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The use of hand postures depends on the type of activity.
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A recent study of Paulson et al. (2011) already demonstrated the successful use of hand posture for discriminating between several actions in an office setting.
For the purpose of hand posture classification, Euclidean and Mahalanobis distances are used in [3, 4].
The technique presented in this paper will be used for the recognition of hand postures needed for the activities of daily life while patients playing rehabilitation games with the SCRIPT orthosis over a period of six weeks.
Let us consider H classes of hand postures.
The learner then replays the resulting sequence of hand postures, to generate a dataset of posture contact pairs that are not influenced by the touch of the teacher.
Top-down view of hand postures in Experiment 2 with the left hand behind the mirror (a g).
This section aims to state the problem of hand posture recognition and describes the dictionary of hand postures chosen in collaboration with an industrial partner.
This work is a seminal attempt to propose the use of multi-objective algorithms in the field of hand posture estimation.
The problem of hand posture detection has been previously approached using vision-based [ 25, 26] or glove-based [ 25, 27– 29] methods depending on the constraints of the specific applications.
We found that the effect of hand posture was significant (ANOVA, α<0.05, p<0.0001).
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