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.
Highlights
A consequence of the booming market of smartphones and other types of Personal Digital Assistant (PDAs) is that the possibility of using mobile devices for locating a person is becoming more and more attractive for many applications
If a sample in the window experiences a bigger value than the evaluated mean, a peak is identified In the upper part of Figure 8, the norm of the gyroscope signal recorded by the Inertial Measurement Unit (IMU) in the swinging hand of the user is reported and the bottom part shows the norm of the acceleration signals recorded on the foot
Algorithms for characterizing the gait of pedestrian using accelerometer and gyroscope signals recorded in a handheld device have been developed and presented
Summary
A consequence of the booming market of smartphones and other types of Personal Digital Assistant (PDAs) is that the possibility of using mobile devices for locating a person is becoming more and more attractive for many applications. The trend is evolving from applications dedicated to professional market to the consumer market This explains the increasing interest for research in handheld based positioning for Location Based Services (LBS) as it could broaden the offer of services based on user’s locations. In urban canyons and in other challenging surroundings the availability of satellite signals cannot be guaranteed and GNSS based services can be highly degraded or totally denied. In these cases, micro-electromechanical systems (MEMS), such as accelerometers and gyroscopes, can aid the geolocation process. PDR algorithms compute the travelled distance by detecting the user’s steps and determining their length
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