As motion sensors evolve, the number of applications of tracking patterns of human motion has dramatically increased. The appeal of motion sensors includes their small size, light weight, low power consumption, and low price (increasing the accessibility of the technology). However, the measurements of low cost MEMS inertial sensors contain errors that accumulate heavily over time if used alone. This paper presents an innovative way to use the raw acceleration and the angular rates derived from motion sensors to mitigate these errors for cycling applications, thereby improving cycling navigation. Additionally, this paper will show how parameters can be obtained for analyzing the performance of the cyclist. Based on repeatable human motions during cycling, assisting models are derived to improve the navigation solution, especially for GNSS-denied environments. These models establish the relationship between forward speed, traveled distance, and pedaling frequency. In addition, they are built online when GNSS signals are available. By incorporating self-contained sensors, the forward speed, and traveled distance (the latter two as derived from predictable human motion); an improved navigation solution for cycling can be achieved. The performance of different aiding algorithms is compared in this paper.
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Space and Planetary Science