A Hidden Markov Model Based Sensor Fusion Approach for Recognizing Continuous Human Grasping Sequences (Bernardin, et al – 2005)

Current Mood: studious

Blogs I Commented On:

• Pankaj Rajan
• Aaron Wolin
• Brandon Paulson

Summary:
This paper discusses a system based on HMMs for recognizing hand grasps. Classification follows grasp types from Kamakura’s grasp taxonomy, separating grasps into 14 different classes by purpose, hand shape, and contact points with grasped objects. Each HMM is assigned to a different grasp type, and recognition is performed using the Viterbi algorithm. The focus of their system differs from existing ones in that it distinguishes between the purpose of grasps, as opposed to the object shape or number of fingers. Their glove-based device is equipped with flexible capacitive sensors to measure sensor readings for grasping. Noise and unwanted motion was filtered out in a garbage model with ergodic topology, and a ‘task’ grammar was used to reduce the search space. Their only assumption was that a grasp motion is followed up by a release motion. Their system is able to achieve 90% for multiple users.

Discussion:
The recognition system discussed in this paper is a lot different from the other types of systems discussed in prior papers because none of the papers tackled the problem of recognizing grasping. I liked the paper because it was different and covers an area overlooked in the hand gesture recognition domain. The use of grasp as a feature is very intriguing, and I believe that incorporating it in a recognition system would make such a system more powerful. Reminds me of the hand tension paper, now that I think about it. It does seem like extracting grasping data is a non-trivial affair.