Summary:
Bernardin et al. created a system to recognize human grasping gestures using a CyberGlove and pressure sensor data. Basic grasps are distinguished in 14 different ways by Kamakura's grasping primitives. These grasps include "5 power grasps, 4 intermediate grasps, 4 precision grasps, and one thumbless grasp".
To recognize these grasps, an 18 sensor CyberGlove is used, along with finger tip and palm sensors. 14 different pressure sensors are sewn into a glove, which is worn under the CyberGlove. The sensor data is passed into HMMs for recognition. A 9-state HMM is built for each gesture using the HTK. After each grasp, the grasped object must be released.
On a total of 112 training gestures from 4 users, the user dependent models were between 77 and 92%, whereas a user independent model was in the low 90s for all 4 users. This is most likely due to the increase in training data when all the user data is combined.
Discussion:
I thought the use of HMMs in this paper was actually quite good. The problem I have with HMMs is that they are absolutely horrible and explode when data is not properly segmented. In the case of grasps, it is probably less likely that somebody is going to go from 1 grasp to another without releasing the object they are holding. For most, general cases, the computer can assume that the lack of tactile input from the palm would indicate a grasp has ended.
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1 comment:
This paper feels, in addition to the hand tension paper, would make excellent cases for adding grasping as viable feature candidates for gesture segmentation. Sounds like using HMMs is not the way to go, but the theory itself is intriguing. It still sounds tricky to me in extracting that kind of feature from the input devices that are in the lab. I guess I'm not thinking hard enough about it.
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