Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users

Abstract

Microelectromechanical Systems (MEMS) technology is playing a key role in the design of the new generation of smartphones. Thanks to their reduced size, reduced power consumption, MEMS sensors can be embedded in above mobile devices for increasing their functionalities. However, MEMS cannot allow accurate autonomous location without external updates, e.g., from GPS signals, since their signals are degraded by various errors. When these sensors are fixed on the user's foot, the stance phases of the foot can easily be determined and periodic Zero velocity UPdaTes (ZUPTs) are performed to bound the position error. When the sensor is in the hand, the situation becomes much more complex. First of all, the hand motion can be decoupled from the general motion of the user. Second, the characteristics of the inertial signals can differ depending on the carrying modes. Therefore, algorithms for characterizing the gait cycle of a pedestrian using a handheld device have been developed. A classifier able to detect motion modes typical for mobile phone users has been designed and implemented. According to the detected motion mode, adaptive step detection algorithms are applied. Success of the step detection process is found to be higher than 97% in all motion modes.

A New PDR Navigation Device for Challenging Urban Environments

“…Activity Classification. ULISS raw data are first processed to classify the pedestrian locomotion state and the handheld device carrying mode [13][14][15]. An outcome of the classification outcome is shown in Figure 7.…”

IMU dataset for motion and device mode classification

“…For example, [14] applies a decision tree for classification purposes while [12] uses multi-layer perceptron (MLP) and a support vector machine (SVM) to improve the performance in terms of recognition of human activity.…”

“…The classifier introduced in [13] is based on accelerations and magnetic field data recorded with a hand-held unit. Another study dealing with different motion models and device modes is performed and reported in [14], where standing still and walking patterns are studied. An extended investigation is to add also the running mode as in [12].…”

IMU and magnetometer modeling for smartphone-based PDR

“…For example, in [8], step detection and heading estimation algorithms are developed and combined with existing step length models to build a PDR system. A more advanced solution that takes different user behavior and device carrying situations into account is developed in [9]. Furthermore, simple filtering-and integration algorithms have been considered in [10] for different measurement setups.…”