Fall detection system for elderly people using IoT and ensemble machine learning algorithm

  • Diana YacchiremaEmail author
  • Jara Suárez de Puga
  • Carlos Palau
  • Manuel Esteve
Original Article


Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.


Fall detection Internet of Things 6LowPAN IoT gateway Ensemble learning algorithm Random Forest Accelerometer sensor Elderly people 


Funding information

Research presented in this article has been partially funded by Horizon 2020 European Project grant INTER-IoT no. 687283, ACTIVAGE project under grant agreement no. 732679, the Escuela Politécnica Nacional, Ecuador, and Secretaría de Educación Superior Ciencia, Tecnología e Innovación (SENESCYT), Ecuador.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Escuela Politécnica NacionalQuitoEcuador
  2. 2.Universitat Politècnica de ValènciaValenciaSpain

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