{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T22:35:35Z","timestamp":1767825335208,"version":"3.49.0"},"reference-count":107,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the project \u201cFAIR\u2013Future Artificial Intelligence Research\u201d","award":["PE00000013"],"award-info":[{"award-number":["PE00000013"]}]},{"name":"the project \u201cFAIR\u2013Future Artificial Intelligence Research\u201d","award":["H23C22000860006"],"award-info":[{"award-number":["H23C22000860006"]}]},{"name":"Italian National Recovery and Resilience Plan"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030.<\/jats:p>","DOI":"10.3390\/jsan15010008","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T12:34:21Z","timestamp":1767789261000},"page":"8","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Measurement Uncertainty and Traceability in Upper Limb Rehabilitation Robotics: A Metrology-Oriented Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4559-9662","authenticated-orcid":false,"given":"Ihtisham Ul","family":"Haq","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Modelling, Electronics and Systems Engineering, University of Calabria, 87036 Arcavacata di Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0933-9902","authenticated-orcid":false,"given":"Francesco","family":"Felicetti","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Modelling, Electronics and Systems Engineering, University of Calabria, 87036 Arcavacata di Rende, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6263-8929","authenticated-orcid":false,"given":"Francesco","family":"Lamonaca","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Modelling, Electronics and Systems Engineering, University of Calabria, 87036 Arcavacata di Rende, Italy"},{"name":"Consiglio Nazionale delle Ricerche\u2013Istituto di Nanotecnologia (CNR\u2013NANOTEC), Institute of Nanotechnology, 87036 Arcavacata di Rende, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"ref_1","unstructured":"GBD 2019 Stroke Collaborators (2021). Global, regional, and national burden of stroke and its risk factors, 1990\u20132019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol., 20, 795\u2013820."},{"key":"ref_2","unstructured":"World Health Organization (2025, October 29). Stroke: Key Facts, Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/stroke."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1161\/01.STR.32.6.1279","article-title":"Estimates of the prevalence of acute stroke impairments: A systematic review","volume":"32","author":"Lawrence","year":"2001","journal-title":"Stroke"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fang, Z., Woodford, S., Senanayake, D., and Ackland, D. (2023). Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review. Sensors, 23.","DOI":"10.3390\/s23146535"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Villa, G., Cerfoglio, S., Bonfiglio, A., Capodaglio, P., Galli, M., and Cimolin, V. (2025). Validation of a Commercially Available IMU-Based System Against an Optoelectronic System for Full-Body Motor Tasks. Sensors, 25.","DOI":"10.20944\/preprints202505.0883.v1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Akbari, S., Bussmann, J.B., Zgonnikov, A., Grauwmeijer, E., Evers, M., and Horemans, H.L. (Res. Sq., 2025). IMU-Derived Kinematic Characterization of Drinking Task in Healthy Individuals and Stroke Survivors with Upper Extremity Impairments, Res. Sq., in press.","DOI":"10.21203\/rs.3.rs-7481086\/v1"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cr\u00e9vecoeur, F., Mathew, J., Bastin, M., and Lef\u00e8vre, P. (2013). Priors Engaged in Long-Latency Responses to Mechanical Perturbations. PLoS Comput. Biol., 9.","DOI":"10.1371\/journal.pcbi.1003177"},{"key":"ref_8","unstructured":"Promwongsa, N., Ebrahimzadeh, A., Naboulsi, D., Kianpisheh, S., Belqasmi, F., Glitho, R., Crespi, N., and Alfandi, O. (2023). Latency Requirements for Haptics and Teleoperation: Typical Acceptable Ranges (10\u2013100 ms) for Medium-Dynamic Tasks. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TRO.2015.2503726","article-title":"Minimal Assist-as-Needed Controller for Upper Limb Robotic Rehabilitation","volume":"32","author":"Pehlivan","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/S0167-9457(99)00023-8","article-title":"Measurement of human motion: Comparison of commercially available systems","volume":"18","author":"Richards","year":"1999","journal-title":"Hum. Mov. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.gaitpost.2004.04.004","article-title":"Human movement analysis using stereophotogrammetry. Part 2: Instrumental errors","volume":"21","author":"Chiari","year":"2005","journal-title":"Gait Posture"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.jbiomech.2017.05.006","article-title":"Accuracy map of an optical motion capture system with 42 or 21 cameras in a large measurement volume","volume":"58","author":"Aurand","year":"2017","journal-title":"J. Biomech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110162","DOI":"10.1016\/j.jbiomech.2020.110162","article-title":"Quantification of the errors associated with marker occlusion in stereophotogrammetric systems and implications on gait analysis","volume":"114","author":"Conconi","year":"2021","journal-title":"J. Biomech."