PublisherDOIYearVolumeIssuePageTitleAuthor(s)Link
Healthcare Informatics Research10.4258/hir.2017.23.3.1472017233147Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype DataMoiz Ahmed, Nadeem Mehmood, Adnan Nadeem, Amir Mehmood, Kashif Rizwanhttps://synapse.koreamed.org/pdf/10.4258/hir.2017.23.3.147, https://synapse.koreamed.org/DOIx.php?id=10.4258/hir.2017.23.3.147, https://synapse.koreamed.org/DOIx.php?id=10.4258/hir.2017.23.3.147
2019 13th International Conference on Sensing Technology (ICST)10.1109/icst46873.2019.90477322019Machine learning-based fall detection system for the elderly using passive RFID sensor tagsKoichi Toda, Norihiko Shinomiyahttp://xplorestaging.ieee.org/ielx7/9039856/9047668/09047732.pdf?arnumber=9047732
2021 International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB)10.1109/iceib53692.2021.96864352021Nine-axis Posture Sensor-based Fall Detection and Alarm SystemYanzhe Zhao, Shuwen Liu, Shukai Tanghttp://xplorestaging.ieee.org/ielx7/9686355/9686372/09686435.pdf?arnumber=9686435
2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)10.1109/icce-tw46550.2019.89919952019Indoor fall detection system for the elderly using passive RFID sensor tagsK. Toda, N. Shinomiyahttp://xplorestaging.ieee.org/ielx7/8966968/8991681/08991995.pdf?arnumber=8991995
Journal of Biotechnology10.1016/j.jbiotec.2016.05.1232016231S29-S30Classification of fall detection in elderly persons based on smart phone dataPranesh Vallabh, Reza Malekian, Ning Ye, Dijana Capeska Bogatinoska, Aleksandar Karadimce, Jozef Ritonjahttps://api.elsevier.com/content/article/PII:S0168165616303868?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S0168165616303868?httpAccept=text/plain
Procedia Computer Science10.1016/j.procs.2019.01.2642019147276-282A HOG-SVM Based Fall Detection IoT System for Elderly Persons Using Deep SensorXiangbo Kong, Zelin Meng, Naoto Nojiri, Yuji Iwahori, Lin Meng, Hiroyuki Tomiyamahttps://api.elsevier.com/content/article/PII:S187705091930287X?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S187705091930287X?httpAccept=text/plain
Procedia Computer Science10.1016/j.procs.2022.07.005202220316-23Automated Machine Learning based Elderly Fall Detection ClassificationFirdous Kausar, Medhat Awadalla, Mostefa Mesbah, Taif AlBadihttps://api.elsevier.com/content/article/PII:S187705092200610X?httpAccept=text/xml, https://api.elsevier.com/content/article/PII:S187705092200610X?httpAccept=text/plain
2020 6th International Conference on Big Data and Information Analytics (BigDIA)10.1109/bigdia51454.2020.000692020Hybrid Real-Time Fall Detection System Based on Deep Learning and Multi-sensor FusionXiaojie Lv, Zongliang Gao, Changshun Yuan, Meng Li, Chao Chenhttp://xplorestaging.ieee.org/ielx7/9384488/9384515/09384546.pdf?arnumber=9384546
Electronics10.3390/electronics1107103020221171030A Novel Feature Set Extraction Based on Accelerometer Sensor Data for Improving the Fall Detection SystemHong-Lam Le, Duc-Nhan Nguyen, Thi-Hau Nguyen, Ha-Nam Nguyenhttps://www.mdpi.com/2079-9292/11/7/1030/pdf
Sensors10.3390/s17122864201717122864Home Camera-Based Fall Detection System for the ElderlyKoldo de Miguel, Alberto Brunete, Miguel Hernando, Ernesto Gambaohttp://www.mdpi.com/1424-8220/17/12/2864/pdf