Elderly Fall Monitoring in Smart Homes Using Wearable Device
Resumo
The constant progress of technology, especially in the area of health, brings numerous benefits, one of which is the increase in human life expectancy. However, problems that occur recurrently among the elderly age group are now on the radar of studies that also seek to improve the quality of life of these people. The number of cases of falls among elderly people is worrying, even more so as this is a portion of the population that tends to live alone. In the context of smart homes, several solutions have emerged for monitoring elderly people to increase safety and provide faster assistance, if necessary. One of these solutions is the use of wearable devices responsible for identifying the person’s movements. This work presents the study and development of a wearable device capable of detecting falls and, if they occur, automatically notifying the necessary people through alert messages via the Telegram application so that they can help the person who has suffered a fall. In this work, a Wi-Fi network, MQTT protocol, accelerometer and gyroscope inertial sensors and an ESP32 board programmed using the Arduino IDE were used. Preliminary tests indicated good performance in recognizing falls, based on tilt angle analysis, gyroscope readings and accelerometer readings. The proof of concept and preliminary tests carried out demonstrate the potential for using low-cost technologies for wearable resources for application in smart homes and monitoring the health of elderly people.
Palavras-chave:
fall detection, wearable technologies, accelerometer and gyroscope, alert messages
Referências
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Shirley Basílio. 2021. O que é Node-RED? Conhecendo e instalando. [link] Acessado: 01-08-2022.
Tingting Chen, Zhenglong Ding, and Biao Li. 2022. Elderly Fall Detection Based on Improved YOLOv5s Network. IEEE Access 10 (2022), 91273–91282. DOI: 10.1109/ACCESS.2022.3202293
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Koldo De Miguel, Alberto Brunete, Miguel Hernando, and Ernesto Gambao. 2017. Home Camera-Based Fall Detection System for the Elderly. Sensors 17, 12 (2017). DOI: 10.3390/s17122864
Luciana De Nardin, Kamila RH Rodrigues, Larissa C Zimmermann, Brunela DM Orlandi, and Maria da Graça C. Pimentel. 2020. Recognition of human activities via wearable sensors: variables identified in a systematic mapping. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 49–56.
Thiago de Quadros, André Eugenio Lazzaretti, and Fábio Kürt Schneider. 2018. A Movement Decomposition and Machine Learning-Based Fall Detection System Using Wrist Wearable Device. IEEE SENSORS 18, 12 (2018).
Irene Gomes e Vinícius Britto. 2024. Censo 2022: número de pessoas com 65 anos ou mais de idade cresceu 57,4% em 12 anos. [link] Acessado: 01-06-2024.
Andressa B Ferreira, Leonardo S Piva, Reinaldo B Braga, and Rossana MC Andrade. 2015. Avaliação da confiança no funcionamento de sistemas de detecção e alerta de quedas. Revista de Informática Aplicada 11, 2 (2015).
Andressa Bezerra Ferreira, Leonardo Sabadini Piva, Reinaldo Bezerra Braga, and Rossana Maria de Castro Andrade. 2014. Trust Evaluation in an Android System for Detection and Alert Falls. In Proceedings of the 20th Brazilian Symposium on Multimedia and the Web. 111–114.
Ligia FREITAS, Elizabete Viana de; PY. 2016. Tratado de geriatria e gerontologia. (4th ed.). Guanabara Koogan. 1651 pages.
Usman M. Daud S. Kabir A. Nawaz Q. Gilani, S.M.M. and O. Judit. 2024. SDN-based multi-level framework for smart home services. (2024), 327–347.
Akash Gupta, Rohini Srivastava, Himanshu Gupta, and Basant Kumar. 2020. IoT Based Fall Detection Monitoring and Alarm System For Elderly. In 2020 IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). 1–5. DOI: 10.1109/UPCON50219.2020.9376569
Sardor Juraev, Akash Ghimire, Jumabek Alikhanov, Vijay Kakani, and Hakil Kim. 2022. Exploring Human Pose Estimation and the Usage of Synthetic Data for Elderly Fall Detection in Real-World Surveillance. IEEE Access 10 (2022), 94249–94261. DOI: 10.1109/ACCESS.2022.3203174
Alexandre Kalache. 2010. WHO Global Report on Falls Prevention in Older Age. [link] Acessado: 01-06-2024.
Suchitporn Lersilp, Supawadee Putthinoi, Peerasak Lerttrakarnnon, and Patima Silsupadol. 2020. Development and Usability Testing of an Emergency Alert Device for Elderly People and People with Disabilities. e Scientific World Journal 2020 (2020).
Muhammad Mubashir, Ling Shao, and Luke Seed. 2013. A survey on fall detection: Principles and approaches. Neurocomputing 100 (2013).
