{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:24:50Z","timestamp":1775147090677,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Grants-in-Aid for Scientific Research (KAKENHI)","award":["YYH3Y07"],"award-info":[{"award-number":["YYH3Y07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In health monitoring systems for the elderly, a crucial aspect is unobtrusively and continuously monitoring their activities to detect potentially hazardous incidents such as sudden falls as soon as they occur. However, the effectiveness of current non-contact sensor-based activity detection systems is limited by obstacles present in the environment. To overcome this limitation, a straightforward yet highly efficient approach involves utilizing multiple sensors that collaborate seamlessly. This paper proposes a method that leverages 2D Light Detection and Ranging (Lidar) technology for activity detection. Multiple 2D Lidars are positioned in an indoor environment with varying obstacles such as furniture, working cohesively to create a comprehensive representation of ongoing activities. The data from these Lidars is concatenated and transformed into a more interpretable format, resembling images. A convolutional Long Short-Term Memory (LSTM) Neural Network is then used to process these generated images to classify the activities. The proposed approach achieves high accuracy in three tasks: activity detection, fall detection, and unsteady gait detection. Specifically, it attains accuracies of 96.10%, 99.13%, and 93.13% for these tasks, respectively. This demonstrates the efficacy and promise of the method in effectively monitoring and identifying potentially hazardous events for the elderly through 2D Lidars, which are non-intrusive sensing technology.<\/jats:p>","DOI":"10.3390\/s24020626","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T11:28:46Z","timestamp":1705577326000},"page":"626","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Activity Detection in Indoor Environments Using Multiple 2D Lidars"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7055-9318","authenticated-orcid":false,"given":"Mondher","family":"Bouazizi","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8704-1248","authenticated-orcid":false,"given":"Alejandro Lorite","family":"Mora","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan"}]},{"given":"Kevin","family":"Feghoul","sequence":"additional","affiliation":[{"name":"UMR-S1172\u2014Lille Neuroscience and Cognition, Centre Hospitalier Universitaire Lille, Inserm, University of Lille, F-59000 Lille, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3961-1426","authenticated-orcid":false,"given":"Tomoaki","family":"Ohtsuki","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,18]]},"reference":[{"key":"ref_1","unstructured":"Andersen, M., Bhaumik, S., Brown, J., Elkington, J., Ivers, R., Keay, L., Lim, M.L., Lukaszyk, C., Ma, T., and Meddings, D. (2021). Step Safely: Strategies for Preventing and Managing Falls Across the Life-Course."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.procir.2022.09.071","article-title":"A Multi-camera System for Human Detection and Activity Recognition","volume":"112","author":"Berger","year":"2022","journal-title":"Procedia CIRP"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Stork, J.A., Spinello, L., Silva, J., and Arras, K.O. (2012, January 9\u201313). Audio-based Human Activity Recognition Using Non-Markovian Ensemble Voting. Proceedings of the 21st IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Paris, France.","DOI":"10.1109\/ROMAN.2012.6343802"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhuang, Z., and Xue, Y. (2019). Sport-related human activity detection and recognition using a smartwatch. Sensors, 19.","DOI":"10.3390\/s19225001"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1038\/s41528-020-00092-7","article-title":"Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications","volume":"4","author":"Zhang","year":"2020","journal-title":"NPJ Flex. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"110870","DOI":"10.1016\/j.measurement.2022.110870","article-title":"Fall detection system based on infrared array sensor and multi-dimensional feature fusion","volume":"192","author":"Yang","year":"2022","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2147\/CIA.S329668","article-title":"Human fall detection using passive infrared sensors with low resolution: A systematic review","volume":"17","author":"Michel","year":"2022","journal-title":"Clin. Interv. Aging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ogawa, Y., and Naito, K. (2020, January 4\u20136). Fall detection scheme based on temperature distribution with IR array sensor. Proceedings of the 2020 