory and Numerical Solution with Low-Rank Tensors. [Doctoral Dissertation, Technische Universit\u00e4t M\u00fcnchen]."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hippe, P., and Deutscher, J. (2009). Design of Observer-Based Compensators: From the Time to the Frequency Domain, Springer.","DOI":"10.1007\/978-1-84882-537-6"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nurse, C.A., Elstub, L.J., Volgyesi, P., and Zelik, K.E. (2023). How Accurately Can Wearable Sensors Assess Low Back Disorder Risks during Material Handling? Exploring the Fundamental Capabilities and Limitations of Different Sensor Signals. Sensors, 23.","DOI":"10.3390\/s23042064"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"103574","DOI":"10.1016\/j.apergo.2021.103574","article-title":"The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review","volume":"98","author":"Chan","year":"2022","journal-title":"Appl. Ergon."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Matijevich, E.S., Volgyesi, P., and Zelik, K.E. (2021). A Promising Wearable Solution for the Practical and Accurate Monitoring of Low Back Loading in Manual Material Handling. Sensors, 21.","DOI":"10.3390\/s21020340"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1109\/TII.2022.3189648","article-title":"IMU and Smartphone Camera Fusion for Knee Adduction and Knee Flexion Moment Estimation During Walking","volume":"19","author":"Tan","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"127107","DOI":"10.1109\/ACCESS.2023.3332116","article-title":"Machine Learning Based Recognition of Elements in Lower-Limb Movement Sequence for Proactive Control of Exoskeletons to Assist Lifting","volume":"11","author":"Woo","year":"2023","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lim, S. (2024). Exposures to Several Risk Factors can be Estimated from a Continuous Stream of Inertial Sensor Measurements during a Variety of Lifting-Lowering Tasks. SSRN Electron. J., 1\u201316.","DOI":"10.1080\/00140139.2024.2343949"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"205","DOI":"10.3109\/17453678208992202","article-title":"Normal Range of Motion of the Hip, Knee and Ankle Joints in Male Subjects, 30\u201340 Years of Age","volume":"53","author":"Roaas","year":"1982","journal-title":"Acta Orthop. Scand."},{"key":"ref_50","unstructured":"Zabala, M., and Hollinger, D. (2023, January 8\u201311). Enhancing Model Generalizability via Transfer Learning for Multi-action Intent Recognition. Proceedings of the 2023 American Society of Biomechanics Meeting, Knoxville, TN, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1038\/s41598-023-29314-4","article-title":"Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment","volume":"13","author":"Donahue","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11517-016-1504-y","article-title":"A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials","volume":"55","author":"Aziz","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"147","DOI":"10.4258\/hir.2017.23.3.147","article-title":"Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data","volume":"23","author":"Ahmed","year":"2017","journal-title":"Healthc. Inform. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3657\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:53:59Z","timestamp":1760108039000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3657"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,5]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113657"],"URL":"https:\/\/doi.org\/10.3390\/s24113657","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,5]]}}}