Action Tracer with Andriod API
Supervised by Associate Proffessor Winberg
Abstract
This dissertation aims to present the design and testing of a low-cost motion capture device with feedback capabilities. In addition to this, an API to communicate with this system will be developed that allows devices to connect to the motion capture system and execute commands on it.
This system, Action Tracer, would be useful in cases where a user may need to train or learn a repetitive motion found in an activity or sport. Such a task needs to be done under supervision and may need to be done away from a coach or therapist. In either situation, a system which can accommodate both cases while providing insights that are not visible to the eye is needed. In addition to this, the system would need to be configurable for the various sports that it may be used with, as well as be operated from an Android device.
Most of the work that will be presented in this report will relate to the development of marker based motion capture systems, with a particular interest in the use of Inertial Measurement Units (IMUs). In addition to this, the subject of Biofeedback will be covered, as well as how it relates to motion capture and how it can be applied in this field.
In order to build this system, first various figures of merit were obtained from multiple motion capture systems which are in use by way of exploring literature in the field. This will be followed by expanding the obtained requirements into design specifications for the subsystems that will be identified as essential. These specifications will form the foundational building blocks for the system, and they will be put together to form the entire system.
In order to evaluate this system, it will be compared to a markerless camera based motion capture system by observing joint rotations at the elbow and wrist. This will lead to analysing the profiles obtained from multiple participants, and will be followed by analysing the variations in the motions performed for each system. The analysis will lead to obtaining an accuracy, precision, and range for the developed system.
Finally, the results of the comparisons between these two systems will be provided and will show that the developed system can keep, on average, within 5% of the markerless system with a precision of 10%
To use less words, this project was about creating a motion capture system which could be used in sports and rehabilitation for as little money as possible. It used the cheapest IMU on the market at the time of research, as well as a Raspberry Pi Zero W. This was compared to another technique using a single camera.
There is still a lot I’d love to do with this project and updates may follow one day in a separate project, but for now this was what I managed to do.
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