{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:23:05Z","timestamp":1760059385091,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["RS-2023-00219725"],"award-info":[{"award-number":["RS-2023-00219725"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>This study proposes an effective method for identifying human movement direction in indoor environments by leveraging a low-resolution time-of-flight (ToF) sensor and a long short-term memory (LSTM) neural network model. While previous studies have employed camera-based or high-resolution ToF-based sensors, we utilize an 8 \u00d7 8 array ToF sensor, which is neither expensive nor related to any privacy issues. Furthermore, in contrast to the conventional rule-based algorithm, the proposed method employs the LSTM model to effectively handle the sequential time-series data. Experimental evaluations, including both basic single-person scenarios and complex multi-user challenge scenarios, confirm that the proposed LSTM-based approach achieves outstanding accuracy of 98% in identifying human entry and exit movements.<\/jats:p>","DOI":"10.3390\/jsan14030061","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T06:11:49Z","timestamp":1749622309000},"page":"61","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Movement Direction Classification Using Low-Resolution ToF Sensor and LSTM-Based Neural Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Sejik","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Robot AI Convergence, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyoung Min","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9071-4837","authenticated-orcid":false,"given":"Seok Young","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Information Technology, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nam Kyu","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8310","DOI":"10.1109\/ACCESS.2025.3526858","article-title":"Technical Analysis of Comfort and Energy Consumption in Smart Buildings with Three Levels of Automation: Scheduling, Smart Sensors, and IoT","volume":"13","author":"Aazami","year":"2025","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Poyyamozhi, M., Murugesan, B., Rajamanickam, N., Shorfuzzaman, M., and Aboelmagd, Y. (2024). IoT\u2014A Promising Solution to Energy Management in Smart Buildings: A Systematic Review, Applications, Barriers, and Future Scope. Buildings, 14.","DOI":"10.3390\/buildings14113446"},{"key":"ref_3","first-page":"117","article-title":"A Study on Energy Saving and Safety Improvement through IoT Sensor Monitoring in Smart Factory","volume":"20","author":"Choi","year":"2024","journal-title":"J. Soc. Disaster Inf."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Singh, S., Aggarwal, N., and Dabas, D. (2024). Empowering homes through energy efficiency: A comprehensive review of smart home systems and devices. Int. J. Energy Sect. Manag., ahead-of-print.","DOI":"10.1108\/IJESM-07-2024-0044"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1080\/19401493.2024.2399053","article-title":"Indoor occupancy monitoring using environmental feature fusion and semi-supervised machine learning models","volume":"17","author":"Ceballos","year":"2024","journal-title":"J. Build. Perform. Simul."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tsang, T.-W., Mui, K.-W., Wong, L.-T., Chan, A.C.-Y., and Chan, R.C.-W. (2024). Real-Time Indoor Environmental Quality (IEQ) Monitoring Using an IoT-Based Wireless Sensing Network. Sensors, 24.","DOI":"10.3390\/s24216850"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhong, C., Sun, J., Xie, J., Grijalva, S., and Meliopoulos, A.S. (2018, January 12\u201314). Real-time human activity-based energy management system using model predictive control. Proceedings of the 2018 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2018.8326070"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"40878","DOI":"10.1109\/ACCESS.2023.3269843","article-title":"Human sensing by using radio frequency signals: A survey on occupancy and activity detection","volume":"11","author":"Shahbazian","year":"2023","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Allik, A., Muiste, S., and Pihlap, H. (2019, January 9\u201311). Movement Based Energy Management Models for Smart Buildings. Proceedings of the 2019 7th International Conference on Smart Grid (icSmartGrid), Newcastle, Australia.","DOI":"10.1109\/icSmartGrid48354.2019.8990844"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Saputro, A.H., and Imawan, C. (2016, January 15\u201316). Local and global human activity detection for room energy saving model. Proceedings of the 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Malang, Indonesia.","DOI":"10.1109\/ICACSIS.2016.7872735"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"114284","DOI":"10.1016\/j.rser.2024.114284","article-title":"A systematic review and comprehensive analysis of building occupancy prediction","volume":"193","author":"Li","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_12","unstructured":"Sekiguchi, T., and Kato, H. (February, January 31). Privacy Assuring Video-Based Monitoring System Considering Browsing Purposes. Proceedings of the 2005 Symposium on Applications and the Internet Workshops (SAINT 2005 Workshops), Trento, Italy."