Wearable IoT Devices: Feasibility and Effectiveness in Implementing Local Decision-Making for Elderly Fall Detection
Resumo
Context: The growing use of IoT wearables in healthcare highlights their potential for elderly fall detection. However, wearable devices based on inertial sensors have processing and energy consumption limitations, making them unsuitable for complex algorithms. Traditional cloud systems face latency and bandwidth issues when processing inertial data, which may delay caregiver responses. Edge computing, with local decision-making, emerges as an efficient and energy-conscious alternative. Problem: The overload in transmitting inertial sensor data to remote servers can delay notifications, putting elderly individuals whose falls are undetected at risk. Rapid intervention is crucial to prevent severe consequences. Solution: This study implements a decision tree algorithm processed locally on the ESP32 microcontroller, enabling real-time fall detection while reducing energy consumption and response times. SI Theory: The study applies General Systems Theory by integrating inertial sensor data, software algorithms, and human behavior. This systemic approach optimizes device performance, promoting greater efficiency in real-time health monitoring and improving elderly safety. Method: This applied, quantitative research pre-processed accelerometer and gyroscope data into statistical metrics. A decision tree algorithm was developed and validated for local processing on the ESP32, using a dataset tailored for fall detection. Summary of Results: The proposed solution on the ESP32 detected falls with precision, without false positives, distinguishing daily activities in controlled tests. Additionally, it demonstrated high energy efficiency, validating its viability for IoT wearable devices. Contributions and Impact in the IS area: This study validates the integration of edge computing with wearable devices, balancing computational efficiency, energy consumption, and safety, offering effective solutions for health monitoring.
Palavras-chave:
Elderly, Fall detection, IOT, Edge-Computing, Decision Tree, Wereables
Referências
Luigi Atzori, Antonio Iera e Giacomo Morabito. 2010. The internet of things: a survey. Computer Networks, 54, 15, 2787–2805. DOI: 10.1016/j.comnet.2010.05.010.
Marek Babiuch, Petr Foltýnek e Pavel Smutný. 2019. Using the esp32 microcontroller for data processing. Em 2019 20th International Carpathian Control Conference (ICCC), 1–6. DOI: 10.1109/CarpathianCC.2019.8765944.
Andrew Banks, Ed Briggs, Ken Borgendale e Rahul Gupta. 2019. MQTT Version 5.0. Andrew Banks, Ed Briggs, Ken Borgendale e Rahul Gupta, editores. OASIS Standard. (Mar. de 2019). [link].
Massimo Banzi e Michael Shiloh. 2022. Getting Started with Arduino. (3rd ed.). O’Reilly Media, Sebastopol, CA. isbn: 978-1-491-95759-0.
Armir Bujari, Ombretta Gaggi, Claudio Enrico Palazzi e Giacomo Quadrio. 2018. Smart wearable sensors: analysis of a real case study. 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 37–41. [link].
Tiago Carneiro, Raul Victor Medeiros Da NóBrega, Thiago Nepomuceno, Gui-Bin Bian, Victor Hugo C. De Albuquerque e Pedro Pedrosa Rebouças Filho. 2018. Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access, 6, 61677–61685. DOI: 10.1109/ACCESS.2018.2874767.
Eduardo Casilari, Moisés Álvarez-Marco e Francisco García-Lagos. 2020. A study of the use of gyroscope measurements in wearable fall detection systems. Symmetry, 12, 4. DOI: 10.3390/sym12040649.
Lin Chen, Rong Li, Hang Zhang, Lili Tian e Ning Chen. 2019. Intelligent fall detection method based on accelerometer data from a wrist-worn smart watch. Measurement, 140, 215–226. DOI: 10.1016/j.measurement.2019.03.079.
Kimaya Desai, Pritam Mane, Manish Dsilva, Amogh Zare, Parth Shingala e Dayanand Ambawade. 2020. A novel machine learning based wearable belt for fall detection. Em 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), 502–505. DOI: 10.1109/GUCON48875.2020.9231114.
A. Elnashar. 2018. Iot evolution towards a super-connected world. Practical Guide to LTE-A, VoLTE and IoT. DOI: 10.1002/9781119063407.ch7.