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1111\/j.1600-0838.2010.01274.x","article-title":"Scapular positioning and movement in unimpaired shoulders, shoulder impingement syndrome, and glenohumeral instability","volume":"21","author":"Struyf","year":"2011","journal-title":"Scand. J. Med. Sci. Sports"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"071007","DOI":"10.1115\/1.4053366","article-title":"Compensating for Soft-Tissue Artifact Using the Orientation of Distal Limb Segments During Electromagnetic Motion Capture of the Upper Limb","volume":"144","author":"Bons","year":"2022","journal-title":"ASME J. Biomech. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF02345966","article-title":"Measuring orientation of human body segments using miniature gyroscopes and accelerometers","volume":"43","author":"Luinge","year":"2005","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1109\/TBME.2006.889184","article-title":"Ambulatory Position and Orientation Tracking Fusing Magnetic and Inertial Sensing","volume":"54","author":"Roetenberg","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s11517-007-0296-5","article-title":"Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors","volume":"46","author":"Cutti","year":"2008","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1109\/TAC.2008.923738","article-title":"Nonlinear Complementary Filters on the Special Orthogonal Group","volume":"53","author":"Mahony","year":"2008","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.apergo.2017.04.011","article-title":"Effect of local magnetic field disturbances on inertial measurement units accuracy","volume":"63","author":"Mecheri","year":"2017","journal-title":"Appl. Ergon."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1109\/TNSRE.2005.847353","article-title":"Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation","volume":"13","author":"Roetenberg","year":"2005","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e28","DOI":"10.1017\/wtc.2025.10003","article-title":"Novel Magnetometer-Free IMU-Based Orientation Estimation Approach for Measuring Upper Limb Kinematics","volume":"6","author":"Baklouti","year":"2025","journal-title":"Wearable Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"28576","DOI":"10.1109\/JSEN.2024.3436532","article-title":"IMU-based systems for upper limb kinematic analysis in clinical applications: A systematic review","volume":"24","author":"Favata","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cerfoglio, S., Capodaglio, P., Rossi, P., Conforti, I., D\u2019angeli, V., Milani, E., Galli, M., and Cimolin, V. (2023). Evaluation of Upper Body and Lower Limbs Kinematics through an IMU-Based Medical System: A Comparative Study with the Optoelectronic System. Sensors, 23.","DOI":"10.3390\/s23136156"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Unger, T., de Sousa Ribeiro, R., Mokni, M., Weikert, T., Pohl, J., Schwarz, A., Held, J.P.O., Sauerzopf, L., K\u00fchnis, B., and Gavagnin, E. (2024). Upper limb movement quality measures: Comparing IMUs and optical motion capture in stroke patients performing a drinking task. Front. Digit. Health, 6.","DOI":"10.3389\/fdgth.2024.1359776"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lee, J.K., and Jeon, T.H. (2018, January 28\u201331). IMU-Based but Magnetometer-Free Joint Angle Estimation of Constrained Links. Proceedings of the 2018 IEEE SENSORS, New Delhi, India.","DOI":"10.1109\/ICSENS.2018.8589825"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Favata, A., Marzabal-Gatell, A., Font-Llagunes, J.M., and P\u00e0mies-Vil\u00e0, R. (2025). Feasibility study of a sensor-to-segment calibration method to enhance upper limb motion analysis using an IMU-based system for clinical and home environments. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0334177"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mundt, M., Johnson, W.R., Potthast, W., Markert, B., Mian, A., and Alderson, J. (2021). A comparison of three neural network approaches for estimating joint angles and moments from inertial measurement units. Sensors, 21.","DOI":"10.3390\/s21134535"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1038\/s41467-025-58624-6","article-title":"Learning-based 3D human kinematics estimation using behavioral constraints from activity classification","volume":"16","author":"Kim","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110552","DOI":"10.1016\/j.jbiomech.2021.110552","article-title":"Real-time conversion of inertial measurement unit data to ankle joint angles using deep neural networks","volume":"125","author":"Senanayake","year":"2021","journal-title":"J. Biomech."