Classification of Human Postural and Gestural Movements Using Center of Pressure Parameters Derived From Force Platforms

The human body, while standing, can be imagined as a complex feedback system that produces continuous sway patterns. Subtle body movements that can be caused by sensory cues such as visual or auditory, affective, cognitive, pathological or many other factors besides intended movements can be easily captured in the sway patterns derived from ground reaction forces and the body's center of pressure (COP). The purpose of this research is to classify human body movements, even the subtle movements, using a carefully selected feature set. For the first time, we propose a method to classify postural and gestural movements using data from force platforms collected from participants performing 11 choreographed movements. Twenty-three different displacement and frequency based features were initially extracted from COP time series, and ranking and wrapper methods were used for classification-guided feature extraction. Linear classifiers such as Fisher's Linear Discriminant analysis classifier and nonlinear classifiers such as nearest neighbor classifiers, support vector machines (SVM), and neural networks were explored and successfully applied to the aforementioned movement classification. The average classification rates on test sets ranged from approximately 79% to 92%. All the methods proposed in this experiment performed well by themselves over at least one movement type, but none could outperform the others for all movement types and therefore a set of movement-specific features and classifiers is proposed.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical Engineering (UMKC)

Grantor dc:publisher

University of Missouri--Kansas City

Year dc:date.issued

2010

Author dc:creator

• Saripalle, Sashi K. (Sashi Kanth)

Advisor dc:contributor.advisor

• Derakhshani, Reza

• Pressure measurement

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10355/9613

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/9613