Md. Jaber Al Nahian, Tapotosh Ghosh, Md. Hasan Al Banna, Mohammed A. Aseeri, Mohammed Nasir Uddin, Muhammad Raisuddin Ahmed, Mufti Mahmud, and M. Shamim Kaiser. 2021. Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features. IEEE Access 9 (2021), 39413–39431. DOI: 10.1109/ACCESS.2021.3056441
Camila Pereira de Oliveira, Cristiano da Silveira Colombo, and Daniel José Ventorim Nunes. 2024. Machine Association for Computing Machinery, New York, NY, USA, Article 58, 9 pages.Learning Applied To Fall Detection in the Elderly. In Proceedings of the 20th Brazilian Symposium on Information Systems (SBSI ’24). DOI: 10.1145/3658271.3658330
Guilherme Bruno Araújo Pimenta. 2019. Uso da Ferramenta Node-RED em Processos de Automatização no Cenário da Quarta Revolução Industrial. Monografia. Universidade Federal de Lavras, Lavras - MG.
Mahsa T. Pourazad, Anahita Shojaei-Hashemi, Panos Nasiopoulos, Maryam Azimi, Michelle Mak, Jennifer Grace, Doojin Jung, and Taran Bains. 2020. A Non-Intrusive Deep Learning Based Fall Detection Scheme Using Video Cameras. In 2020 International Conference on Information Networking (ICOIN). 443–446. DOI: 10.1109/ICOIN48656.2020.9016455
Luis GS Rodrigues, Diego RC Dias, Marcelo P Guimarães, Alexandre F Brandão, Leonardo CD Rocha, Rogério L Iope, and José RF Brega. 2021. Upper limb motion tracking and classification: A smartphone approach. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 61–64.
Thanos G. Stavropoulos, Asterios Papastergiou, Lampros Mpaltadoros, Spiros Nikolopoulos, and Ioannis Kompatsiaris. 2020. IoT Wearable Sensors and Devices in Elderly Care: A Literature Review. Sensors 20 (2020).
Deen MJ. Subramaniam S, Faisal AI. 2022. Wearable Sensor Systems for Fall Risk Assessment: A Review. Front Digit Health.
Guilherme Gerzson Torres. 2018. Tecnologia Assistiva para Detecção de Quedas: Desenvolvimento de Sensor Vestível Integrado ao Sistema de Casa Inteligente. Dissertação de mestrado. Universidade Federal do Rio Grande do Sul, Porto Alegre-RS. [link].
Xueyi Wang, Joshua Ellul, and George Azzopardi. 2020. Elderly Fall Detection Systems: A Literature Survey. Frontiers in Robotics and AI 7 (2020). DOI: 10.3389/frobt.2020.00071
Jing Zhang, Jia Li, and Weibing Wang. 2021. A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors. Sensors 21, 19 (2021). DOI: 10.3390/s21196511
Sejal Badgujar and Anju S. Pillai. 2020. Fall Detection for Elderly People using Machine Learning. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 1–4. DOI: 10.1109/ICCCNT49239.2020.9225494
Shirley Basílio. 2021. O que é Node-RED? Conhecendo e instalando. [link] Acessado: 01-08-2022.
Tingting Chen, Zhenglong Ding, and Biao Li. 2022. Elderly Fall Detection Based on Improved YOLOv5s Network. IEEE Access 10 (2022), 91273–91282. DOI: 10.1109/ACCESS.2022.3202293
Ministério da Saúde. 2009. Quedas de idosos. [link]. Acessado: 01-06-2024.
Koldo De Miguel, Alberto Brunete, Miguel Hernando, and Ernesto Gambao. 2017. Home Camera-Based Fall Detection System for the Elderly. Sensors 17, 12 (2017). DOI: 10.3390/s17122864
Luciana De Nardin, Kamila RH Rodrigues, Larissa C Zimmermann, Brunela DM Orlandi, and Maria da Graça C. Pimentel. 2020. Recognition of human activities via wearable sensors: variables identified in a systematic mapping. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 49–56.
Thiago de Quadros, André Eugenio Lazzaretti, and Fábio Kürt Schneider. 2018. A Movement Decomposition and Machine Learning-Based Fall Detection System Using Wrist Wearable Device. IEEE SENSORS 18, 12 (2018).
Irene Gomes e Vinícius Britto. 2024. Censo 2022: número de pessoas com 65 anos ou mais de idade cresceu 57,4% em 12 anos. [link] Acessado: 01-06-2024.
Andressa B Ferreira, Leonardo S Piva, Reinaldo B Braga, and Rossana MC Andrade. 2015. Avaliação da confiança no funcionamento de sistemas de detecção e alerta de quedas. Revista de Informática Aplicada 11, 2 (2015).
Andressa Bezerra Ferreira, Leonardo Sabadini Piva, Reinaldo Bezerra Braga, and Rossana Maria de Castro Andrade. 2014. Trust Evaluation in an Android System for Detection and Alert Falls. In Proceedings of the 20th Brazilian Symposium on Multimedia and the Web. 111–114.
