On the feature extraction process in machine learning. An experimental study about guided versus non-guided process in falling detection systems

• Published in: Engineering applications of artificial intelligence Vol. 114; p. 105170
• Main Authors: Escobar-Linero, Elena, Luna-Perejón, Francisco, Muñoz-Saavedra, Luis, Sevillano, José Luis, Domínguez-Morales, Manuel
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2022
• Subjects: Deep learning; Discrete wavelet transform; Fall detection; Recurrent neural networks; Deep learning; Recurrent neural networks; Discrete wavelet transform; Fall detection
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2022.105170

Falls are current events that can lead to severe injuries and even accidental deaths among the population, especially the elderly. Since them usually live alone and their contact with other people has decreased since pandemic, recent years studies have focused on automatic fall detection systems with wearable devices using machine learning algorithms. Overall, and according to other works, these systems can be classified as non-guided, if the machine learning model directly uses raw data without feature extraction, or as guided systems, if a previous step of feature extraction is needed to reduce complexity of the algorithm. However, no recommendations are made in the literature on which system could be more advantageous for detecting fall events. Therefore, in this work, a detailed comparison between both types of systems is carried out, using the same process for different machine learning models in order to obtain an accurate classification of activities of daily living, falling risks, and falls. This process includes the optimization of models’ hyperparameters to obtain the best classifiers, followed by an assessment using common evaluation metrics, confusion matrices, ROC curves and execution times. Results show a better classification of models’ three classes for the non-guided models. However, the guided models show more stable metrics and lower computational load. •Two main approaches for ML fall detection classifiers: guided and non-guided systems.•Guided system includes previous feature extraction and NN, non-guided includes an RNN.•Both are analyzed and assessed in equal conditions using AI metrics and time execution.•Similar accuracy results are obtained, but guided system shows a better time response.•Results obtained improves most systems published before and using one more class.