Predicting user's movement path in indoor environments using the stacked deep learning method and the fuzzy soft‐max classifier

Abstract

Abstract Accurate prediction of a user's movement path has various advantages for many applications, such as optimising a nurse's trajectory in a hospital and assisting elderly or disabled people and making them feel secure and protected in the places where they live. Recently, researchers have suggested techniques based on machine learning and deep learning in this field. However, these approaches have drawbacks such as their low accuracy in classifying the extracted features into associated movement paths, high sensitivity to noisy data, and ignoring time dependencies within raw data. In this work, a three‐phase stacked method named CNN‐LSTM‐FSC is proposed, which uses the Convolutional Neural Network (CNN), Long Short‐Term Memory (LSTM), and Fuzzy Soft‐max Classifier (FSC) to overcome the mentioned constraints. In the first phase, the CNN structure extracts time dependencies within raw data using the stacked convolutional and pooling layer. In the second phase, the long‐term time dependency of the user's movement path is learnt using the LSTM layers, and the user path is determined using a new innovated fuzzy soft‐max classifier. Finally, in the post‐processing phase, by performing a majority voting technique on the k‐adjacent sample predictions of the classifier, the authors have tried to reduce the effects of noise in identifying the user's movement path. Experiments were conducted on the MovementAAL_RSS dataset. The proposed method has successfully reached 93.86%, 93.71%, and 93.26% accuracy rate on the MovementAAL_RSS datasets, with 0%, 5%, and 10% Gaussian noise, respectively, and demonstrates superior results in comparison to the previous literature research.

Keywords

• convolutional neural network
• fuzzy soft‐max classifier
• long short‐term memory
• user's movement path