This work demonstrates an interesting approach toudgesture recognition for elderly people for the purpose of health monitoring at home. The system proposes to detect disorder symptoms on the basis of gesture analysis and generate alarms, thereby finding significance in elderly healthcare. Here the gestures are tracked using Microsoft’s Kinect sensor. From each frame captured by the Kinect sensor, four centroids representing four parts of the body are calculated and from these four centroids a novel feature set is extracted in terms of Euclideanuddistances and angles. We have noticed that for different persons’ body types the extracted features might vary. Thus to accommodate these non-uniformities, we have used the concept of interval type-2 fuzzy logic based classification. The unknown gesture is recognized based on matching with all the known gestures from the dataset. The proposed methodology provides a high accuracy rate of 92.14%.
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