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1145\/3583135","article-title":"Presentation-level privacy protection techniques for automated face recognition\u2014A survey","volume":"55","author":"Hasan","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Klauser, D., B\u00e4rwolff, G., and Schwandt, H. (2015). A TOF-based automatic passenger counting approach in public transportation systems. AIP Conference Proceedings, American Institute of Physics.","DOI":"10.1063\/1.4913168"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.enbuild.2015.01.043","article-title":"People occupancy detection and profiling with 3D depth sensors for building energy management","volume":"92","author":"Diraco","year":"2015","journal-title":"Energy Build."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.1109\/TPAMI.2021.3075156","article-title":"Exploiting wavelength diversity for high resolution time-of-flight 3D imaging","volume":"43","author":"Li","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111390","DOI":"10.1016\/j.enbuild.2021.111390","article-title":"A zone-level occupancy counting system for commercial office spaces using low-resolution time-of-flight sensors","volume":"252","author":"Lu","year":"2021","journal-title":"Energy Build."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zappi, P., Farella, E., and Benini, L. (2007, January 5\u20137). Enhancing the spatial resolution of presence detection in a PIR based wireless surveillance network. Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, London, UK.","DOI":"10.1109\/AVSS.2007.4425326"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wahl, F., Milenkovic, M., and Amft, O. (2012, January 5\u20137). A distributed PIR-based approach for estimating people count in office environments. Proceedings of the 2012 IEEE 15th International Conference on Computational Science and Engineering, Paphos, Cyprus.","DOI":"10.1109\/ICCSE.2012.92"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109965","DOI":"10.1016\/j.enbuild.2020.109965","article-title":"A review of building occupancy measurement systems","volume":"216","author":"Sun","year":"2020","journal-title":"Energy Build."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s11554-016-0584-0","article-title":"Real-time identification of pedestrian meeting and split events from surveillance videos using motion similarity and its applications","volume":"16","author":"Chandran","year":"2019","journal-title":"J. Real-Time Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110386","DOI":"10.1016\/j.enbuild.2020.110386","article-title":"A vision-based deep learning approach for the detection and prediction of occupancy heat emissions for demand-driven control solutions","volume":"226","author":"Tien","year":"2020","journal-title":"Energy Build."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111355","DOI":"10.1016\/j.jobe.2024.111355","article-title":"Deep learning models for vision-based occupancy detection in high occupancy buildings","volume":"98","author":"Zhang","year":"2024","journal-title":"J. Build. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TKDE.2005.32","article-title":"Preserving privacy by de-identifying face images","volume":"17","author":"Newton","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dai, J., Wu, J., Saghafi, B., Konrad, J., and Ishwar, P. (2015, January 7\u201312). Towards privacy-preserving activity recognition using extremely low temporal and spatial resolution cameras. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301356"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ahmad, S., Morerio, P., and Del Bue, A. (2023, January 1\u20136). Person re-identification without identification via event anonymization. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01022"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/JIOT.2019.2953713","article-title":"Door-monitor: Counting in-and-out visitors with COTS WiFi devices","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cokbas, M., Ishwar, P., and Konrad, J. (2020, January 14\u201319). Low-resolution overhead thermal tripwire for occupancy estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00052"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109811","DOI":"10.1016\/j.buildenv.2022.109811","article-title":"Smart detection of indoor occupant thermal state via infrared thermography, computer vision, and machine learning","volume":"228","author":"He","year":"2023","journal-title":"Build. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/3\/61\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:49:57Z","timestamp":1760032197000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/3\/61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,11]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["jsan14030061"],"URL":"https:\/\/doi.org\/10.3390\/jsan14030061","relation":{},"ISSN":["2224-2708"],"issn-type":[{"type":"electronic","value":"2224-2708"}],"subject":[],"published":{"date-parts":[[2025,6,11]]}}}