{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T14:31:59Z","timestamp":1769005919892,"version":"3.49.0"},"reference-count":37,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["811504"],"award-info":[{"award-number":["811504"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Intent sensing\u2014the ability to sense what a user wants to happen\u2014has many potential technological applications. Assistive medical devices, such as prosthetic limbs, could benefit from intent-based control systems, allowing for faster and more intuitive control. The accuracy of intent sensing could be improved by using multiple sensors sensing multiple environments. As users will typically pass through different sensing environments throughout the day, the system should be dynamic, with sensors dropping in and out as required. An intent-sensing algorithm that allows for this cannot rely on training from only a particular combination of sensors. It should allow any (dynamic) combination of sensors to be used. Therefore, the objective of this study is to develop and test a dynamic intent-sensing system under changing conditions. A method has been proposed that treats each sensor individually and combines them using Bayesian sensor fusion. This approach was tested on laboratory data obtained from subjects wearing Inertial Measurement Units and surface electromyography electrodes. The proposed algorithm was then used to classify functional reach activities and compare the performance to an established classifier (k-nearest-neighbours) in cases of simulated sensor dropouts. Results showed that the Bayesian sensor fusion algorithm was less affected as more sensors dropped out, supporting this intent-sensing approach as viable in dynamic real-world scenarios.<\/jats:p>","DOI":"10.3390\/s22072603","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"2603","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0121-0734","authenticated-orcid":false,"given":"Joseph","family":"Russell","sequence":"first","affiliation":[{"name":"Natural Interaction Lab, Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Parks Road, Oxford OX1 3PJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7306-2630","authenticated-orcid":false,"given":"Jeroen H. 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Med. Robot. Bionics"},{"key":"ref_2","unstructured":"Karam, A., Alnajjar, F., and Gochoo, M. (April, January 4). Assistive and rehabilitation robotics for upper limb impairments in post-stroke patients: Evaluation criteria for the design and functionality. Proceedings of the Advances in Science and Engineering Technology International Conference, Dubai, United Arab Emirates."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Navarathna, P., Bequette, B.W., and Cameron, F. (2018, January 27\u201329). Wearable Device Based Activity Recognition and Prediction for Improved Feedforward Control. Proceedings of the 2018 Annual American Control Conference (ACC), Milwaukee, WI, USA.","DOI":"10.23919\/ACC.2018.8430775"},{"key":"ref_4","first-page":"105","article-title":"Global prevalence of traumatic non-fatal limb amputation","volume":"2","author":"McDonald","year":"2020","journal-title":"Prosthet. Orthot. Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3389\/fnbot.2016.00007","article-title":"The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control","volume":"10","author":"Chadwell","year":"2016","journal-title":"Front. Neurorobot."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lee Chang, M., Gutierrez, R.A., Khante, P., Schaertl Short, E., and Lockerd Thomaz, A. (2018, January 1\u20135). Effects of Integrated Intent Recognition and Communication on Human-Robot Collaboration. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593359"},{"key":"ref_7","first-page":"2","article-title":"Historical aspects of powered limb prostheses","volume":"9","author":"Childress","year":"1985","journal-title":"Clin. Prosthet. Orthot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/TNSRE.2017.2682642","article-title":"IMU-Based Wrist Rotation Control of a Transradial Myoelectric Prosthesis","volume":"26","author":"Bennett","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Comito, C., Falcone, D., and Forestiero, A. (2020, January 16\u201319). Current Trends and Practices in Smart Health Monitoring and Clinical Decision Support. Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea.","DOI":"10.1109\/BIBM49941.2020.9313449"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/MPRV.2020.2977739","article-title":"Smart Homes","volume":"19","author":"Brush","year":"2020","journal-title":"IEEE Pervasive Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bergmann, J., Noble, A., and Thompson, M. (2016, January 14\u201317). Probabilistic sensor network design. Proceedings of the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA, USA.","DOI":"10.1109\/BSN.2016.7516234"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4061\/2011\/404605","article-title":"Some Applications of Bayes\u2019 Rule in Probability Theory to Electrocatalytic Reaction Engineering","volume":"2011","author":"Fahidy","year":"2011","journal-title":"Int. J. Electrochem."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20150245","DOI":"10.1098\/rsta.2015.0245","article-title":"Quantum Bayesianism as the basis of general theory of decision-making","volume":"374","author":"Khrennikov","year":"2016","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, R., and Liu, M. (2010, January 18\u201320). Recognizing human activities based on multi-sensors fusion. Proceedings of the 2010 4th International Conference on Bioinformatics and Biomedical Engineering, Chengdu, China.","DOI":"10.1109\/ICBBE.2010.5514802"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, K., Yun, G., Park, S.K., and Kim, D.H. (2019, January 23\u201327). Fall Detection for the Elderly Based on 3-Axis Accelerometer and Depth Sensor Fusion with Random Forest Classifier. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856698"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vasuhi, S., Vaidehi, V., and Midhunkrishna, P.R. (2011, January 14\u201316). Multiple target tracking using Support Vector Machine and data fusion. Proceedings of the 2011 Third International Conference on Advanced Computing, Chennai, India.","DOI":"10.1109\/ICoAC.2011.6165210"},{"key":"ref_17","unstructured":"Nagaraja, V., Cheng, R., Kwong, E., Bergmann, J.H., Andersen, M.S., and Thompson, M.S. (2019, January 20\u201323). Marker-based vs. Inertial-based Motion Capture: Musculoskeletal Modelling of Upper Extremity Kinetics. Proceedings of the 2019 Trent International Prosthetics Symposium (TIPS\u201919), Manchester, UK."},{"key":"ref_18","unstructured":"(2021, August 10). XSens XSens Mtw Awinda. Available online: https:\/\/www.xsens.com\/products\/mtw-awinda."},{"key":"ref_19","unstructured":"(2021, December 24). Xsens MVN Analyze. Available online: https:\/\/www.xsens.com\/products\/mvn-analyze."},{"key":"ref_20","unstructured":"(2021, August 10). Vicon Vicon Nexus. Available online: https:\/\/www.vicon.com\/software\/nexus\/."},{"key":"ref_21","unstructured":"(2022, February 26). Xsens Syncronising Xsens Systems with Vicon Nexus. Available online: https:\/\/www.xsens.com\/hubfs\/Downloads\/pluginstools\/SynchronisingXsenswithVicon.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nazmi, N., Rahman, M.A.A., Yamamoto, S.I., Ahmad, S.A., Zamzuri, H., and Mazlan, S.A. (2016). A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors, 16.","DOI":"10.3390\/s16081304"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17","DOI":"10.32474\/OAJBEB.2018.01.000104","article-title":"A Comprehensive Study on EMG Feature Extraction and Classifiers","volume":"1","author":"Spiewak","year":"2018","journal-title":"Open Access J. Biomed. Eng. Biosci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Osman, H., Ghafari, M., and Nierstrasz, O. (2017, January 21). Hyperparameter optimization to improve bug prediction accuracy. Proceedings of the 2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE), Klagenfurt, Austria.","DOI":"10.1109\/MALTESQUE.2017.7882014"},{"key":"ref_25","unstructured":"Ruano, P., Delgado, L.L., Picco, S., Villegas, L., Tonelli, F., Merlo, M., Rigau, J., Diaz, D., and Masuelli, M. (2016). Artificial Human Arm Driven by EMG Signal, Intech."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Al-Faiz, M.Z., Ali, A.A., and Miry, A.H. (December, January 30). A k-nearest neighbor based algorithm for human arm movements recognition using EMG signals. Proceedings of the 2010 1st International Conference on Energy, Power and Control, Basrah, Iraq.","DOI":"10.37917\/ijeee.6.2.12"},{"key":"ref_27","unstructured":"Daniel, W.W. (1990). Spearman rank correlation coefficient. Applied Nonparametric Statistics, PWS-Kent."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1152\/japplphysiol.01251.2011","article-title":"A biomechanical model for encoding joint dynamics: Applications to transfemoral prosthesis control","volume":"112","author":"McGibbon","year":"2012","journal-title":"J. Appl. Physiol."},{"key":"ref_29","first-page":"11","article-title":"Online Human Activity Recognition on Smart Phones","volume":"16","author":"Kose","year":"2012","journal-title":"Perform. Eval."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Russell, J., and Bergmann, J. (2019, January 10\u201312). Probabilistic sensor design for healthcare technology. Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York City, NY, USA.","DOI":"10.1109\/UEMCON47517.2019.8993086"},{"key":"ref_31","unstructured":"(2022, February 13). Medical Center Orthotics & Prosthetics Arm & Hand Prosthetics. Available online: https:\/\/mcopro.com\/blog\/resources\/arm-hand-prosthetics."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Marino, M., Pattni, S., Greenberg, M., Miller, A., Hocker, E., Ritter, S., and Mehta, K. (2015, January 8\u201311). Access to prosthetic devices in developing countries: Pathways and challenges. Proceedings of the 2015 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA.","DOI":"10.1109\/GHTC.2015.7343953"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2196\/jmir.2208","article-title":"Classification accuracies of physical activities using smartphone motion sensors","volume":"14","author":"Wu","year":"2012","journal-title":"J. Med. Internet Res."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E., and Casson, A.J. (2019). Machine learning algorithm validation with a limited sample size. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0224365"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-019-0211-6","article-title":"Upper limb activity of twenty myoelectric prosthesis users and twenty healthy anatomically intact adults","volume":"6","author":"Chadwell","year":"2019","journal-title":"Sci. Data"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wei, S.J., Zhang, B., Tan, X.W., Zhao, X.G., and Ye, D. (2020, January 27\u201329). A Real-time Human Activity Recognition Approach with Generalization Performance. Proceedings of the Chinese Control Conference CCC, Shenyang, China.","DOI":"10.23919\/CCC50068.2020.9188860"},{"key":"ref_37","unstructured":"Jolliffe, I.T. (2002). Definition and Derivation of Principal Components. Principal Component Analysis, Springer."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2603\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:45:26Z","timestamp":1760136326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2603"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,29]]},"references-count":37,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072603"],"URL":"https:\/\/doi.org\/10.3390\/s22072603","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,29]]}}}