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Abdalla, M., and Ashour, M. (2025, January 12\u201313). Enhancing Inertial Measurement Unit Based Posture and Motion Estimation through Multi-Modal Training Using Video Posture Detection Models. Proceedings of the 2025 Intelligent Methods, Systems, and Applications (IMSA), Giza, Egypt.","DOI":"10.1109\/IMSA65733.2025.11166801"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Negri, V., Mingotti, A., Tinarelli, R., and Peretto, L. (2025, January 28\u201330). Uncertainty-Aware Human Activity Recognition: Investigating Sensor Impact in ML Models. Proceedings of the 2025 IEEE Medical Measurements & Applications (MeMeA), Chania, Greece.","DOI":"10.1109\/MeMeA65319.2025.11067967"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3942","DOI":"10.1109\/TNSRE.2024.3486173","article-title":"Kinematic Assessment of Upper Limb Movements using the ArmeoPower Robotic Exoskeleton","volume":"32","author":"Knill","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/TRO.2006.886270","article-title":"Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking","volume":"22","author":"Yun","year":"2006","journal-title":"IEEE Trans. Robot."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Gan, K.B., Aziz, N.A.A., Huong, A., and You, H.W. (2023). Upper Limb Joint Angle Estimation Using Wearable IMUs and Personalized Calibration Algorithm. Mathematics, 11.","DOI":"10.3390\/math11040970"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/s12984-025-01605-z","article-title":"Systematic review of AI\/ML applications in multi-domain robotic rehabilitation: Trends, gaps, and future directions","volume":"22","author":"Nicora","year":"2025","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.csbj.2025.08.018","article-title":"CKATool: A clinical kinematic analysis toolbox for upper limb rehabilitation","volume":"28","author":"Latif","year":"2025","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhu, J., Ye, Z., Liu, R., and Liu, J. (2025). Inertial measurement units (IMUs) for biomechanical analysis in sport: A review of applications, challenges and future directions. Sens. Rev., ahead-of-print.","DOI":"10.1108\/SR-04-2025-0261"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"180968","DOI":"10.1109\/ACCESS.2024.3508029","article-title":"Human Digital Twins in Rehabilitation: A Case Study on Exoskeleton and Serious-Game-Based Stroke Rehabilitation Using the ETHICA Methodology","volume":"12","author":"Cash","year":"2024","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e58078","DOI":"10.2196\/58078","article-title":"Trade-Offs Between Simplifying Inertial Measurement Unit\u2013Based Movement Recordings and the Attainability of Different Levels of Analyses: Systematic Assessment of Method Variations","volume":"13","author":"Airaksinen","year":"2025","journal-title":"JMIR MHealth UHealth"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yeo, S.S., and Park, G.Y. (2020). Accuracy verification of spatio-temporal and kinematic parameters for gait using inertial measurement unit system. Sensors, 20.","DOI":"10.3390\/s20051343"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_44","unstructured":"Hammerla, N.Y., Halloran, S., and Pl\u00f6tz, T. (2016). Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kasnesis, P., Patrikakis, C.Z., and Venieris, I.S. (2018, January 6\u20137). PerceptionNet: A deep convolutional neural network for late sensor fusion. Proceedings of the SAI Intelligent Systems Conference, London, UK.","DOI":"10.1007\/978-3-030-01054-6_7"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"467","DOI":"10.5312\/wjo.v12.i7.467","article-title":"Technological advancements in the analysis of human motion and posture management through digital devices","volume":"12","author":"Roggio","year":"2021","journal-title":"World J. Orthop."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1186\/s12984-020-00779-y","article-title":"Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments","volume":"17","author":"Rast","year":"2020","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bergmann, J.H.M., Langdon, P., Mayagoitia, R.E., and Howard, N. (2014). Exploring the Use of Sensors to Measure Behavioral Interactions: An Experimental Evaluation of Using Hand Trajectories. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0088080"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.medengphy.2021.03.010","article-title":"The importance of inertial measurement unit placement in assessing upper limb motion","volume":"92","author":"Grip","year":"2021","journal-title":"Med. Eng. Phys."