Item-picking Gesture Recognition Using Extended Shapelet Mining
碩士 === 國立交通大學 === 電機資訊國際學程 === 106 === The usage of wearable device to recognize human gestures are getting more exposed, there are many work in the field which yield good recognition result. However, existing study still can not show the shape of a key feature which special and distinct-able to cla...
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| Format: | Others |
| Language: | en_US |
| Published: |
2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/q6yjec |
| Summary: | 碩士 === 國立交通大學 === 電機資訊國際學程 === 106 === The usage of wearable device to recognize human gestures are getting more exposed, there are many work in the field which yield good recognition result. However, existing study still can not show the shape of a key feature which special and distinct-able to classify gestures. In this work we propose SenseShape, an algorithm based on Shapelet Mining. It is a powerful algorithm which robust to noise and distortions because of local features usage. We modify the algorithm to work with inertial sensor whose data are multidimensional to recognize micro activity from item-picking gesture. SenseShape improved the traditional shapelet mining by implementing statistical features other than distance calculation into inertial data. To explore the feasibility of our vision, we conducted experiments using 6-axis sensor worn at wrist of the user to collect item-picking gesture data where objects are put at 3 different height levels of shelf. The new proposed method shows that it can identify micro activity of item-picking gesture regardless of shelf-location or movement speed with 94.7\% accuracy while precision and recall rate is at 94.5\% and 91.6\%, respectively.
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