{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T21:21:00Z","timestamp":1792099260106,"version":"4.4.0"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council, Taiwan","award":["NSTC 113-2221-E-027-082"],"award-info":[{"award-number":["NSTC 113-2221-E-027-082"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. A common limitation is the lack of location awareness, especially in outdoor scenarios where accurately determining the fall location is crucial for a timely emergency response. Moreover, the complexity of many existing algorithms poses a challenge for deployment on edge devices, such as wearable systems, which are constrained by limited computational resources and battery life. As a result, these solutions are often impractical for long-term, continuous use in practical settings. To address the aforementioned issues, we developed a portable, wearable device that integrates a microcontroller (MCU), an inertial sensor, and a chip module featuring Global Positioning System (GPS) and Narrowband Internet of Things (NB-IoT) technologies. A low-complexity algorithm based on a finite-state machine was employed to detect fall events, enabling the module to meet the requirements for long-term outdoor use. The proposed algorithm is capable of filtering out eight types of daily activities\u2014running, walking, sitting, ascending stairs, descending stairs, stepping, jumping, and rapid sitting\u2014while detecting four types of falls: forward, backward, left, and right. In case a fall event is detected, the device immediately transmits a fall alert and GPS coordinates to a designated server via NB-IoT. The server then forwards the alert to a specified communication application. Experimental tests demonstrated the system\u2019s effectiveness in outdoor environments. A total of 6750 samples were collected from fifteen test participants, including 6000 daily activity samples and 750 fall events. The system achieved an average sensitivity of 97.9%, an average specificity of 99.9%, and an overall accuracy of 99.7%. The implementation of this system provides enhanced safety assurance for elderly individuals during outdoor activities.<\/jats:p>","DOI":"10.3390\/s25123632","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T05:11:49Z","timestamp":1749532309000},"page":"3632","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Wearable Fall Detection System with Real-Time Localization and Notification Capabilities"],"prefix":"10.3390","volume":"25","author":[{"given":"Chin-Kun","family":"Tseng","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan"},{"name":"Tri-Service General Hospital Songshan Branch, Taipei 105309, Taiwan"},{"name":"National Defense Medical Center, Taipei 114201, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi-Jia","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8115-3751","authenticated-orcid":false,"given":"Lih-Jen","family":"Kau","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Taipei University of Technology, Taipei 106344, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"ref_1","unstructured":"National Development Council (2025, June 09). Population Projections for the Taiwan. National Development Council, October 2024, Available online: https:\/\/pop-proj.ndc.gov.tw\/main_en\/cp.aspx?n=137."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, T., Zhou, Y., and Zhu, J. (2018). New Advances and Challenges of Fall Detection Systems: A Survey. Appl. Sci., 8.","DOI":"10.3390\/app8030418"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hsieh, C.-Y., Huang, C.-N., Liu, K.-C., Chu, W.-C., and Chan, C.-T. (2016, January 12\u201313). A machine learning approach to fall detection algorithm using wearable sensor. Proceedings of the 2016 International Conference on Advanced Materials for Science and Engineering (ICAMSE), Tainan, Taiwan.","DOI":"10.1109\/ICAMSE.2016.7840209"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nari, M.I., Suprapto, S.S., Kusumah, I.H., and Adiprawita, W. (2016, January 29\u201330). A simple design of wearable device for fall detection with accelerometer and gyroscope. Proceedings of the 2016 International Symposium on Electronics and Smart Devices (ISESD), Bandung, Indonesia.","DOI":"10.1109\/ISESD.2016.7886698"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Boudouane, I., Makhlouf, A., Djelal, N., Saadia, N., and Ramdane-Ch\u00e9rif, A. (2023, January 12\u201314). Portable Fall Detection System Using a Triaxial Accelerometer Based on a Thresholds Algorithm. Proceedings of the 2023 IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE), Sousse, Tunisia.","DOI":"10.1109\/ICAIGE58321.2023.10346559"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15219","DOI":"10.1109\/JSEN.2024.3375603","article-title":"A Novel Embedded Deep Learning Wearable Sensor for Fall Detection","volume":"24","author":"Campanella","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/JBHI.2014.2328593","article-title":"A Smart Phone-based Pocket Fall Accident Detection, Positioning and Rescue System","volume":"19","author":"Kau","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6000804","DOI":"10.1109\/LSENS.2020.2996746","article-title":"Fall monitoring for the elderly using wearable inertial measurement sensors on eyeglasses","volume":"4","author":"Lin","year":"2020","journal-title":"IEEE Sens. Lett."