{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:28:55Z","timestamp":1760146135207,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"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>The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we developed a prototype system that acquires multiple physiological signals with different mechanisms under a low burden and detects VIMS from the collected data. Signals during VIMS were recorded from nine healthy adult males operating excavator simulators equipped with multiple displays and a head-mounted display. Light gradient-boosting machine-based VIMS detection binary classification models were constructed using approximately 30,000 s of time-series data, comprising 23 features derived from the physiological signals. These models were validated using leave-one-out cross-validation on seven participants who experienced severe VIMS and evaluated through area under the curve (AUC) scores. The mean receiver operating characteristic curve AUC score was 0.84, and the mean precision\u2013recall curve AUC score was 0.71. All features were incorporated into the models, with saccade frequency and skin conductance response identified as particularly important. These trends aligned with subjective assessments of VIMS severity. This study contributes to advancing the use of remote-controlled machinery by addressing a critical challenge to operator performance and safety.<\/jats:p>","DOI":"10.3390\/s24196465","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T07:30:18Z","timestamp":1728286218000},"page":"6465","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Combined Method Comprising Low Burden Physiological Measurements with Dry Electrodes and Machine Learning for Classification of Visually Induced Motion Sickness in Remote-Controlled Excavator"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8222-934X","authenticated-orcid":false,"given":"Naohito","family":"Yoshioka","sequence":"first","affiliation":[{"name":"Graduate School of Robotics and Design, Osaka Institute of Technology, Chayamachi 1-45, Osaka 530-0013, Japan"},{"name":"Research and Development Center, Yanmar Holdings Co., Ltd., Umegahara 2481, Maibara 521-8511, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroki","family":"Takeuchi","sequence":"additional","affiliation":[{"name":"Graduate School of Robotics and Design, Osaka Institute of Technology, Chayamachi 1-45, Osaka 530-0013, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhuo","family":"Shu","sequence":"additional","affiliation":[{"name":"Research and Development Center, Yanmar Holdings Co., Ltd., Umegahara 2481, Maibara 521-8511, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taro","family":"Okamatsu","sequence":"additional","affiliation":[{"name":"Research and Development Center, Yanmar Holdings Co., Ltd., Umegahara 2481, Maibara 521-8511, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nobuyuki","family":"Araki","sequence":"additional","affiliation":[{"name":"Research and Development Center, Yanmar Holdings Co., Ltd., Umegahara 2481, Maibara 521-8511, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshiyuki","family":"Kamakura","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Osaka Institute of Technology, Kitayama 1-79-1, Hirakata 573-0196, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0006-7853","authenticated-orcid":false,"given":"Mieko","family":"Ohsuga","sequence":"additional","affiliation":[{"name":"Faculty of Robotics and Design, Osaka Institute of Technology, Chayamachi 1-45, Osaka 530-0013, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"ref_1","unstructured":"(2023, February 13). MLIT Japan. Available online: https:\/\/www.mlit.go.jp\/tec\/i-construction\/index.html."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.displa.2007.09.002","article-title":"A theory on visually induced motion sickness","volume":"29","author":"Bos","year":"2008","journal-title":"Displays"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ijpsycho.2022.03.006","article-title":"Detecting and predicting visually induced motion sickness with physiological measures in combination with machine learning techniques","volume":"176","author":"Keshavarz","year":"2022","journal-title":"Int. J. Psychophysiol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Moteki, M., Akihiko, N., Yuta, S., Mishima, H., and Fujino, K. (2016, January 18\u201321). Research on Improving Work Efficiency of Unmanned Construction Improving Work Efficiency of Unmanned Construction by Using Appropriate Visual Information and Necessity of Haptic Function for Construction Machine Operation Interface. Proceedings of the 33rd International Symposium on Automation and Robotics in Construction, Auburn, AL, USA.","DOI":"10.22260\/ISARC2016\/0058"},{"key":"ref_5","unstructured":"Matsuyama, A., Minoshima, M., Watanabe, H., Ujike, H., and Endou, H. (2019, January 9\u201311). Visually Induced Motion Sickness in Operation of Remote Control Hydraulic Excavator. Proceedings of the 19th Symposium on Construction Robotics in Japan, Fukuoka, Japan."},{"key":"ref_6","first-page":"15","article-title":"Remote Controlled Construction Equipment by Using High-resolution Stereoscopic 3D Images","volume":"73","author":"Ito","year":"2017","journal-title":"J. Jpn. Soc. Civ. Eng. Ser. F3 Civ. Eng. Inform."