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1109\/JSEN.2021.3137305","article-title":"Effects of IMU sensor-to-segment misalignment and orientation error on 3-D knee joint angle estimation","volume":"22","author":"Fan","year":"2021","journal-title":"IEEE Sen. J."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Crenna, F., Rossi, G.B., and Berardengo, M. (2021). Filtering biomechanical signals in movement analysis. Sensors, 21.","DOI":"10.3390\/s21134580"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Alemayoh, T.T., Lee, J.H., and Okamoto, S. (2023). Leg-joint angle estimation from a single inertial sensor attached to various lower-body links during walking motion. Appl. Sci., 13.","DOI":"10.3390\/app13084794"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9182","DOI":"10.3390\/s111009182","article-title":"Kalman-filter-based orientation determination using inertial\/magnetic sensors: Observability analysis and performance evaluation","volume":"11","author":"Sabatini","year":"2011","journal-title":"Sensors"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"14449","DOI":"10.1038\/s41598-019-50759-z","article-title":"Upper limb joint kinematics using wearable magnetic and inertial measurement units: An anatomical calibration procedure based on bony landmark identification","volume":"9","author":"Picerno","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"042003","DOI":"10.1088\/1742-6596\/1087\/4\/042003","article-title":"A review of wearable IMU (inertial-measurement-unit)-based pose estimation and drift reduction technologies","volume":"1087","author":"Zhao","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Falbriard, M., Meyer, F., Mariani, B., Millet, G.P., and Aminian, K. (2020). Drift-free foot orientation estimation in running using wearable IMU. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.00065"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/MAES.2019.2927898","article-title":"On computational complexity reduction methods for Kalman filter extensions","volume":"34","author":"Raitoharju","year":"2019","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"S\u00e4rkk\u00e4, S., and Svensson, L. (2023). Bayesian Filtering and Smoothing, Cambridge University Press.","DOI":"10.1017\/9781108917407"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"19302","DOI":"10.3390\/s150819302","article-title":"Keeping a good attitude: A quaternion-based orientation filter for IMUs and MARGs","volume":"15","author":"Valenti","year":"2015","journal-title":"Sensors"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"117265","DOI":"10.1016\/j.measurement.2025.117265","article-title":"Attitude estimation using an adaptive generalized complementary filter","volume":"251","author":"Wu","year":"2025","journal-title":"Measurement"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Caruso, M., Sabatini, A.M., Laidig, D., Seel, T., Knaflitz, M., Della Croce, U., and Cereatti, A. (2021). Analysis of the accuracy of ten algorithms for orientation estimation using inertial and magnetic sensing under optimal conditions: One size does not fit all. Sensors, 21.","DOI":"10.3390\/s21072543"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/1743-0003-11-3","article-title":"A survey on robotic devices for upper limb rehabilitation","volume":"11","author":"Maciejasz","year":"2014","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_63","first-page":"107","article-title":"Effects of robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis","volume":"19","author":"Veerbeek","year":"2019","journal-title":"BMC Health Serv. Res."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Pezenka, L., and Wirth, K. (2025). Reliability of a low-cost Inertial Measurement Unit (IMU) to measure punch and kick velocity. Sensors, 25.","DOI":"10.3390\/s25020307"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"23098","DOI":"10.1038\/s41598-024-73557-8","article-title":"Validation and user experience of a dry electrode based Health Patch for heart rate and respiration rate monitoring","volume":"14","author":"Wei","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ravanelli, N., Lefebvre, K., Brough, A., Paquette, S., and Lin, W. (2025). Validation of an Open-Source Smartwatch for Continuous Monitoring of Physical Activity and Heart Rate in Adults. Sensors, 25.","DOI":"10.3390\/s25092926"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"110223","DOI":"10.1016\/j.isci.2024.110223","article-title":"SolunumWear: A smart textile system for dynamic respiration monitoring across various postures","volume":"27","author":"Cay","year":"2024","journal-title":"iScience"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Feigl, T., Kram, S., Woller, P., Siddiqui, R.H., Philippsen, M., and Mutschler, C. (October, January 30). A bidirectional LSTM for estimating dynamic human velocities from a single IMU. Proceedings of the 