},{"key":"ref_9","unstructured":"Pandya, B., Pourabdollah, A., and Lotfi, A. (July, January 30). Fuzzy logic web services for real-time fall detection using wearable accelerometer and gyroscope sensors. Proceedings of the 13th ACM International Conference on Pervasive Technologies Related to Assistive Environments, Corfu, Greece."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Casilari, E., \u00c1lvarez-Marco, M., and Garc\u00eda-Lagos, F. (2020). A study of the use of gyroscope measurements in wearable fall detection systems. Symmetry, 12.","DOI":"10.3390\/sym12040649"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"15154","DOI":"10.1109\/JSEN.2022.3184513","article-title":"A three-stage low-complexity human fall detection method using IR-UWB radar","volume":"22","author":"Chen","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6842","DOI":"10.1109\/TII.2021.3049342","article-title":"Contactless fall detection using time-frequency analysis and convolutional neural networks","volume":"17","author":"Sadreazami","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21038","DOI":"10.1109\/JIOT.2022.3175894","article-title":"Fall detection system using millimeter-wave radar based on neural network and information fusion","volume":"9","author":"Yao","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"129965","DOI":"10.1109\/ACCESS.2021.3113824","article-title":"A pose estimation-based fall detection methodology using artificial intelligence edge computing","volume":"9","author":"Chang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1109\/TMC.2016.2557795","article-title":"RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices","volume":"16","author":"Wang","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TCE.2020.3021398","article-title":"A WiFi-Based Smart Home Fall Detection System Using Recurrent Neural Network","volume":"66","author":"Ding","year":"2020","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/JBHI.2018.2808281","article-title":"Deep Learning for Fall Detection: Three-Dimensional CNN Combined with LSTM on Video Kinematic Data","volume":"23","author":"Lu","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Santos, G.L., Lima, M.A., de Souza, F.M., de Albuquerque, V.H.C., and Cardoso, J.M.P. (2019). Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks. Sensors, 19.","DOI":"10.3390\/s19071644"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8575","DOI":"10.1109\/JSEN.2020.3045135","article-title":"Deep ConvLSTM with Self-Attention for Human Activity Decoding Using Wearable Sensors","volume":"21","author":"Singh","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1109\/JBHI.2020.3027967","article-title":"Fall Detection with UWB Radars and CNN-LSTM Architecture","volume":"25","author":"Maitre","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"12497","DOI":"10.1109\/TKDE.2023.3277839","article-title":"Channel Attention for Sensor-Based Activity Recognition: Embedding Features into All Frequencies in DCT Domain","volume":"35","author":"Xu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1109\/TCSVT.2023.3303258","article-title":"Video-Based Fall Detection Using Human Pose and Constrained Generative Adversarial Network","volume":"34","author":"Wu","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_23","unstructured":"STMicroelectronics (2025, June 09). STM32WB5MMG Datasheet. Available online: https:\/\/www.st.com\/en\/microcontrollers-microprocessors\/stm32wb5mmg.html."},{"key":"ref_24","unstructured":"STMicroelectronics (2025, June 09). LSM6DSO Datasheet. Available online: https:\/\/www.st.com\/en\/mems-and-sensors\/lsm6dso.html."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"\u00d6zdemir, A.T. (2016). An analysis on sensor locations of the human body for wearable fall detection devices: Principles and practice. Sensors, 16.","DOI":"10.3390\/s16081161"},{"key":"ref_26","unstructured":"Analog Devices (2025, June 09). Using an Accelerometer for Inclination Sensing. Available online: https:\/\/www.analog.com\/en\/resources\/app-notes\/an-1057.html."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1109\/TNSRE.2015.2460373","article-title":"Prior-to- and Post-Impact Fall Detection Using Inertial and Barometric Altimeter Measurements","volume":"24","author":"Sabatini","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. 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