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A complete scheme for motion prediction based on motion capture data is presented. The scheme rests on three main components: a special posture representation, a diverse motion capture database and prediction method. Most prior motion prediction schemes have been based on posture representations based on well-known local or global angles. Difficulties have arisen when trying to satisfy constraints, such as placing a hand on a target or scaling the posture for a subject of different stature. Inverse kinematic methods based on such angles require optimization that become increasingly complex and computationally intensive for longer linkages. A different representation called stretch pivot coordinates is presented that avoids these difficulties. The representation allows for easy rescaling for stature and other linkage length variations and satisfaction of endpoint constraints, all without optimization allowing for rapid real time use.

Motion prediction models may give the average reach for an individual of specified characteristics. The actual reach will vary from this reach in a manner that may depend on both systematic and random factors. We describe a modeling approach that incorporates the variability within the reaches of a given subject and that between subjects. This information is useful to designers in investigating phenomena that may not occur during the average reach but may occur during variants such as collision with an obstacle or injury due to over-exertion.