{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:48:50Z","timestamp":1760147330282,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T00:00:00Z","timestamp":1674777600000},"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 (EPSRC) Centre for Doctoral Training in Embedded Intelligence","doi-asserted-by":"publisher","award":["EP\/L014998\/1"],"award-info":[{"award-number":["EP\/L014998\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A little explored area of human activity recognition (HAR) is in people operating in relation to extreme environments, e.g., mountaineers. In these contexts, the ability to accurately identify activities, alongside other data streams, has the potential to prevent death and serious negative health events to the operators. This study aimed to address this user group and investigate factors associated with the placement, number, and combination of accelerometer sensors. Eight participants (age = 25.0 \u00b1 7 years) wore 17 accelerometers simultaneously during lab-based simulated mountaineering activities, under a range of equipment and loading conditions. Initially, a selection of machine learning techniques was tested. Secondly, a comprehensive analysis of all possible combinations of the 17 accelerometers was performed to identify the optimum number of sensors, and their respective body locations. Finally, the impact of activity-specific equipment on the classifier accuracy was explored. The results demonstrated that the support vector machine (SVM) provided the most accurate classifications of the five machine learning algorithms tested. It was found that two sensors provided the optimum balance between complexity, performance, and user compliance. Sensors located on the hip and right tibia produced the most accurate classification of the simulated activities (96.29%). A significant effect associated with the use of mountaineering boots and a 12 kg rucksack was established.<\/jats:p>","DOI":"10.3390\/s23031416","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T02:01:18Z","timestamp":1675044078000},"page":"1416","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2954-4071","authenticated-orcid":false,"given":"Stephen","family":"Ward","sequence":"first","affiliation":[{"name":"Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9637-4730","authenticated-orcid":false,"given":"Sijung","family":"Hu","sequence":"additional","affiliation":[{"name":"Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4741-4334","authenticated-orcid":false,"given":"Massimiliano","family":"Zecca","sequence":"additional","affiliation":[{"name":"Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1073\/pnas.2102463118","article-title":"Global and country-level estimates of human population at high altitude","volume":"118","author":"Tremblay","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/jtm\/taz103","article-title":"Helicopter evacuations in the Nepalese Himalayas (2016\u20132017)","volume":"27","author":"Dawadi","year":"2020","journal-title":"J. Travel Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cheng, F.Y., Jeng, M.J., Lin, Y.C., Wang, S.H., Wu, S.H., Li, W.C., Huang, K.F., and Chiu, T.F. (2017). Incidence and severity of acute mountain sickness and associated symptoms in children trekking on Xue Mountain, Taiwan. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183207"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1111\/joim.12908","article-title":"Measurement of physical activity in clinical practice using accelerometers","volume":"286","author":"Arvidsson","year":"2019","journal-title":"J. Intern. Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, J.Y., Kwon, S., Kim, W.S., Hahn, S.J., Park, J., and Paik, N.J. (2018). Feasibility, reliability, and validity of using accelerometers to measure physical activities of patients with stroke during inpatient rehabilitation. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0209607"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1123\/jpah.2019-0088","article-title":"Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review","volume":"17","author":"Narayanan","year":"2020","journal-title":"J. Phys. Act. Health"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhao, S., Li, W., and Cao, J. (2018). A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution. Sensors, 18.","DOI":"10.3390\/s18061850"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"135","DOI":"10.7600\/jpfsm.6.135","article-title":"Assessing sedentary behavior using wearable devices: An overview and future directions","volume":"6","author":"Sasai","year":"2017","journal-title":"J. Phys. Fit. Sport. Med."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Camomilla, V., Bergamini, E., Fantozzi, S., and Vannozzi, G. (2018). Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review. Sensors, 18.","DOI":"10.3390\/s18030873"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., and Craddock, I. (2018). A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics, 5.","DOI":"10.20944\/preprints201803.0147.v1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Arshad, M.H., Bilal, M., and Gani, A. (2022). Human Activity Recognition: Review, Taxonomy and Open Challenges. Sensors, 22.","DOI":"10.3390\/s22176463"},{"key":"ref_12","first-page":"332","article-title":"How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies","volume":"54","author":"Turicchi","year":"2018","journal-title":"Br. J. Sport. Med."},{"key":"ref_13","first-page":"171","article-title":"Fall identification in rock climbing using wearable device","volume":"230","author":"Tonoli","year":"2016","journal-title":"Proc. Inst. Mech. Eng. Part P J. Sport. Eng. