No abstract

Abstract-Activity recognition in smart homes plays an important role in healthcare by maintaining the well being of elderly and patients through remote monitoring and assisted technologies. In this paper, we propose a two level classification approach for activity recognition by utilizing the information obtained from the sensors deployed in a smart home. In order to separate the similar activities from the non similar activities, we group the homogeneous activities using the Lloyd's clustering algorithm. For the classification of non-separated activities within each cluster, we apply a computationally less expensive learning algorithm Evidence Theoretic K-Nearest Neighbor, which performs better in uncertain conditions and noisy data. The approach enables us to achieve improved recognition accuracy particularly for overlapping activities. A comparison of the proposed approach with the existing activity recognition approaches is presented on two publicly available smart home datasets. The proposed approach demonstrates better recognition rate compared to the existing methods.

No abstract

No abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractActivity recognition in smart homes provides valuable benefits in the field of health and elderly care by remote monitoring of patients. In health care, capabilities of both performing the correct recognition and reducing the wrong assignments are of high importance. The novelty of the proposed activity recognition approach lies in being able to assign a category to the incoming activity, while measuring the confidence score of the assigned category that reduces the false positives in the assignments. Multiple sensors deployed at different locations of a smart home are used for activity observations. For multi-class activity classification, we propose a binary solution using support vector machines, which simplifies the problem to correct/incorrect assignments. We obtain the confidence score of each assignment by estimating the activity distribution within each class such that the assignments with low confidence are separated for further investigation by a human operator. The proposed approach is evaluated using a comprehensive performance evaluation metrics. Experimental results obtained from nine publicly available smart home datasets demonstrate a better performance of the proposed approach compared to the state of the art.