A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

Bernardin et al add hand mounted pressure sensors to the CyberGlove to determine grasps. These sensors are mounted at various positions on the hand that contact grasped objects. The CyberGlove+Pressure data is input into a set of HMMs. Single user trained systems perform between 75-92% accuracy, while multiple user trained systems perform at 90% on average.

Discussion
Not much to say. Pressure seems like a very useful piece of information for determining contact between the hand and objects.

Reference
Bernardin, K., K. Ogawara, et al. (2005). "A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models." Robotics, IEEE Transactions on [see also Robotics and Automation, IEEE Transactions on] 21(1): 47-57