Ligia FREITAS, Elizabete Viana de; PY. 2016. Tratado de geriatria e gerontologia. (4th ed.). Guanabara Koogan. 1651 pages.
Usman M. Daud S. Kabir A. Nawaz Q. Gilani, S.M.M. and O. Judit. 2024. SDN-based multi-level framework for smart home services. (2024), 327–347.
Akash Gupta, Rohini Srivastava, Himanshu Gupta, and Basant Kumar. 2020. IoT Based Fall Detection Monitoring and Alarm System For Elderly. In 2020 IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). 1–5. DOI: 10.1109/UPCON50219.2020.9376569
Sardor Juraev, Akash Ghimire, Jumabek Alikhanov, Vijay Kakani, and Hakil Kim. 2022. Exploring Human Pose Estimation and the Usage of Synthetic Data for Elderly Fall Detection in Real-World Surveillance. IEEE Access 10 (2022), 94249–94261. DOI: 10.1109/ACCESS.2022.3203174
Alexandre Kalache. 2010. WHO Global Report on Falls Prevention in Older Age. [link] Acessado: 01-06-2024.
Suchitporn Lersilp, Supawadee Putthinoi, Peerasak Lerttrakarnnon, and Patima Silsupadol. 2020. Development and Usability Testing of an Emergency Alert Device for Elderly People and People with Disabilities. e Scientific World Journal 2020 (2020).
Muhammad Mubashir, Ling Shao, and Luke Seed. 2013. A survey on fall detection: Principles and approaches. Neurocomputing 100 (2013).
Md. Jaber Al Nahian, Tapotosh Ghosh, Md. Hasan Al Banna, Mohammed A. Aseeri, Mohammed Nasir Uddin, Muhammad Raisuddin Ahmed, Mufti Mahmud, and M. Shamim Kaiser. 2021. Towards an Accelerometer-Based Elderly Fall Detection System Using Cross-Disciplinary Time Series Features. IEEE Access 9 (2021), 39413–39431. DOI: 10.1109/ACCESS.2021.3056441
Camila Pereira de Oliveira, Cristiano da Silveira Colombo, and Daniel José Ventorim Nunes. 2024. Machine Association for Computing Machinery, New York, NY, USA, Article 58, 9 pages.Learning Applied To Fall Detection in the Elderly. In Proceedings of the 20th Brazilian Symposium on Information Systems (SBSI ’24). DOI: 10.1145/3658271.3658330
Guilherme Bruno Araújo Pimenta. 2019. Uso da Ferramenta Node-RED em Processos de Automatização no Cenário da Quarta Revolução Industrial. Monografia. Universidade Federal de Lavras, Lavras - MG.
Mahsa T. Pourazad, Anahita Shojaei-Hashemi, Panos Nasiopoulos, Maryam Azimi, Michelle Mak, Jennifer Grace, Doojin Jung, and Taran Bains. 2020. A Non-Intrusive Deep Learning Based Fall Detection Scheme Using Video Cameras. In 2020 International Conference on Information Networking (ICOIN). 443–446. DOI: 10.1109/ICOIN48656.2020.9016455
Luis GS Rodrigues, Diego RC Dias, Marcelo P Guimarães, Alexandre F Brandão, Leonardo CD Rocha, Rogério L Iope, and José RF Brega. 2021. Upper limb motion tracking and classification: A smartphone approach. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 61–64.
Thanos G. Stavropoulos, Asterios Papastergiou, Lampros Mpaltadoros, Spiros Nikolopoulos, and Ioannis Kompatsiaris. 2020. IoT Wearable Sensors and Devices in Elderly Care: A Literature Review. Sensors 20 (2020).
Deen MJ. Subramaniam S, Faisal AI. 2022. Wearable Sensor Systems for Fall Risk Assessment: A Review. Front Digit Health.
Guilherme Gerzson Torres. 2018. Tecnologia Assistiva para Detecção de Quedas: Desenvolvimento de Sensor Vestível Integrado ao Sistema de Casa Inteligente. Dissertação de mestrado. Universidade Federal do Rio Grande do Sul, Porto Alegre-RS. [link].
Xueyi Wang, Joshua Ellul, and George Azzopardi. 2020. Elderly Fall Detection Systems: A Literature Survey. Frontiers in Robotics and AI 7 (2020). DOI: 10.3389/frobt.2020.00071
Jing Zhang, Jia Li, and Weibing Wang. 2021. A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors. Sensors 21, 19 (2021). DOI: 10.3390/s21196511
Publicado
14/10/2024
Como Citar
MOREIRA, Júlia M. P.; BETTIO, Raphael W.; FREIRE, André P.; SANTOS, Luciano M.; PEREIRA, Marluce R..
Elderly Fall Monitoring in Smart Homes Using Wearable Device. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 30. , 2024, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 124-132.
DOI: https://doi.org/10.5753/webmedia.2024.241629.