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE46568.2020.9043000"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bouazizi, M., Ye, C., and Ohtsuki, T. (2022). Low-Resolution Infrared Array Sensor for Counting and Localizing People Indoors: When Low End Technology Meets Cutting Edge Deep Learning Techniques. Information, 13.","DOI":"10.3390\/info13030132"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1007\/s42979-022-01111-2","article-title":"A Novel Approach in WiFi CSI-Based Fall Detection","volume":"3","author":"Mattela","year":"2022","journal-title":"SN Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4471","DOI":"10.1109\/TMC.2022.3157666","article-title":"AFall: Wi-Fi-based device-free fall detection system using spatial angle of arrival","volume":"22","author":"Chen","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8515","DOI":"10.1109\/JIOT.2021.3116136","article-title":"DeFall: Environment-independent passive fall detection using WiFi","volume":"9","author":"Hu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1175\/BAMS-D-11-00057.1","article-title":"Wind energy meteorology: Insight into wind properties in the turbine-rotor layer of the atmosphere from high-resolution Doppler Lidar","volume":"94","author":"Banta","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4224","DOI":"10.1109\/TII.2018.2822828","article-title":"Object classification using CNN-based fusion of vision and Lidar in autonomous vehicle environment","volume":"14","author":"Gao","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1017\/S0263574719001024","article-title":"Detection and tracking of moving obstacles (DATMO): A review","volume":"38","author":"Llamazares","year":"2020","journal-title":"Robotica"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6191","DOI":"10.1109\/JSEN.2020.2975129","article-title":"Human gait tracking for normal people and walker users using a 2D Lidar","volume":"20","author":"Duong","year":"2020","journal-title":"IEEE Sensors J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7432","DOI":"10.1109\/JIOT.2020.2984544","article-title":"Temporal convolutional networks for multiperson activity recognition using a 2-d Lidar","volume":"7","author":"Luo","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10872","DOI":"10.1109\/JIOT.2021.3127186","article-title":"2D Lidar-Based Approach for Activity Identification and Fall Detection","volume":"9","author":"Bouazizi","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bouazizi, M., Ye, C., and Ohtsuki, T. (2021, January 7\u201311). Activity Detection using 2D Lidar for Healthcare and Monitoring. Proceedings of the IEEE Global Communications Conference (GLOBECOM), Madrid, Spain.","DOI":"10.1109\/GLOBECOM46510.2021.9685470"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bouazizi, M., Lorite Mora, A., and Ohtsuki, T. (2023). A 2D-Lidar-Equipped Unmanned Robot-Based Approach for Indoor Human Activity Detection. Sensors, 23.","DOI":"10.3390\/s23052534"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tipaldi, G.D., and Ramos, F. (2009, January 11\u201315). Motion clustering and estimation with conditional random fields. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, St Louis, MO, USA.","DOI":"10.1109\/IROS.2009.5354692"},{"key":"ref_22","unstructured":"Hahnel, D., Triebel, R., Burgard, W., and Thrun, S. (2003, January 14\u201319). Map building with mobile robots in dynamic environments. Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan."},{"key":"ref_23","unstructured":"Besl, P.J., and McKay, N.D. (1992, January 14\u201315). Method for registration of 3-D shapes. Proceedings of the Sensor Fusion IV: Control Paradigms Data Structures, Boston, MA, USA."},{"key":"ref_24","unstructured":"Dub\u00e9, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., and Cadena, C. (June, January 29). Segmatch: Segment based place recognition in 3d point clouds. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Konolige, K., and Agrawal, M. (2007, January 10\u201314). Frame-Frame Matching for Realtime Consistent Visual Mapping. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Rome, Italy.","DOI":"10.1109\/ROBOT.2007.363896"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ha, S., and Choi, S. (2016, January 24\u201329). Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727224"},{"key":"ref_27","unstructured":"Mishkhal, I.A. (2017). Human Activity Recognition Based on Accelerometer and Gyroscope Sensors. [Master\u2019s Thesis, Ball State University]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16979","DOI":"10.1109\/JSEN.2021.3079883","article-title":"Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach","volume":"21","author":"Webber","year":"2021","journal-title":"IEEE Sensors J."