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa, Italy.","DOI":"10.1109\/IPIN.2019.8911814"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Choffin, Z., Jeong, N., Callihan, M., Sazonov, E., and Jeong, S. (2022). Lower body joint angle prediction using machine learning and applied biomechanical inverse dynamics. Sensors, 23.","DOI":"10.3390\/s23010228"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ni\u00f1o-Tejada, K., Salda\u00f1a-Aristiz\u00e1bal, L., Rivas-Caicedo, J.L., and Patarroyo-Montenegro, J.F. (2025). Upper Limb Joint Angle Estimation Using a Reduced Number of IMU Sensors and Recurrent Neural Networks. Electronics, 14.","DOI":"10.3390\/electronics14153039"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Rivas-Caicedo, J.L., Ni\u00f1o-Tejada, K., Salda\u00f1a-Aristizabal, L., and Patarroyo-Montenegro, J.F. (2025). A Distributed Wearable Computing Framework for Human Activity Classification. Electronics, 14.","DOI":"10.3390\/electronics14163203"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Rivas-Caicedo, J.L., Salda\u00f1a-Aristizabal, L., Ni\u00f1o-Tejada, K., and Patarroyo-Montenegro, J.F. (2025). A Multi-Sensor Dataset for Human Activity Recognition Using Inertial and Orientation Data. Data, 10.","DOI":"10.3390\/data10080129"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"18090","DOI":"10.1038\/s41598-025-01710-y","article-title":"Empowering stroke recovery with upper limb rehabilitation monitoring using TinyML based heterogeneous classifiers","volume":"15","author":"Xie","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.21014\/actaimeko.v14i3.2124","article-title":"Intelligent robotic positioning through AI-enhanced metrology: Integration of standards, sensor fusion, and adaptive calibration","volume":"14","author":"Haq","year":"2025","journal-title":"Acta IMEKO"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.medengphy.2017.12.005","article-title":"Kinematic measures for upper limb robot-assisted therapy following stroke and correlations with clinical outcome measures: A review","volume":"53","author":"Tran","year":"2018","journal-title":"Med. Eng. Phys."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Goffredo, M., Pournajaf, S., Proietti, S., Gison, A., Posteraro, F., and Franceschini, M. (2021). Retrospective Robot-Measured Upper Limb Kinematic Data From Stroke Patients Are Novel Biomarkers. Front. Neurol., 12.","DOI":"10.3389\/fneur.2021.803901"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1177\/15459683241270066","article-title":"Wearable-Based Kinematic Analysis of Upper-Limb Movements During Daily Activities Could Provide Insights into Stroke Survivors\u2019 Motor Ability","volume":"38","author":"Lee","year":"2024","journal-title":"Neurorehabilit. Neural Repair"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1161\/STROKEAHA.118.023531","article-title":"Systematic review on kinematic assessments of upper limb movements after stroke","volume":"50","author":"Schwarz","year":"2019","journal-title":"Stroke"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"46","DOI":"10.21037\/atm-25-46","article-title":"Wearable devices in neurological disorders: A narrative review of status quo and perspectives","volume":"13","author":"Cai","year":"2025","journal-title":"Ann. Transl. Med."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Farabolini, G., Baldini, N., Pagano, A., Andrenelli, E., Pepa, L., Morone, G., Ceravolo, M.G., and Capecci, M. (2025). Continuous movement monitoring at home through wearable devices: A systematic review. Sensors, 25.","DOI":"10.3390\/s25164889"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Moyle, W., Hong, M., and Aw, K. (2025). From Sensors to Care: How Robotic Skin Is Transforming Modern Healthcare\u2014A Mini Review. Sensors, 25.","DOI":"10.3390\/s25092895"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"4317","DOI":"10.1007\/s00542-018-3769-6","article-title":"An IMU-Compensated Skeletal Tracking System Using Kinect for the Upper Limb","volume":"24","author":"Du","year":"2018","journal-title":"Microsyst. Technol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1109\/TSMC.2018.2870290","article-title":"Nonlinear stochastic attitude filters on the special orthogonal group 3: Ito and stratonovich","volume":"49","author":"Hashim","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1109\/TSMCB.2009.2016571","article-title":"Head Orientation Prediction: Delta Quaternions Versus Quaternions","volume":"39","author":"Himberg","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"042001","DOI":"10.1088\/2516-1091\/adeb1e","article-title":"Post-stroke upper limb rehabilitation: Clinical practices, compensatory movements, assessment, and trends","volume":"7","author":"Rocha","year":"2025","journal-title":"Prog. Biomed. Eng."},{"key":"ref_86","unstructured":"Hassan, M. (2025). Domain Adaptation for Cross-Domain alignment in Human Activity Recognition Using Device-Free Sensing. [Ph.D. Thesis, The University of St Andrews]."