Achieving an accurate navigation solution for cycling applications using portable devices without applying constraints, is very challenging. It is especially difficult during times where absolute navigation information, such as from Global Navigation Satellite Systems (GNSS) is unavailable. There are three main challenges that arise when trying to find a solution.) The first challenge is the device containing the sensors is not tethered to the moving platform, but rather moves with respect to the moving platform (here a bicycle). This means the device could be on the body of the cyclist and undergo any type of motion dynamics as well as vibrations. Secondly, the device and consequently the frame of the sensors inside, can be in any orientation with respect to the direction of motion of the cycling platform. This relative orientation (defined as misalignment between the device and platform or bicycle) can change at any time. The final major challenge is the error characteristics of the used low-cost inertial sensors lead to an increase in position errors during the unavailability of absolute navigation information from GNSS. This paper presents a solution that provides accurate, continuous, and real-time navigation for portable devices in cycling applications. The proposed solution utilizes new techniques and methods to overcome the above-mentioned challenges. This solution does not rely purely on the Inertial Navigation System (INS) when absolute navigation updates are unavailable but uses Cycling Dead Reckoning (CDR) to update the INS solution. During GNSS availability, models for estimating speed from cycling frequency and distance travelled from the detected cycles are obtained. During GNSS outages, these models are used to provide velocity and position updates to the INS solution. When there is no pedaling motion, dynamics CDR is not used. To contribute to the INS solution in such scenarios, Non-Holonomic Constraints (NHC) are used. The proposed solution also includes an extension of CDR, called multi-gear CDR (MG-CDR) that can handle bicycles with multiple gears. When GNSS is available, the MG-CDR builds a group of models for different gear ratios. When GNSS signals are lost, the system runs a routine to detect the most likely gear ratio. These corresponding models, derived by matching or interpolation/extrapolation of the results of existing models, are used. To run CDR, MG-CDR and NHC, 3D misalignments are needed because both speed and distance traveled are in the bicycle frame, not in the device frame (INS frame). The proposed solution includes routines to calculate the 3D misalignments, whether in the presence or absence of GNSS. The proposed real-time navigation solution was tested extensively in many trajectories collected by different users, on different bicycles, including both multi-gear and single-gear bicycles. The experiments included multiple different positions and orientations of the portable devices on the cyclists’ body. Different smartphones, tablets, and smartwatches were used in the experiments. The results presented demonstrate the capabilities and competitiveness of the proposed solution in the various real-life scenarios discussed.

This paper presents techniques that use raw accelerations and angular rates obtained from inertial sensors to improve cycling navigation. The main aim of the paper is the estimation of three dimensional (3D) misalignments between the device frame and the bicycle frame, which is necessary so that the portable device can be used in any orientation without constraints. The focus is on developing techniques to calculate roll, pitch, and heading misalignments. These 3D misalignments enable the calculation of the transformation matrix between the device frame and the bicycle frame. This information is important for cycling navigation using a portable device in two respects. The first is that it enables the application Non-Holonomic Constraints (NHC), which limits velocity error since a moving platform cannot skid or jump. NHC are in the bicycle frame and thus the transformation between device and bicycle frames is needed to apply NHC. The second reason this is important is that the transformation matrix and the misalignment enable Cycling Dead-Reckoning (CDR). CDR is based on using models for speed and distance traveled per cycle as a function of frequency and cycle detection; these models are obtained during GNSS availability and are utilized when GNSS becomes unavailable. As the sensors in the devices are able to obtain the device heading, heading misalignment between the device and bicycle is necessary to obtain the bicycle heading and consequently for calculating positions using dead reckoning. Furthermore, the speed update needs the transformation matrix between device and platform frame. The above highlights the importance of calculating the misalignment between the device and bicycle frames, as well as the transformation matrix between them and how this can be exploited in obtaining an improved navigation solution for cycling activities using portable devices. This paper uses a unit that integrates accelerometers, gyroscopes, magnetometers, barometers, and GNSS receiver data. This navigation solution is intended to achieve real-time tracking and monitoring of the user’s performance as well as offline analysis and assessment of their performance. To analyze the proposed methods, several real-life cycling experiments were conducted. To verify the performance in different locations and orientations, three different units comprising of the integrated system were mounted on the upper back, thigh, and leg. The results demonstrate that the proposed methods can estimate 3D misalignments between the device frame and the platform frame. This further demonstrates that the device may be used in any orientation with respect to the platform. Additionally, it helps improve the navigation solution by incorporating NHC and/or CDR, which significantly reduces the position error, especially in GNSS-denied environments. In conclusion, this paper demonstrates the proposed techniques effectiveness in estimating 3D misalignments and deriving additional position and velocity measurements for cycling applications. The proposed system has proven to be an accurate, portable, and inexpensive integrated navigation system to provide a robust and accurate positioning solution. In addition to being able to accurately track the motion of the cyclist, the proposed system can also monitor the cyclist’s instantaneous correct acceleration and turning rate. The fact that the whole architecture of this system can be embedded in a widely available device such as a smartphone, makes it a very attractive solution.

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