{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:06:01Z","timestamp":1789103161848,"version":"build-2803163510"},"reference-count":26,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"projects GERIO from FEDER, UE","award":["AEI\/10.13039\/501100011033\/"],"award-info":[{"award-number":["AEI\/10.13039\/501100011033\/"]}]},{"DOI":"10.13039\/501100002809","name":"Generalitat de Catalunya","doi-asserted-by":"publisher","award":["2021 SGR 0112"],"award-info":[{"award-number":["2021 SGR 0112"]}],"id":[{"id":"10.13039\/501100002809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["2021 SGR 0112"],"award-info":[{"award-number":["2021 SGR 0112"]}],"id":[{"id":"https:\/\/ror.org\/01bg62x04","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008722","name":"University of Girona","doi-asserted-by":"publisher","award":["IFUdG2025\/14"],"award-info":[{"award-number":["IFUdG2025\/14"]}],"id":[{"id":"10.13039\/100008722","id-type":"DOI","asserted-by":"publisher"}]},{"award":["IFUdG2025\/14"],"award-info":[{"award-number":["IFUdG2025\/14"]}],"id":[{"id":"https:\/\/ror.org\/01xdxns91","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Spanish Government\u2019s Ministerio de Ciencia e Innovaci\u00f3n","award":["ID2023-1477870B-100"],"award-info":[{"award-number":["ID2023-1477870B-100"]}]},{"award":["ID2023-1477870B-100"],"award-info":[{"award-number":["ID2023-1477870B-100"]}],"id":[{"id":"https:\/\/ror.org\/05r0vyz12","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Artificial intelligence can extend the functional capabilities of embedded sensors by extracting application-level information from physical measurements. This study investigates whether footstep-induced floor vibrations acquired with a single triaxial MEMS accelerometer contain sufficient information to characterize pedestrian path orientation and travel sense. A custom sensing platform based on an ADXL355 accelerometer and an ESP32 microcontroller was developed to acquire the structural vibration response at 4 kSPS. Lightweight temporal features were processed using a Random Forest classifier. The primary assessment used leakage-aware event-level cross-validation, with complete footsteps as the data-partitioning units. Under this more conservative protocol, discrimination of the complete A\u2013K movement-label set was poor, whereas a compact 12-dimensional descriptor representation achieved 73.18% accuracy, 71.52% balanced accuracy, and 70.98% macro-F1 for X\/Y path-orientation classification. Reliable positive\/negative travel-sense discrimination could not be demonstrated from isolated footsteps. For historical comparison, the original sample-level procedure yielded 97.09% accuracy, but this value is retained only as a within-sequence reference because densely sampled observations contain strongly overlapping information. The findings provide proof-of-concept evidence that a single floor-mounted MEMS accelerometer can capture coarse pedestrian path-orientation information without cameras or spatially distributed vibration-sensor networks. Feature extraction and classifier inference were performed offline; broader validation across participants, sessions, floor structures, and realistic disturbances is required before deployment as an embedded edge AI sensing node.<\/jats:p>","DOI":"10.3390\/s26185736","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T15:01:56Z","timestamp":1788966116000},"page":"5736","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Enhanced Directional Pedestrian Sensing Using a Single MEMS Accelerometer"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2098-5838","authenticated-orcid":false,"given":"Enric","family":"Casademont","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Automatic Control Engineering, University of Girona, 17003 Girona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7518-0207","authenticated-orcid":false,"given":"Narc\u00eds","family":"Planellas","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Automatic Control Engineering, University of Girona, 17003 Girona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9044-6994","authenticated-orcid":false,"given":"Carles","family":"Pous","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Automatic Control Engineering, University of Girona, 17003 Girona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5677-7594","authenticated-orcid":false,"given":"Lloren\u00e7","family":"Burgas","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Automatic Control Engineering, University of Girona, 17003 Girona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3609-0688","authenticated-orcid":false,"given":"Joaquim","family":"Massana","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Automatic Control Engineering, University of Girona, 17003 Girona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6582-4551","authenticated-orcid":false,"given":"Pere","family":"Marti-Puig","sequence":"additional","affiliation":[{"name":"Data and Signal Processing Group, University of Vic\u2014Central University of Catalonia, 08500 Vic, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","unstructured":"Breque, M., De Nul, L., and Petridis, A. (2021). Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry, European Commission, Directorate-General for Research and Innovation. Technical Report."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Niemann, F., L\u00fcdtke, S., Bartelt, C., and ten Hompel, M. (2022). Context-Aware Human Activity Recognition in Industrial Processes. Sensors, 22.","DOI":"10.3390\/s22010134"},{"key":"ref_3","first-page":"19","article-title":"A review of occupancy sensing technologies and approaches in smart buildings","volume":"15","author":"Elkhoukhi","year":"2025","journal-title":"Int. J. RF Technol. Res. Appl."