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"25432","DOI":"10.1109\/ACCESS.2025.3536837","article-title":"Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport","volume":"13","author":"Pandukabhaya","year":"2025","journal-title":"IEEE Access"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Sabaty, A., Fishman, A., Batcir, S., Tan, T., Shull, P.B., Levy, K., and Fischer, A.G. (2025). Novel Deep Learning Model to Estimate Knee Flexion and Adduction Moments with Wearable IMUs during Treadmill and Overground Walking. IEEE J. Biomed. Health Inform., 1\u201311.","DOI":"10.1109\/JBHI.2025.3584389"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Wang, Y., Han, X., Xin, B., and Zhao, P. (2025). Innovations in Upper Limb Rehabilitation Robots: A Review of Mechanisms, Optimization, and Clinical Applications. Robotics, 14.","DOI":"10.3390\/robotics14060081"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Hochreiter, D., Schmermbeck, K., Vazquez-Pufleau, M., and Ferscha, A. (2025). Intention Prediction for Active Upper-Limb Exoskeletons in Industrial Applications: A Systematic Literature Review. Sensors, 25.","DOI":"10.3390\/s25175225"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s12984-018-0366-y","article-title":"Advanced Robotic Therapy Integrated Centers (ARTIC): An international collaboration facilitating the application of rehabilitation technologies","volume":"15","author":"Network","year":"2018","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"5705","DOI":"10.1109\/LRA.2025.3562792","article-title":"Physics-Informed Neural Networks with Unscented Kalman Filter for Sensorless Joint Torque Estimation in Humanoid Robots","volume":"10","author":"Sorrentino","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_93","unstructured":"Pan, C.W. (2025). Model-Based Machine Learning for Physiological Wearables in Healthcare and Human-Robot Interaction. [Ph.D. Thesis, Arizona State University]."},{"key":"ref_94","unstructured":"Ferrante, L., Sridharan, M., Zito, C., and Farina, D. (2022). Toward a Framework for Adaptive Impedance Control of an Upper-limb Prosthesis. arXiv."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.neucom.2015.01.071","article-title":"Upper limb motion tracking with the integration of IMU and Kinect","volume":"159","author":"Tian","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_96","unstructured":"Nguiadem, C., Raison, M., and Achiche, S. (2021). A Test Bench for Evaluating Exoskeletons for Upper Limb Rehabilitation. arXiv."},{"key":"ref_97","unstructured":"(2014). Robots and Robotic Devices\u2014Safety Requirements for Personal Care Robots (Standard No. ISO 13482:2014)."},{"key":"ref_98","unstructured":"(2019). Medical Electrical Equipment\u2014Part 2-78: Particular Requirements for Basic Safety and Essential Performance of Medical Robots for Rehabilitation, Assessment, Compensation or Alleviation (Standard No. IEC 80601-2-78:2019)."},{"key":"ref_99","unstructured":"(2023). IEEE Standard for Specification of Sensor Interface for Cyber and Physical Worlds (Standard No. IEEE Std 2888.1-2023)."},{"key":"ref_100","unstructured":"(2024). IEEE Standard for Orchestration of Digital Synchronization between Cyber and Physical Worlds (Standard No. IEEE Std 2888.3-2024)."},{"key":"ref_101","unstructured":"(2015). IEEE Standard Ontologies for Robotics and Automation (Standard No. IEEE Std 1872-2015)."},{"key":"ref_102","unstructured":"(2022). IEEE Standard for Autonomous Robotics (AuR) Ontology (Standard No. IEEE Std 1872.2-2022)."},{"key":"ref_103","unstructured":"(2017). General Requirements for the Competence of Testing and Calibration Laboratories (Standard No. ISO\/IEC 17025:2017)."},{"key":"ref_104","unstructured":"(2019). Security Techniques\u2014Extension to ISO\/IEC 27001 and ISO\/IEC 27002 for Privacy Information Management\u2014Requirements and Guidelines (Standard No. ISO\/IEC 27701:2019)."},{"key":"ref_105","unstructured":"(2016). General Data Protection Regulation (GDPR) (Standard No. Regulation (EU) 2016\/679)."},{"key":"ref_106","unstructured":"(2019). Quantities and Units\u2014Part 4: Mechanics (Standard No. ISO 80000-4:2019)."},{"key":"ref_107","unstructured":"Bureau International des Poids et Mesures (BIPM) (2019). The International System of Units (SI), BIPM. [9th ed.]."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/15\/1\/8\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T12:46:55Z","timestamp":1767790015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/15\/1\/8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,7]]},"references-count":107,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["jsan15010008"],"URL":"https:\/\/doi.org\/10.3390\/jsan15010008","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,7]]}}}