TFG: Development of a system for motion analysis
Posted on by b105

Obtaining information about the motion of an object has many applications in today’s society. Large industries such as cinema or videogames use motion capture technologies for their development. Motion capture systems collect the information that allows to know the acceleration, speed, orientation and position of an object.

The development of MicroElectroMechanical Systems or MEMS by the end of the 1980s has increased the use of accelerometers and gyroscopes to increase motion capture. That led to the development of Inertial Measurement Units with a small size, resulting from the combination of accelerometers and gyroscopes. This miniaturisation enabled the use in other applications, like augmented reality, 3D animation, navigation, video games and sports . Another of its features that stands out is that it does not need an external reference to be used, resulting in a simpler implementation.

In this graduate thesis, a system has been developed that can collect the data generated by an IMU, store it and then dump it into another system for analysis. Some criteria were needed to be established, so the design is focused on been small and low power consumption. For the development of the system, a hardware design was made, followed by the implementation of the software. Finally, some test were made to evaluate the final result.

TFM: Design strategies for detecting action potentials in actions based on movements
Posted on by anacp

This work is located in the studies of the brain and their signals. The puspose is to know when someone wants to make a movement. Thus, it might help to people that actually are not able to move a member of their body or more. Mainly, it is focused in the design of strategies for detection of action potentials or spikes when a movement wants to be made. This study is not looking for action potentials form, it is looking for patterns and characteristics that allow to recognize the movement. Although there are action potentials covered by the signals taken from the electrodes, but they are unavailable.

To accomplish the objective, it is used the EEG signals of a public data base. It is selected the ones related to the movement of the hands, concretely, the movement of open and close the fist. Signal sources of noise that dirty the signal are analyzed, they are called artifacts, and then, filtering stage comes, giving the signals of below for movement and no movement.

slotMov15slotRest15

Now, possible algorithms are checked. It is decided to use the Wavelet transform and the way in which it obtains the energy of the signal. Thanks to the calculation of Wavelet energy in 22 subjects, it is reached to the conclusion that Wavelet energy for movement is higher than for no movement. So, electrodes that comply with this condition at 100% are 4.

The final algorithm is implemented three features: correlation, a parameter that gives a relation between two signals, their energy range and their energy average. It could be said that algorithm has two parts: a training stage and a decision stage. Inside decision part, there are three algorithms: ProMove, ProMove + improve and Logic. The basic difference among ProMove and Logic is an or (||) and an and (&). The improve is based on empiric knowledge.

commonstages

 

systemcomplete

Final conclusions show that the signals between subjects are very changing. Therefore, same algorithm is not useful for everybody. To some subjects, the successful probability is very high (92,86% – 1 fail), while for others is more low than what is expected (50% – 7 fail). With these test, the importance in the length of the signals is reflected, because if signals for subjects with more than 3 fails are inversely processed, the fails are reduced. The most useful algorithm for a larger number of subjects is ProMove + improve.