COMMENT
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SUMMARY
This paper talks about identifying activities thorugh hand posture recognition. Paper's goal is to find out whether hand posture is a useful indicator for recognizing activities. It also tries to find out whether the hand postures can be generalized independent of the users.
Authors use a single right handed cyberglove with 22 censors reading the abduction and flexion angles of different joints in the hand. The paper uses a simple 1-nearest neighbor method to test hand posture recognition. The data for hand postures involved in activities was collected from 8 users while they were naturally performing activities around an office desk for e.g. Dialing a telephone, Using a mouse, reading a piece of paper etc. To test whether hand postures could be associated with correct activities independent of which user the recognizer was trained on, the authors performed a leave one out cross validation across all 8 users. The average accuracy in this case was 62.5%. For user dependent training and testing the accuracy obtained was 94.2%. This lead the authors to conclude that hand posture can indeed be used as a cue to perform activity based recognition. However, a user dependent trained system performs much better. A user independent system performs poorly due to high variation in how users carry out tasks.
DISCUSSION
The paper provides a good insight into how hand postures can inform activity recognition. It would be interesting to see if the method performs better when combined with vision techniques. For example its seems that the ambiguity in recognizing 'picking up a pape'r and 'picking up earphones' could be resolved if the system could see the 'object'..
2.
SUMMARY
This paper talks about identifying activities thorugh hand posture recognition. Paper's goal is to find out whether hand posture is a useful indicator for recognizing activities. It also tries to find out whether the hand postures can be generalized independent of the users.
Authors use a single right handed cyberglove with 22 censors reading the abduction and flexion angles of different joints in the hand. The paper uses a simple 1-nearest neighbor method to test hand posture recognition. The data for hand postures involved in activities was collected from 8 users while they were naturally performing activities around an office desk for e.g. Dialing a telephone, Using a mouse, reading a piece of paper etc. To test whether hand postures could be associated with correct activities independent of which user the recognizer was trained on, the authors performed a leave one out cross validation across all 8 users. The average accuracy in this case was 62.5%. For user dependent training and testing the accuracy obtained was 94.2%. This lead the authors to conclude that hand posture can indeed be used as a cue to perform activity based recognition. However, a user dependent trained system performs much better. A user independent system performs poorly due to high variation in how users carry out tasks.
DISCUSSION
The paper provides a good insight into how hand postures can inform activity recognition. It would be interesting to see if the method performs better when combined with vision techniques. For example its seems that the ambiguity in recognizing 'picking up a pape'r and 'picking up earphones' could be resolved if the system could see the 'object'..
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