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1007\/s00221-015-4312-y","article-title":"Inter-hemispheric desynchronization of the human MT+ during visually induced motion sickness","volume":"233","author":"Miyazaki","year":"2015","journal-title":"Exp. Brain Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1007\/s11760-022-02164-9","article-title":"VIMSNet: An effective network for visually induced motion sickness detection","volume":"16","author":"Liu","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_9","unstructured":"Yoshioka, N., Takeuchi, H., Shu, Y., Okamatsu, T., Araki, N., Kamakura, Y., and Ohsuga, M. (2024, January 25\u201329). Low Burden Multidimensional Physiological Measurements Toward Estimation of Excavator Operator Condition. Proceedings of the 22nd Triennial Congress of International Ergonomics Association, Jeju, Republic of Korea."},{"key":"ref_10","unstructured":"(2023, March 30). COCOMI. Available online: https:\/\/www.toyobo.co.jp\/discover\/materials\/cocomi\/."},{"key":"ref_11","unstructured":"Sumitomo Bakelite Co., Ltd. (2024, May 10). Press Release. Available online: https:\/\/www.sumibe.co.jp\/topics\/2024\/bmi\/0411_01\/index.html."},{"key":"ref_12","unstructured":"(2023, March 20). DuraQ\u00ae. Available online: https:\/\/www.sumibe.co.jp\/solution\/mobility\/AD_ADAS\/duraq.html."},{"key":"ref_13","first-page":"3","article-title":"The ten-twenty electrode system of the International Federation","volume":"52","author":"Klem","year":"1999","journal-title":"Electroencephalogr. Clin. Neurophysiol. Suppl."},{"key":"ref_14","first-page":"71","article-title":"The Proposal of the Model Task for Efficiency Evaluation of the Construction Work by Remote Control of a Hydraulic Excavator","volume":"66","author":"Moteki","year":"2014","journal-title":"Constr. Mach. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kojima, T., Ohsuga, M., Kamakura, Y., Hori, J., and Watanabe, S. (2022, January 9\u201312). Assessment of car sickness in passengers using physiological indices. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Prague, Czech Republic.","DOI":"10.1109\/SMC53654.2022.9945432"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1177\/0018720811403736","article-title":"Validating an Efficient Method to Quantify Motion Sickness","volume":"53","author":"Keshavarz","year":"2011","journal-title":"Hum. Factors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1207\/s15327108ijap0303_3","article-title":"Simulator Sickness Questionnaire: An Enhanced Method for Quantifying Simulator Sickness","volume":"3","author":"Kennedy","year":"1993","journal-title":"Int. J. Aviat. Psychol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"142","DOI":"10.2486\/indhealth.43.142","article-title":"Actual Conditions of Work, Fatigue and Sleep in Non-Employed, Home-Based Female Information Technology Workers with Preschool Children","volume":"43","author":"Sasaki","year":"2005","journal-title":"Ind. Health"},{"key":"ref_19","first-page":"81","article-title":"Towards Evaluating Visually Induced Motion Sickness Using Facial Expressions -Investigation in a Simulated Remote Control Environment Using Hydraulic Excavator Simulator","volume":"23","author":"Takeuchi","year":"2023","journal-title":"Jpn. Acad. Facial Stud."},{"key":"ref_20","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T. (2017, January 4\u20139). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_21","unstructured":"(2020, October 25). Lessons Learned From Bench-Marking Fast Machine Learning Algorithms. Available online: https:\/\/docs.microsoft.com\/ja-jp\/archive\/blogs\/machinelearning\/lessons-learned-benchmarking-fast-machine-learning-algorithms."},{"key":"ref_22","unstructured":"Shi, H. (2007). Best-First Decision Tree Learning. [Master\u2019s Thesis, University of Waikato]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1007\/s11517-006-0119-0","article-title":"Heart rate variability: A review","volume":"44","author":"Acharya","year":"2006","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_24","first-page":"121","article-title":"On a QRS Detection Algorithm Applicable to the Evaluation of Mental State in Normal Subjects","volume":"30","author":"Ohsuga","year":"1992","journal-title":"Jpn. J. Med. Electron. Biol. Eng."},{"key":"ref_25","first-page":"33","article-title":"Program and its application to respiratory waveform analysis -Case study on motion sickness","volume":"43","author":"Nakagawa","year":"2007","journal-title":"Jpn. J. Ergon."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1037\/0096-1523.15.3.529","article-title":"Speed and accuracy of saccadic eye movements: Characteristics of impulse variability in the oculomotor system","volume":"15","author":"Abrams","year":"1989","journal-title":"J. Exp. Psychol. Hum. Percept. Perform."},{"key":"ref_27","first-page":"198","article-title":"An experimental consideration on the definition of a fixation point","volume":"32","author":"Fukuda","year":"1996","journal-title":"J. Jpn. Hum. Factors Ergon. Soc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yoshioka, N., Kimura, T., Shu, Y., Okamatsu, T., Araki, N., and Ohsuga, M. (2020, January 20\u201324). Evaluation of the tiller switch layout of a tractor using eye-fixation related potentials. Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176280"},{"key":"ref_29","unstructured":"(2020, December 08). A Hyperparameter Optimization Framework. Available online: https:\/\/optuna.readthedocs.io\/en\/stable\/index.html."