I Arun Faisal, T Waluyo Purboyo e A Siswo Raharjo Ansori. 2019. A review of accelerometer sensor and gyroscope sensor in imu sensors on motion capture. J. Eng. Appl. Sci, 15, 3, 826–829.
Vanilson Fula e Plinio Moreno. 2024. Wrist-based fall detection: towards generalization across datasets. Sensors. DOI: 10.3390/s24051679.
Eva García-Martín. 2017. Extraction and energy efficient processing of streaming data. Tese de doutoramento. Blekinge Tekniska Högskola.
Aurélien Gerón. 2019. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. English. O’Reilly Media; 856 pp. isbn: 9781492032649.
Antônio Carlos Gil e outros. 2002. Como elaborar projetos de pesquisa. Vol. 4. Atlas São Paulo, [S.l.]
Gunther Gridling e Bettina Weiss. 2007. Introduction to microcontrollers. Vienna University of Technology Institute of Computer Engineering Embedded Computing Systems Group, 25.
Yujin Hong, Ig-Jae Kim, Sang Ahn e Hyoung-Gon Kim. 2010. Mobile health monitoring system based on activity recognition using accelerometer. Simulation Modelling Practice and Theory, 18, (abr. de 2010), 446–455. DOI: 10.1016/j.simpat.2009.09.002.
Shang-Lin Hsieh, Chun-Che Chen, Shin-Han Wu e Tai-Wen Yue. 2014. A wrist-worn fall detection system using accelerometers and gyroscopes. Em Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, 518–523. DOI: 10.1109/ICNSC.2014.6819680.
Emőd Kovács. 2012. Rotation about an arbitrary axis and reflection through an arbitrary plane. Em Annales Mathematicae et Informaticae. Vol. 40, 175–186.
Stephen R. Lord, Catherine Sherrington e Hylton B. Menz. 2001. Falls in Older People: Risk Factors and Strategies for Prevention. First published 2001. Includes index. Cambridge University Press, Cambridge, United Kingdom. isbn: 0-521-58964-9. [link].
Emna Mezghani, Ernesto Exposito e Khalil Drira. 2017. A model-driven methodology for the design of autonomic and cognitive iot-based systems: application to healthcare. IEEE Transactions on Emerging Topics in Computational Intelligence, 1, 3, 224–234. DOI: 10.1109/TETCI.2017.2699218.
H. Mohajan. 2020. Quantitative research: a successful investigation in natural and social sciences. Journal of Economic Development, Environment and People. DOI: 10.26458/jedep.v9i4.679.
Ilkka Niiniluoto. 1993. The aim and structure of applied research. Erkenntnis, 38, 1, 1–21. DOI: 10.1007/BF01129020.
Olukunle Ojetola, Elena I. Gaura e James Brusey. 2011. Fall Detection with Wearable Sensors—Safe (Smart Fall Detection). Em Proceedings of the 7th International Conference on Intelligent Environments (IE). IEEE, Nottingham, UK. DOI: 10.1109/IE.2011.38.
Camila Pereira de Oliveira. 2023. Aprendizagem de máquina aplicada à detecção de queda de idosos. Trabalho de Conclusão de Curso (Bacharelado em Sistemas de Informação). Instituto Federal do Espírito Santo (IFES), Cachoeiro de Itapemirim, (dez de 2023), 68. [link].
Tasnim Hossain Orpa, Adil Ahnaf, Tareque Bashar Ovi e Mubdiul Islam Rizu. 2022. An iot based healthcare solution with esp32 using machine learning model. Em 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), 1–6. DOI: 10.1109/STI56238.2022.10103231.
Alexandros Pantelopoulos e Nikolaos G. Bourbakis. 2010. Prognosis—a wearable health-monitoring system for people at risk: methodology and modeling. IEEE Transactions on Information Technology in Biomedicine, 14, 3, 613–621. DOI: 10.1109/TITB.2010.2040085.
Maurício César Pinto Pessoa, Geraldo Braz Junior e Tiago Bonini Borchartt. 2016. Home e-Care: Monitoramento de quedas em idosos através de smartwatches. Em XV Congresso Brasileiro de Informática em Saúde (CBIS). SBIS, Goiânia, Brasil, 265–276. [link].
