For this part of the project I am looking at different types of sensors that we can use as a group to detect body positioning on the bike.
The main purpose of this research is to find out which sensors will give use the most accurate data so we can determine which position the user is in.
The following are the sensors I found to be the most fitting:
Capacitive
Ultrasonic proximity
Ambient light sensors
Capacitive
Capacitive sensors are a great option to use for this type of application. They are cheap to make (have a do-it-yourself version that we can try and create for testing purposes), fairly reliable, and do not have many moving parts to potentially fail.
How they are made
Therefore, from this drawing, we can modify several components to create a body part detector. If we remove the bottom plate and insulate the entire top plate, then we would be able to mount this onto the bike and detect if the user is near that particular section on the bike, i.e handlebars, pedals, seat, and crossbar.
This type of sensor still has limitations that we need to consider. It is not very directional, meaning if a knee is placed near if from the user whilst cycling, it could cause erroneous readings. We could try and manage these but placing a shield of some sort in certain directions but this is again more investigations that we would have to do.
Ultrasonic proximity
These types of sensors are very cheap and readily available which means we could try and implement them and see which type of results we get without a lot of costs incurred and or time spent.
A downside to this type of sensor is that it is rather sensitive and might create a situation where a user momentarily moves out of the way or leans forwards which will return bad readings. This is again avoidable with some data handling but it will make us to do more work to implement it.
This type of sensor is only good for certain positions as it is not possible to have it mounted on the handlebars to detect if the user is holding onto it. It is too big to make the user hold onto it and therefore will only work mounted on particular spots on the bike.
Ambient light sensors
The ambient light sensors are compact units that can accurately detect light levels nearby. This is excellent for many different applications but is not directly designed for our needs. However, it can detect accurately if a person gets closer to it as the light levels will reduce and therefore indicate that via the results. This can be translated into our project nicely as it is small and inconspicuous enough to place on the seat, handlebar and crossbar without being invasive to the user.
A downside to this type of sensor is that in some weather it can give false readings and therefore not be ideal for an outdoor project.
Conclusion
As an overall look at the three selected sensors to implement into our project, the capacitive sensor seems to have the most flexibility to offer. We can make it ourselves from simple materials, it has the physical flexibility to be mounted in many shapes and around objects as we require and gives fairly reliable output data.
The next stage will be to create several capacitive sensors and test out the reliability of homemade sensors verse ready-made store-bought ones.
Sensor types
For this part of the project I am looking at different types of sensors that we can use as a group to detect body positioning on the bike. The main purpose of this research is to find out which sensors will give use the most accurate data so we can determine which position the user is in. The following are the sensors I found to be the most fitting:
Capacitive Capacitive sensors are a great option to use for this type of application. They are cheap to make (have a do-it-yourself version that we can try and create for testing purposes), fairly reliable, and do not have many moving parts to potentially fail. How they are made
Therefore, from this drawing, we can modify several components to create a body part detector. If we remove the bottom plate and insulate the entire top plate, then we would be able to mount this onto the bike and detect if the user is near that particular section on the bike, i.e handlebars, pedals, seat, and crossbar.
This type of sensor still has limitations that we need to consider. It is not very directional, meaning if a knee is placed near if from the user whilst cycling, it could cause erroneous readings. We could try and manage these but placing a shield of some sort in certain directions but this is again more investigations that we would have to do.
Ultrasonic proximity
These types of sensors are very cheap and readily available which means we could try and implement them and see which type of results we get without a lot of costs incurred and or time spent.
A downside to this type of sensor is that it is rather sensitive and might create a situation where a user momentarily moves out of the way or leans forwards which will return bad readings. This is again avoidable with some data handling but it will make us to do more work to implement it.
This type of sensor is only good for certain positions as it is not possible to have it mounted on the handlebars to detect if the user is holding onto it. It is too big to make the user hold onto it and therefore will only work mounted on particular spots on the bike.
Ambient light sensors
The ambient light sensors are compact units that can accurately detect light levels nearby. This is excellent for many different applications but is not directly designed for our needs. However, it can detect accurately if a person gets closer to it as the light levels will reduce and therefore indicate that via the results. This can be translated into our project nicely as it is small and inconspicuous enough to place on the seat, handlebar and crossbar without being invasive to the user.
A downside to this type of sensor is that in some weather it can give false readings and therefore not be ideal for an outdoor project.
Conclusion
As an overall look at the three selected sensors to implement into our project, the capacitive sensor seems to have the most flexibility to offer. We can make it ourselves from simple materials, it has the physical flexibility to be mounted in many shapes and around objects as we require and gives fairly reliable output data.
The next stage will be to create several capacitive sensors and test out the reliability of homemade sensors verse ready-made store-bought ones.