},{"key":"ref_30","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., and K\u2019egl, B. (2011, January 12\u201317). Algorithms for hyper-parameter optimization. Proceedings of the 24th International Conference on Neural Information Processing Systems, Granada, Spain."},{"key":"ref_31","unstructured":"Bergstra, J., Yamins, D., and Cox, D. (2013, January 17\u201319). Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"1997","journal-title":"Biometrics"},{"key":"ref_33","first-page":"862","article-title":"The Quantization of Cybersickness Level Using EEG and ECG for Virtual Reality Head-Mounted Display","volume":"49","author":"Lin","year":"2018","journal-title":"Hum. Factors AR\/VR Syst."},{"key":"ref_34","first-page":"131","article-title":"Basic study toward VE-sickness assessment","volume":"36","author":"Nakagawa","year":"2000","journal-title":"Jpn. J. Ergon."},{"key":"ref_35","first-page":"229","article-title":"Analysis of Autonomic Nervous Reactions Accompanying Nausea Induced by Visual Stimulation","volume":"28","author":"Himi","year":"2002","journal-title":"Kawasaki Med. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.displa.2016.07.002","article-title":"Use of physiological signals to predict cybersickness","volume":"44","author":"Mark","year":"2016","journal-title":"Display"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1113\/EP087385","article-title":"Changes in skin blood flow, respiration and blood pressure in participants reporting motion sickness during sinusoidal galvanic vestibular stimulation","volume":"104","author":"Javaid","year":"2019","journal-title":"Exp. Physiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1111\/j.1469-8986.2005.00349.x","article-title":"Characteristic changes in the physiological components of cybersickness","volume":"42","author":"Young","year":"2005","journal-title":"Psychophysiology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.autneu.2009.06.007","article-title":"Effect of temporal relationship between respiration and body motion on motion sickness","volume":"151","author":"Denise","year":"2009","journal-title":"Auton. Neurosci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Adhanom, I., Halow, S., Folmer, E., and MacNeilage, P. (2022). VR Sickness Adaptation With Ramped Optic Flow Transfers From Abstract To Realistic Environments. Front. Virtual Real., 3.","DOI":"10.3389\/frvir.2022.848001"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Magaki, T., and Vallance, M. (2019, January 23\u201327). Developing an Accessible Evaluation Method of VR Cybersickness. Proceedings of the 26th Annual IEEE Conference on Virtual Reality and 3D User Interfaces, Osaka, Japan.","DOI":"10.1109\/VR.2019.8797748"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s10055-019-00386-w","article-title":"Physical discomfort and eye movements during arbitrary and optical flow-like motions in stereo 3D contents","volume":"24","author":"Wibirama","year":"2020","journal-title":"Virtual Real."},{"key":"ref_43","first-page":"728","article-title":"Predicting cybersickness based on user\u2019s gaze behaviors in HMD-based virtual reality","volume":"8","author":"Chang","year":"2021","journal-title":"J. Comput. Des. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, J.X., Guo, C.T., So, R.H.Y., and Cheung, R.T.F. (2011, January 19\u201323). Effects of Eye Fixation on Visually Induced Motion Sickness: Are they Caused by Changes in Retinal Slip Velocity?. Proceedings of the Annual Meeting of the Human Factors and Ergonomics Society, Las Vegas, NV, USA.","DOI":"10.1177\/1071181311551254"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"135589","DOI":"10.1016\/j.neulet.2020.135589","article-title":"Test-retest reliability of the virtual reality sickness evaluation using electroencephalography (EEG)","volume":"743","author":"Lim","year":"2021","journal-title":"Neurosci. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"020501-1","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020501","article-title":"A Pilot Study on EEG-Based Evaluation of Visually Induced Motion Sickness","volume":"64","author":"Liu","year":"2020","journal-title":"J. Imaging Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ahn, M.H., Park, J.H., Jeon, H., Lee, H.J., Kim, H.J., and Hong, S.K. (2020). Temporal Dynamics of Visually Induced Motion Perception and Neural Evidence of Alterations in the Motion Perception Process in an Immersive Virtual Reality Environment. Front. Neurosci., 14.","DOI":"10.3389\/fnins.2020.600839"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102981","DOI":"10.1016\/j.ergon.2020.102981","article-title":"Inhibition-related N2 and P3: Indicators of visually induced motion sickness (VIMS)","volume":"78","author":"Wu","year":"2020","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Adler, A.I., and Painsky, A. (2022). Feature importance in gradient boosting trees with cross-validation feature selection. Entropy, 24.","DOI":"10.3390\/e24050687"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6465\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:12:01Z","timestamp":1760112721000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6465"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,7]]},"references-count":49,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196465"],"URL":"https:\/\/doi.org\/10.3390\/s24196465","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,10,7]]}}}