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Barna, A., Masum, A.K.M., Hossain, M.E., Bahadur, E.H., and Alam, M.S. (2019, January 7\u20139). A study on human activity recognition using gyroscope, accelerometer, temperature and humidity data. Proceedings of the International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox\u2019sBazar, Bangladesh.","DOI":"10.1109\/ECACE.2019.8679226"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6932","DOI":"10.3390\/s20236932","article-title":"Using convolutional neural networks with multiple thermal sensors for unobtrusive pose recognition","volume":"20","author":"Burns","year":"2020","journal-title":"Sensors"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bouazizi, M., and Ohtsuki, T. (2020, January 20\u201324). An Infrared Array Sensor-Based Method for Localizing and Counting People for Health Care and Monitoring. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176199"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ahmed, S., Park, J., and Cho, S.H. (2022, January 6\u20139). FMCW Radar Sensor Based Human Activity Recognition using Deep Learning. Proceedings of the International Conference on Electronics, Information, and Communication (ICEIC), Jeju, Republic of Korea.","DOI":"10.1109\/ICEIC54506.2022.9748776"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.3390\/electronics10182237","article-title":"Discrete human activity recognition and fall detection by combining FMCW RADAR data of heterogeneous environments for independent assistive living","volume":"10","author":"Saeed","year":"2021","journal-title":"Electronics"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shah, S.A., and Fioranelli, F. (2019, January 23\u201327). Human Activity Recognition: Preliminary Results for Dataset Portability using FMCW Radar. Proceedings of the International Radar Conference (RADAR), Toulon, France.","DOI":"10.1109\/RADAR41533.2019.171307"},{"key":"ref_35","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS 2012), Lake Tahoe, NV, USA."},{"key":"ref_36","unstructured":"Piezzo, C., Leme, B., Hirokawa, M., and Suzuki, K. (September, January 28). Gait measurement by a mobile humanoid robot as a walking trainer. Proceedings of the 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Lisbon, Portugal."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1109\/JSEN.2017.2784900","article-title":"A multi-type features method for leg detection in 2-D laser range data","volume":"18","author":"Li","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10027","DOI":"10.1109\/TCYB.2021.3085489","article-title":"A multimodal data processing system for Lidar-based human activity recognition","volume":"52","author":"Roche","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/TCSVT.2016.2595331","article-title":"Lidar-based gait analysis and activity recognition in a 4d surveillance system","volume":"28","author":"Benedek","year":"2016","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_40","unstructured":"Bouazizi, M., Feghoul, K., Lorite, A., and Ohtsuki, T. (December, January 4). A Novel Approach for Activity, Fall and Gait Detection Using Multiple 2D Lidars. Proceedings of the IEEE Global Communications Conference (GLOBECOM), Kuala Lampur, Malaysia."},{"key":"ref_41","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996, January 2\u20134). Density-based spatial clustering of applications with noise. Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, USA."},{"key":"ref_42","first-page":"484","article-title":"A history of the unity game engine","volume":"483","author":"Haas","year":"2014","journal-title":"Diss. Worcest. Polytech. Inst."},{"key":"ref_43","unstructured":"Hautam\u00e4ki, J. (2022, January 23\u201327). ROS2-Unity-XR interface demonstration. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA."},{"key":"ref_44","unstructured":"Wang, Z., Han, K., and Tiwari, P. (August, January 15). Digital twin simulation of connected and automated vehicles with the unity game engine. Proceedings of the IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI), Beijing, China."},{"key":"ref_45","unstructured":"Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., and Black, M.J. (November, January 27). AMASS: Archive of Motion Capture as Surface Shapes. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/626\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:50:00Z","timestamp":1760104200000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/626"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,18]]},"references-count":45,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020626"],"URL":"https:\/\/doi.org\/10.3390\/s24020626","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,18]]}}}