In movement science with inertial sensor many different methodologies resolving specific aspects of movement recognition have been proposed. They are very interesting, and useful, but none of them are generally explicative of what is going on in the semantic sense. When we go down to the movement recognition/classification area (for example in Ambient Intelligence) we do not have a feasible model that can be considered generally predictive or usable for activity recognition. Also, in the field of movement recognition with inertial sensors many technological issues arise: technological diversity, calibration matters, sensor model problems, orientation and position of sensors, and a lot of numerous specificities that, with all the above aspects, and the lack of public dataset of movements sufficiently generic and semantically rich, contribute to create a strong barrier to any approach to a classification matters with wearable sensors. We have also to notice that a movement is a phenomenon explicitly or implicitly (voluntary or involuntary) controlled by brain. The individual free-will introduce a further matter when we want to temporary predict the movements looking at the close past. Pattern can change at any time when ambient, psychological context, age of the subject change. Also, pathological issues, and physiological differences and the will of the subject, introduce important differences. For all these reasons I considered that a semantical /lexical approach to movement recognition with sensors, driven by machine learning techniques could be a promising way to solve some of these challenge and problems. In this Ph.D. Thesis wearable inertial sensors has been used to classify movements, the choice of inertial sensors has been driven by technological and practical advantages, they are cheap, lightweight, and - differently from video cameras - are not prone to the hidden face, or luminance problems. The main idea is to use inertial sensor to understand what a person is doing for ambient-intelligent, healthcare, medical-sport applications. My principal concerns was to propose a method that was not centered on technology issues but on data analysis, that could be a general framework and could also create a general representation of movement,that could be useful also in other area of research, like reasoning. Inertial sensors are treated just as an example, a particular type of sensors, the method is new, reusable, algorithmically simple, net and easy to understand. Accuracy is very high outperforming the best results given in literature, reducing the error rate of 4 times.
(2011). Movements recognition with intelligent multisensor analysis. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2011).
|Data di pubblicazione:||8-feb-2011|
|Titolo:||Movements recognition with intelligent multisensor analysis|
|Settore Scientifico Disciplinare:||INF/01 - INFORMATICA|
|Scuola di dottorato:||Scuola di dottorato di Scienze|
|Corso di dottorato:||INFORMATICA - 22R|
|Citazione:||(2011). Movements recognition with intelligent multisensor analysis. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2011).|
|Parole Chiave:||Inertial Sensors, Intracluster Similarity, Intercluster Similarity, Wireless Sensor Networks, Elderly, Ambient Intelligence, Healthcare, Movement Science, Calibration, Human Profile, Body Profile, Human Computer Interaction, Machine Learning, Principal Component Analysis, Classification, Similarity, Distance, Metrics, WARD 1.0, NIDA 1.0, FFxIVFF, FFxIVF, FF, IVF, HCI, PCA|
|Appare nelle tipologie:||07 - Tesi di dottorato Bicocca post 2009|