Sarah Brandão Pinheiro, ALS SILVA, CJ CÁRDENAS e ML SILVA. 2015. A síndrome do pós-queda em idosos que sofrem fratura de fêmur. Cadernos de Estudos e Pesquisas, 19, 41.
Alvin Rajkomar, Jeffrey Dean e Isaac Kohane. 2019. Machine learning in medicine. New England Journal of Medicine, 380, 14, 1347–1358. PMID: 30943338. eprint: [link]. DOI: 10.1056/NEJMra1814259.
Arkham Zahri Rakhman, Lukito Edi Nugroho, Widyawan e Kurnianingsih. 2014. Fall detection system using accelerometer and gyroscope based on smartphone. Em 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering, 99–104. DOI: 10.1109/ICITACEE.2014.7065722.
Ali Akbar Sadri, Amir Masoud Rahmani, Morteza Saberikamarposhti e Mehdi Hosseinzadeh. 2021. Fog data management: a vision, challenges, and future directions. Journal of Network and Computer Applications, 174, 102882. DOI: 10.1016/j.jnca.2020.102882.
Suranga Seneviratne, Yining Hu, Tham Nguyen, Guohao Lan, Sara Khalifa, Kanchana Thilakarathna, Mahbub Hassan e Aruna Seneviratne. 2017. A survey of wearable devices and challenges. IEEE Communications Surveys Tutorials, 19, 4, 2573–2620. DOI: 10.1109/COMST.2017.2731979.
Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li e Lanyu Xu. 2016. Edge computing: vision and challenges. IEEE Internet of Things Journal, 3, 5, 637–646. DOI: 10.1109/JIOT.2016.2579198.
Robert Susmaga. 2004. Confusion matrix visualization. Em Intelligent Information Processing and Web Mining: Proceedings of the International IIS: IIPWM ‘04 Conference held in Zakopane, Poland, May 17–20, 2004. Springer, 107–116.
Chin-Woo Tan e Sungsu Park. 2005. Design of accelerometer-based inertial navigation systems. IEEE Transactions on Instrumentation and Measurement, 54, 6, 2520–2530. DOI: 10.1109/TIM.2005.858129.
Prajoona Valsalan, Tariq Ahmed Barham Baomar e Ali Hussain Omar Baabood. 2020. Iot based health monitoring system. Journal of critical reviews, 7, 4, 739–743.
Waveshare. 2024. Esp32-s3-touch-lcd-1.69. Acesso em: 14 nov. 2024. (2024). [link].
Thomas C. Wong, John G. Webster, Henry J. Montoye e Richard Washburn. 1981. Portable accelerometer device for measuring human energy expenditure. IEEE Transactions on Biomedical Engineering, BME-28, 6, 467–471. DOI: 10.1109/TBME.1981.324820.
A.D. Wood e J.A. Stankovic. 2002. Denial of service in sensor networks. Computer, 35, 10, 54–62. DOI: 10.1109/MC.2002.1039518.
Marek Babiuch, Petr Foltýnek e Pavel Smutný. 2019. Using the esp32 microcontroller for data processing. Em 2019 20th International Carpathian Control Conference (ICCC), 1–6. DOI: 10.1109/CarpathianCC.2019.8765944.
Andrew Banks, Ed Briggs, Ken Borgendale e Rahul Gupta. 2019. MQTT Version 5.0. Andrew Banks, Ed Briggs, Ken Borgendale e Rahul Gupta, editores. OASIS Standard. (Mar. de 2019). [link].
Massimo Banzi e Michael Shiloh. 2022. Getting Started with Arduino. (3rd ed.). O’Reilly Media, Sebastopol, CA. isbn: 978-1-491-95759-0.
Armir Bujari, Ombretta Gaggi, Claudio Enrico Palazzi e Giacomo Quadrio. 2018. Smart wearable sensors: analysis of a real case study. 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 37–41. [link].
Tiago Carneiro, Raul Victor Medeiros Da NóBrega, Thiago Nepomuceno, Gui-Bin Bian, Victor Hugo C. De Albuquerque e Pedro Pedrosa Rebouças Filho. 2018. Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access, 6, 61677–61685. DOI: 10.1109/ACCESS.2018.2874767.
Eduardo Casilari, Moisés Álvarez-Marco e Francisco García-Lagos. 2020. A study of the use of gyroscope measurements in wearable fall detection systems. Symmetry, 12, 4. DOI: 10.3390/sym12040649.
