Abstract
One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone’s accelerometers.
Highlights
An inertial navigation system (INS) contains three orthogonal accelerometers and gyroscopes
convolutional neural network (CNN)/Longand short-term memorymemory (LSTM) obtained an accuracy above 91%
When testing on the other six datasets with 100 different recordings, the accuracy was less than 70% for both the accelerometer and gyroscope (Section 3.1) and accelerometer only (Section 3.2)
Summary
An inertial navigation system (INS) contains three orthogonal accelerometers and gyroscopes (inertial sensors). Using the sensors’ measurements and initial conditions, the navigation solution (position, velocity, and attitude) can be calculated. Such a process requires several integrations on the measured vectors. The inertial sensors’ measurements contain noise and other error terms, when integrated, a drift in the navigation solution occurs. To circumvent this drift, INS is commonly fused with other sensors or data, such as global navigation satellite systems (GNSSs) [2]
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