},{"key":"ref_4","first-page":"1014","article-title":"The Role of Smart Buildings and Automated Energy Management Systems in Enhancing Building Energy Efficiency","volume":"9","author":"Daissaoui","year":"2020","journal-title":"Int. J. Adv. Res. Electr. Electron. Instrum. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"El Husseini, F., Noura, H.N., Salman, O., and Chahine, K. (2025). Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends. Appl. Sci., 15.","DOI":"10.3390\/app15147682"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Carmenate, T.S., Leante, D., Zanlongo, S.A., Bobadilla, L., and Mostafavi, A. (2015). A Non-Invasive Sensing System for Decoding Occupancy Behaviors Affecting Building Energy Performance. Computing in Civil Engineering 2015, American Society of Civil Engineers.","DOI":"10.1061\/9780784479247.033"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"101289","DOI":"10.1016\/j.aei.2021.101289","article-title":"Using footstep-induced vibrations for occupant detection and recognition in buildings","volume":"49","author":"Drira","year":"2021","journal-title":"Adv. Eng. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1109\/JSYST.2019.2937960","article-title":"Smart Home Monitoring System via Footstep-Induced Vibrations","volume":"14","author":"Li","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yu, Y., Qin, X., Hussain, S., Hou, W., and Weis, T. (2022). Pedestrian Counting Based on Piezoelectric Vibration Sensor. Appl. Sci., 12.","DOI":"10.3390\/app12041920"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1117\/12.441277","article-title":"Footstep detection and tracking","volume":"Volume 4393","author":"Carapezza","year":"2001","journal-title":"Proceedings of the Unattended Ground Sensor Technologies and Applications III"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.ymssp.2016.11.023","article-title":"Indoor Footstep Localization from Structural Dynamics Instrumentation","volume":"88","author":"Poston","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.ymssp.2018.04.026","article-title":"Occupant Localization Using Footstep-Induced Structural Vibration","volume":"112","author":"Mirshekari","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1038\/s41597-025-05517-4","article-title":"A Structural Vibration-based Dataset for Human Gait Recognition","volume":"12","author":"Chakraborty","year":"2025","journal-title":"Sci. Data"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pozo, F., Tibaduiza, D.A., and Vidal, Y. (2021). Sensors for Structural Health Monitoring and Condition Monitoring. Sensors, 21.","DOI":"10.3390\/s21051558"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"117198","DOI":"10.1016\/j.engstruct.2023.117198","article-title":"Human-induced vibrations of floors: A probabilistic approach","volume":"300","author":"Bayat","year":"2024","journal-title":"Eng. Struct."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.3813\/AAA.918913","article-title":"Evaluation of Floor Vibrations Induced by Walking Barefoot in Heavyweight Buildings","volume":"101","author":"Lee","year":"2015","journal-title":"Acta Acust. United Acust."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1145\/3130954","article-title":"FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing","volume":"1","author":"Pan","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dong, Y., and Noh, H.Y. (2024). Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations. Sensors, 24.","DOI":"10.20944\/preprints202403.0617.v1"},{"key":"ref_19","first-page":"980306","article-title":"Occupant traffic estimation through structural vibration sensing","volume":"Volume 9803","author":"Lynch","year":"2016","journal-title":"Proceedings of the Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"10774","DOI":"10.1038\/s41598-024-60034-5","article-title":"Ambient Floor Vibration Sensing Advances the Accessibility of Functional Gait Assessments for Children with Muscular Dystrophies","volume":"14","author":"Dong","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"04025131","DOI":"10.1061\/JCCEE5.CPENG-6869","article-title":"Automated and Scalable Footstep Vibration-Based Pedestrian Localization in Built Environments Using Deep Learning","volume":"40","author":"Appelle","year":"2026","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dong, Y., Zhu, J., and Noh, H.Y. (2022, January 9\u201310). Re-vibe: Vibration-based indoor person re-identification through cross-structure optimal transport. Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys \u201922), Boston, MA, USA.","DOI":"10.1145\/3563357.3566134"},{"key":"ref_24","unstructured":"Analog Devices (2025, November 06). ADXL355 Low Noise, Low Drift, Low Power, 3-Axis MEMS Accelerometer Datasheet. Available online: https:\/\/www.analog.com\/media\/en\/technical-documentation\/data-sheets\/adxl354_adxl355.pdf."},{"key":"ref_25","unstructured":"Espressif Systems (2025). ESP32-WROOM-32D & ESP32-WROOM-32U Datasheet, Espressif Systems. Not Recommended For New Designs (NRND)."},{"key":"ref_26","unstructured":"Casademont Ferr\u00e9s, E. (2026). Single Triaxial MEMS Walking Dataset, Zenodo. Version v1."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5736\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T04:16:27Z","timestamp":1789100187000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":26,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185736"],"URL":"https:\/\/doi.org\/10.3390\/s26185736","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]}}}