Lin Chen, Rong Li, Hang Zhang, Lili Tian e Ning Chen. 2019. Intelligent fall detection method based on accelerometer data from a wrist-worn smart watch. Measurement, 140, 215–226. DOI: 10.1016/j.measurement.2019.03.079.
Kimaya Desai, Pritam Mane, Manish Dsilva, Amogh Zare, Parth Shingala e Dayanand Ambawade. 2020. A novel machine learning based wearable belt for fall detection. Em 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), 502–505. DOI: 10.1109/GUCON48875.2020.9231114.
A. Elnashar. 2018. Iot evolution towards a super-connected world. Practical Guide to LTE-A, VoLTE and IoT. DOI: 10.1002/9781119063407.ch7.
I Arun Faisal, T Waluyo Purboyo e A Siswo Raharjo Ansori. 2019. A review of accelerometer sensor and gyroscope sensor in imu sensors on motion capture. J. Eng. Appl. Sci, 15, 3, 826–829.
Vanilson Fula e Plinio Moreno. 2024. Wrist-based fall detection: towards generalization across datasets. Sensors. DOI: 10.3390/s24051679.
Eva García-Martín. 2017. Extraction and energy efficient processing of streaming data. Tese de doutoramento. Blekinge Tekniska Högskola.
Aurélien Gerón. 2019. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. English. O’Reilly Media; 856 pp. isbn: 9781492032649.
Antônio Carlos Gil e outros. 2002. Como elaborar projetos de pesquisa. Vol. 4. Atlas São Paulo, [S.l.]
Gunther Gridling e Bettina Weiss. 2007. Introduction to microcontrollers. Vienna University of Technology Institute of Computer Engineering Embedded Computing Systems Group, 25.
Yujin Hong, Ig-Jae Kim, Sang Ahn e Hyoung-Gon Kim. 2010. Mobile health monitoring system based on activity recognition using accelerometer. Simulation Modelling Practice and Theory, 18, (abr. de 2010), 446–455. DOI: 10.1016/j.simpat.2009.09.002.
Shang-Lin Hsieh, Chun-Che Chen, Shin-Han Wu e Tai-Wen Yue. 2014. A wrist-worn fall detection system using accelerometers and gyroscopes. Em Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, 518–523. DOI: 10.1109/ICNSC.2014.6819680.
Emőd Kovács. 2012. Rotation about an arbitrary axis and reflection through an arbitrary plane. Em Annales Mathematicae et Informaticae. Vol. 40, 175–186.
Stephen R. Lord, Catherine Sherrington e Hylton B. Menz. 2001. Falls in Older People: Risk Factors and Strategies for Prevention. First published 2001. Includes index. Cambridge University Press, Cambridge, United Kingdom. isbn: 0-521-58964-9. [link].
Emna Mezghani, Ernesto Exposito e Khalil Drira. 2017. A model-driven methodology for the design of autonomic and cognitive iot-based systems: application to healthcare. IEEE Transactions on Emerging Topics in Computational Intelligence, 1, 3, 224–234. DOI: 10.1109/TETCI.2017.2699218.
H. Mohajan. 2020. Quantitative research: a successful investigation in natural and social sciences. Journal of Economic Development, Environment and People. DOI: 10.26458/jedep.v9i4.679.
Ilkka Niiniluoto. 1993. The aim and structure of applied research. Erkenntnis, 38, 1, 1–21. DOI: 10.1007/BF01129020.
Olukunle Ojetola, Elena I. Gaura e James Brusey. 2011. Fall Detection with Wearable Sensors—Safe (Smart Fall Detection). Em Proceedings of the 7th International Conference on Intelligent Environments (IE). IEEE, Nottingham, UK. DOI: 10.1109/IE.2011.38.
Camila Pereira de Oliveira. 2023. Aprendizagem de máquina aplicada à detecção de queda de idosos. Trabalho de Conclusão de Curso (Bacharelado em Sistemas de Informação). Instituto Federal do Espírito Santo (IFES), Cachoeiro de Itapemirim, (dez de 2023), 68. [link].
