Abstract
Fall Detection (FD) and ADL (Activity of Daily Living) identification is one the main challenges in a lot of real-world problems like work monitoring, healthcare systems, etc. Up to our knowledge, there are a lot of proposals in the literature for both problems separately, but few of them pose both problems at a time. A possible solution relies on-wrist wearable devices including tri-axial accelerometers performing ADL and Fall identification autonomously. Since the dynamics of both kind of activities (FALL and ADL) are quite similar and not easy to identify, mainly in FALL and high ADLs like Running, Jogging, GoUpstairs, etc, a technique considering peaks is suitable. Thus, in this study, an ensemble between KMEANS and KNN (stands for EKMEANS) taking as input a 19 features dataset calculated from a time window whenever a peak is detected. As peak detection algorithm is used, the MAX-PEAKS algorithm presented in [15].
The proposal is evaluated using the UMA Fall, one of the publicly available simulated fall detection data sets, and compared to two classical well-known algorithms: the KNN and a Feed Forward Neural Network (NN) [15].
The results show that our proposal outperforms the NN results.
Future work includes a further analysis of the dynamics of the ensemble EKMEANS and a study of this problem using Deep-Learning.
This research has been funded by the Spanish Ministry of Science and Innovation, under project MINECO-TIN2017-84804-R, and by the Grant FC-GRUPIN-IDI/2018/000226 project from the Asturias Regional Government.
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Notes
- 1.
The default implementation of the R platform “kmeans” function has been used.
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de la Cal, E.A., Fáñez, M., Villar, M., Villar, J.R., Suárez, V. (2020). A Simple Classification Ensemble for ADL and Falls. In: de la Cal, E.A., Villar Flecha, J.R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2020. Lecture Notes in Computer Science(), vol 12344. Springer, Cham. https://doi.org/10.1007/978-3-030-61705-9_9
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