Deep Multi Temporal Scale Networks for Human Motion Analysis

Author:

D'AMATO, VINCENZO STEFANO [claim]

Description:

The movement of human beings appears to respond to a complex motor system that contains signals at different hierarchical levels. For example, an action such as ``grasping a glass on a table'' represents a high-level action, but to perform this task, the body needs several motor inputs that include the activation of different joints of the body (shoulder, arm, hand, fingers, etc.). Each of these different joints/muscles have a different size, responsiveness, and precision with a complex non-linearly stratified temporal dimension where every muscle has its temporal scale. Parts such as the fingers responds much faster to brain input than more voluminous body parts such as the shoulder. The cooperation we have when we perform an action produces smooth, effective, and expressive movement in a complex multiple temporal scale cognitive task. Following this layered structure, the human body can be described as a kinematic tree, consisting of joints connected. Although it is nowadays well known that human movement and its perception are characterised by multiple temporal scales, very few works in the literature are focused on studying this particular property. In this thesis, we will focus on the analysis of human movement using data-driven techniques. In particular, we will focus on the non-verbal aspects of human movement, with an emphasis on full-body movements. The data-driven methods can interpret the information in the data by searching for rules, associations or patterns that can represent the relationships between input (e.g. the human action acquired with sensors) and output (e.g. the type of action performed). Furthermore, these models may represent a new research frontier as they can analyse large masses of data and focus on aspects that even an expert user might miss. The literature on data-driven models proposes two families of methods that can process time series and human movement. The first family, called shallow models, extract features from the time series that can help the learning algorithm find ...

Publisher:

Università degli studi di Genova

Contributors:

D'Amato, VINCENZO STEFANO ; ONETO, LUCA ; CAMURRI, ANTONIO

Year of Publication:

2023-02-06

Document Type:

info:eu-repo/semantics/doctoralThesis ; [Doctoral and postdoctoral thesis]

Language:

eng

Subjects:

Movement Technology ; Music Education ; Music Learning Technology ; Multimodal Interactive System ; Motion Capture ; Electromyography ; Affective Movement Recognition Challenge 2021 ; Protective Behaviour Detection ; Detection of Reflective Thinking ; Detection of Lightness and Fragility ; Body Movement ; Feature Engineering ; Random Forests ; XGBoost ; Model Selection ; Model Estimation ; Kinect ; Machine Learning ; Feature Ranking ; Motion Recognition ; Multiple Temporal Scales ; Shallow Learning ; Deep Learning ; Feature Learning ; Attention Maps ; Open Data ; Open Implementation ; Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni

DDC:

004 Data processing & computer science (computed)

Rights:

info:eu-repo/semantics/openAccess

Relations:

https://hdl.handle.net/11567/1104759

URL:

https://hdl.handle.net/11567/1104759
https://doi.org/10.15167/d-amato-vincenzo-stefano_phd2023-02-06

Content Provider:

Università degli Studi di Genova: CINECA IRIS Flag of Italy