Tasnim Hossain Orpa, Adil Ahnaf, Tareque Bashar Ovi e Mubdiul Islam Rizu. 2022. An iot based healthcare solution with esp32 using machine learning model. Em 2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), 1–6. DOI: 10.1109/STI56238.2022.10103231.
Alexandros Pantelopoulos e Nikolaos G. Bourbakis. 2010. Prognosis—a wearable health-monitoring system for people at risk: methodology and modeling. IEEE Transactions on Information Technology in Biomedicine, 14, 3, 613–621. DOI: 10.1109/TITB.2010.2040085.
Maurício César Pinto Pessoa, Geraldo Braz Junior e Tiago Bonini Borchartt. 2016. Home e-Care: Monitoramento de quedas em idosos através de smartwatches. Em XV Congresso Brasileiro de Informática em Saúde (CBIS). SBIS, Goiânia, Brasil, 265–276. [link].
Sarah Brandão Pinheiro, ALS SILVA, CJ CÁRDENAS e ML SILVA. 2015. A síndrome do pós-queda em idosos que sofrem fratura de fêmur. Cadernos de Estudos e Pesquisas, 19, 41.
Alvin Rajkomar, Jeffrey Dean e Isaac Kohane. 2019. Machine learning in medicine. New England Journal of Medicine, 380, 14, 1347–1358. PMID: 30943338. eprint: [link]. DOI: 10.1056/NEJMra1814259.
Arkham Zahri Rakhman, Lukito Edi Nugroho, Widyawan e Kurnianingsih. 2014. Fall detection system using accelerometer and gyroscope based on smartphone. Em 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering, 99–104. DOI: 10.1109/ICITACEE.2014.7065722.
Ali Akbar Sadri, Amir Masoud Rahmani, Morteza Saberikamarposhti e Mehdi Hosseinzadeh. 2021. Fog data management: a vision, challenges, and future directions. Journal of Network and Computer Applications, 174, 102882. DOI: 10.1016/j.jnca.2020.102882.
Suranga Seneviratne, Yining Hu, Tham Nguyen, Guohao Lan, Sara Khalifa, Kanchana Thilakarathna, Mahbub Hassan e Aruna Seneviratne. 2017. A survey of wearable devices and challenges. IEEE Communications Surveys Tutorials, 19, 4, 2573–2620. DOI: 10.1109/COMST.2017.2731979.
Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li e Lanyu Xu. 2016. Edge computing: vision and challenges. IEEE Internet of Things Journal, 3, 5, 637–646. DOI: 10.1109/JIOT.2016.2579198.
Robert Susmaga. 2004. Confusion matrix visualization. Em Intelligent Information Processing and Web Mining: Proceedings of the International IIS: IIPWM ‘04 Conference held in Zakopane, Poland, May 17–20, 2004. Springer, 107–116.
Chin-Woo Tan e Sungsu Park. 2005. Design of accelerometer-based inertial navigation systems. IEEE Transactions on Instrumentation and Measurement, 54, 6, 2520–2530. DOI: 10.1109/TIM.2005.858129.
Prajoona Valsalan, Tariq Ahmed Barham Baomar e Ali Hussain Omar Baabood. 2020. Iot based health monitoring system. Journal of critical reviews, 7, 4, 739–743.
Waveshare. 2024. Esp32-s3-touch-lcd-1.69. Acesso em: 14 nov. 2024. (2024). [link].
Thomas C. Wong, John G. Webster, Henry J. Montoye e Richard Washburn. 1981. Portable accelerometer device for measuring human energy expenditure. IEEE Transactions on Biomedical Engineering, BME-28, 6, 467–471. DOI: 10.1109/TBME.1981.324820.
A.D. Wood e J.A. Stankovic. 2002. Denial of service in sensor networks. Computer, 35, 10, 54–62. DOI: 10.1109/MC.2002.1039518.
Publicado
19/05/2025
Como Citar
TOLEDO, Vitor Miller de; COLOMBO, Cristiano da Silveira; PIZETTA, Igor Henrique Beloti; CARMO, Lucas Poubel Timm do; GUIMARÃES, Rafael Silva.
Wearable IoT Devices: Feasibility and Effectiveness in Implementing Local Decision-Making for Elderly Fall Detection. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 21. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 389-398.
DOI: https://doi.org/10.5753/sbsi.2025.246513.