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cero Dinarevi\u0107, E., Barakovi\u0107 Husi\u0107, J., and Barakovi\u0107, S. (2019). Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications. Sensors, 19.","DOI":"10.3390\/s19235206"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rosati, S., Balestra, G., and Knaflitz, M. (2018). Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors. Sensors, 18.","DOI":"10.3390\/s18124189"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sousa Lima, W., Souto, E., El-Khatib, K., Jalali, R., and Gama, J. (2019). Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview. Sensors, 19.","DOI":"10.3390\/s19143213"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6611","DOI":"10.1109\/ACCESS.2018.2890004","article-title":"A Wearable Activity Recognition Device Using Air-Pressure and IMU Sensors","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9183","DOI":"10.3390\/s130709183","article-title":"Optimal placement of accelerometers for the detection of everyday activities","volume":"13","author":"Cleland","year":"2013","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Bosch, S., Incel, O., Scholten, H., and Havinga, P. (2016). Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors. Sensors, 16.","DOI":"10.3390\/s16040426"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tian, Y., and Zhang, J. (2020). Optimizing Sensor Deployment for Multi-Sensor-Based HAR System with Improved Glowworm Swarm Optimization Algorithm. Sensors, 20.","DOI":"10.3390\/s20247161"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.pmcj.2018.08.003","article-title":"Physiological state in extreme environments","volume":"50","author":"Divis","year":"2018","journal-title":"Pervasive Mob. Comput."},{"key":"ref_22","unstructured":"Lee, S.H., Ni, J.C., Zhao, Y.G., and Yang, C.S. (2017, January 8\u201310). A real-time emergency rescue assistance system for mountaineers. Proceedings of the 2017 IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100775","DOI":"10.1016\/j.imu.2021.100775","article-title":"Healthcare monitoring of mountaineers by low power Wireless Sensor Networks","volume":"27","author":"Garg","year":"2021","journal-title":"Inform. Med. Unlocked"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Galli, A., Narduzzi, C., Peruzzi, G., and Pozzebon, A. (2022, January 6\u20138). Satellite IoT for Monitoring and Tracking of Athletes in Extreme Environments. Proceedings of the 2022 IEEE International Workshop on Sport, Technology and Research (STAR), Trento-Cavalese, Italy.","DOI":"10.1109\/STAR53492.2022.9859740"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.procs.2014.07.009","article-title":"A Study on Human Activity Recognition Using Accelerometer Data from Smartphones","volume":"34","author":"Bayat","year":"2014","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ka\u0144toch, E. (2018). Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk. Sensors, 18.","DOI":"10.3390\/s18103219"},{"key":"ref_27","first-page":"1","article-title":"Human Activity Recognition Using a Single Wrist IMU Sensor via Deep Learning Convolutional and Recurrent Neural Nets Index Terms\u2014Human Activities, Inertial Measurement Units (IMUs), Convolutional Neural Nets (CNN), Recurrent Neural Nets (RNN), HAR System","volume":"1","author":"Valarezo","year":"2017","journal-title":"J. Ict Des. Eng. Technol. Sci. JITDETS"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1109\/TMC.2017.2761744","article-title":"HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices","volume":"17","author":"Khalifa","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fridriksdottir, E., and Bonomi, A.G. (2020). Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network. Sensors, 20.","DOI":"10.3390\/s20226424"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.inffus.2018.06.002","article-title":"Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions","volume":"46","author":"Nweke","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/978-3-319-53480-0_52","article-title":"A New Approach to Human Activity Recognition Using Machine Learning Techniques","volume":"Volume 557","author":"Marinho","year":"2017","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"ref_32","first-page":"1","article-title":"Position-Based Feature Selection for Body Sensors regarding Daily Living Activity Recognition","volume":"2018","author":"Nguyen","year":"2018","journal-title":"J. Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"18347","DOI":"10.1038\/s41598-022-22881-y","article-title":"Task demand and load carriage experience affect gait variability among military cadets","volume":"12","author":"Ulman","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1080\/00140139.2019.1690710","article-title":"Effect of a load distribution system on mobility and performance during simulated and field hiking while under load","volume":"63","author":"Sessoms","year":"2020","journal-title":"Ergonomics"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1080\/00140139.2016.1206624","article-title":"Influence of a 12.8-km military load carriage activity on lower limb gait mechanics and muscle activity","volume":"60","author":"Rice","year":"2017","journal-title":"Ergonomics"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1416\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:17:07Z","timestamp":1760120227000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1416"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,27]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031416"],"URL":"https:\/\/doi.org\/10.3390\/s23031416","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,1,27]]}}}