Thanks to the evolution of motion sensors, it no longer takes a full laboratory to assess human motion. Inertial sensors (e.g. accelerometers and gyroscopes), barometers and magnetometers have made it possible to easily assess without the need for hard to access equipment. With the recent advances in Micro-Electro-Mechanical System (MEMS), MEMS-based sensors can be easily incorporated in small portable devices, such as watches, goggles, shoes, belts, smartphones, or custom-built devices. MEMs-based inertial sensors have become the popular choice for monitoring human motion in sport applications, due to their low cost, small size, and low power consumption. The extra measurements and constraints that can be derived from this human motion data can be used to improve athlete coaching, as well as enhanced navigation solutions for sports such as cycling. Inertial sensors are self-contained systems not dependent on the transmission or reception of signals from an external source, thereby minimizing problems such as signal blockage, jamming, and multipath caused by environmental factors. The device’s relative position, velocity and attitude are further derived through integration of raw measurements. The device is generally located within or tethered to the platform. The measurements taken from the sensors need to be transformed from device’s computational space to the platform’s computational space. This is typically achieved through a precise mounting and proper alignment process. However, for portable devices, which tend to move in a constrained or unconstrained way within the platform, careful mounting is not an option. Therefore, it is critical to have techniques to determine the transformation matrix between the platform’s frame and the device’s frame. MEMS inertial sensors can provide high data rate acceleration and angular rate measurements. However, since the MEMS inertial sensors found in consumer portable devices are very low-cost and intended for entertainment and short-term applications, they are not suitable for long-term applications such as navigation. They would require absolute updates due to the eventual large accumulation of sensor errors. Even with calibration and compensation, the navigation solutions for long-term applications deteriorate over time because these errors enlarge significantly due to mathematical integration operations. It is important to employ other absolute measurements to reduce accumulation of errors for navigation and other long-term purposes. Global Positioning System (GPS) calculates the user’s position and velocity through trilateration techniques, providing relatively accurate position and velocity information when in open sky conditions. The integration of GPS/MEMS sensors is already commonly used to monitor the movements of the human body. Sports such a cycling, rowing, car racing, and winter sports can benefit from the use of the GPS/MEMS sensors integration, as the development continues, and the availability of wearable devices increases.
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.