Towards an Ecological Pedagogy: Body Movement for Ecological Consciousness

This paper examines the moving body as a vehicle for raising ecological consciousness. Due to the modern over-preoccupation with the pursuit of rational aims, human interactions with the surrounding environment increasingly lack conscious awareness. Consequently, in the modern world people tend to lack an ecological consciousness. Nevertheless, the human body is a rich reservoir of ecological significance. From birth, humans are woven into tremendous interconnection with the world. However, humans thrive when their sensitivity to the physical world exists in harmony with their ability to pursue their rational aims. It is the combination of these characteristics that enables humans to survive in capricious surroundings and prosper in a wide array of contexts. Today, the human species faces an unprecedented crisis that threatens to collapse the reciprocality of the ecological bonds bolstering the prosperity of all worldly beings. This paper proposes that it is no longer a rational strategy for people to remain inattentive to their embodied ecological resonance, and that the moving body is an adequate pedagogical site for raising ecological consciousness. Ritualized body movements derived from Chinese traditional cultivation systems such as Taijiquan could orient practitioners to reestablish a perceptual intimacy with the larger cosmic world, thereby raising their ecological consciousness.

The application of PCA as a movement pattern recognition technique: A proof of principle

Quantitative methods can help us understand how underlying attributes contribute to movement patterns. Applying principal components analysis (PCA) to whole-body motion data may provide an objective data-driven method to identify unique and statistically important movement patterns. Therefore, the primary purpose of this study was to determine if athletes’ movement patterns can be differentiated based on skill level or sport played using PCA. Motion capture data from 542 athletes performing three sport-screening movements (i.e. bird-dog, drop jump, T-balance) were analyzed. A PCA-based pattern recognition technique was used to analyze the data. Prior to analyzing the effects of skill level or sport on movement patterns, methodological considerations related to motion analysis reference coordinate system were assessed. All analyses were addressed as case-studies. For the first case study, referencing motion data to a global (lab-based) coordinate system compared to a local (segment-based) coordinate system affected the ability to interpret important movement features. Furthermore, for the second case study, where the interpretability of PCs was assessed when data were referenced to a stationary versus a moving segment-based coordinate system, PCs were more interpretable when data were referenced to a stationary coordinate system for both the bird-dog and T-balance task. As a result of the findings from case study 1 and 2, only stationary segment-based coordinate systems were used in cases 3 and 4. During the bird-dog task, elite athletes had significantly lower scores compared to recreational athletes for principal component (PC) 1. For the T-balance movement, elite athletes had significantly lower scores compared to recreational athletes for PC 2. In both analyses the lower scores in elite athletes represented a greater range of motion. Finally, case study 4 reported differences in athletes’ movement patterns who competed in different sports, and significant differences in technique were detected during the bird-dog task. Through these case studies, this thesis highlights the feasibility of applying PCA as a movement pattern recognition technique in athletes. Future research can build on this proof-of-principle work to develop robust quantitative methods to help us better understand how underlying attributes (e.g. height, sex, ability, injury history, training type) contribute to performance.