A Multi-Sensor Approach to Optimise Mountain Bike Suspension
Lesauvage, Jeremy (2021)
Lesauvage, Jeremy
2021
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe202103046627
https://urn.fi/URN:NBN:fi-fe202103046627
Tiivistelmä
The Mountain bike sport is an off-road sport, with distinctive aspects, from the family-friendly excursion on the forest roads to the disciplines recognised as extreme, combining obstacles such as jumps, roots, rock gardens and other rough terrains. The suspension is a primordial piece of equipment on the mountain bike to provide dampening, comfort, traction, and efficiency over those cited obstacles. They are designed to be versatile through different adjustments to accustom to any situation, trail, rider’s weight, and preferences.
Even though the process of setting the suspension itself is effortless, it is the users’ responsibility to optimise it, based only on their feelings, since no affordable tools or universal solutions are provided to them. This thesis introduces the design and implementation of a multi-sensor approach to back up or contrasts the user feelings when tuning the suspension.
This design proposes a two-part solution. First, the on-bike electronic, composed of distance sensors to keep track of the fork travel over times, working with an Arduino Mega board, transmitting data through a Bluetooth module to an Android app to save the data. The sensors are paired with a GoPro Hero 7 to provide synchronised video footage as well as another acquisition method. The second part is the computer environment where the sensor’s data will be exported from the Android app and analysed through a Python script, as well as the data of the video footage extracted via a motion analysis software.
One main goal of the thesis is to conclude if we could uniquely adopt a GoPro to optimise the suspension, leading to a competitive price for such a product and even free since most riders already own a GoPro. For this purpose, a comparison with the sensors’ results will be provided and discussed.
The expected results will be easy to understand written suggestions, to lead to an optimised setting, and graphics displaying the following data acquired:
- Shaft compression (mm) / time (ms)
- Shaft velocity (m/s) / time (ms)
From these graphics, we can analyse the four different damping adjustments: low-speed/high-speed compression and rebound, but also if the user’s suspension exploits all its travel, the bottom-out resistance, and its capability to return to SAG value.
Even though the process of setting the suspension itself is effortless, it is the users’ responsibility to optimise it, based only on their feelings, since no affordable tools or universal solutions are provided to them. This thesis introduces the design and implementation of a multi-sensor approach to back up or contrasts the user feelings when tuning the suspension.
This design proposes a two-part solution. First, the on-bike electronic, composed of distance sensors to keep track of the fork travel over times, working with an Arduino Mega board, transmitting data through a Bluetooth module to an Android app to save the data. The sensors are paired with a GoPro Hero 7 to provide synchronised video footage as well as another acquisition method. The second part is the computer environment where the sensor’s data will be exported from the Android app and analysed through a Python script, as well as the data of the video footage extracted via a motion analysis software.
One main goal of the thesis is to conclude if we could uniquely adopt a GoPro to optimise the suspension, leading to a competitive price for such a product and even free since most riders already own a GoPro. For this purpose, a comparison with the sensors’ results will be provided and discussed.
The expected results will be easy to understand written suggestions, to lead to an optimised setting, and graphics displaying the following data acquired:
- Shaft compression (mm) / time (ms)
- Shaft velocity (m/s) / time (ms)
From these graphics, we can analyse the four different damping adjustments: low-speed/high-speed compression and rebound, but also if the user’s suspension exploits all its travel, the bottom-out resistance, and its capability to return to SAG value.