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摘要
The technology trend of context-aware computer systems carries the promise of more flexible automated systems, with a high degree of adaptation to the user's situation, but it implies as a precondition that the context information ( such as the place, time, activity, preferences, etc.) is indeed available. One very important aspect of the user context is the activity in which the human is currently involved. Human Activity Recognition (HAR) has become a trending topic in the last years because of its potential applications in pervasive health care, assisted living, exercise monitoring, etc. Most of the works on HAR either require from the user to label the activities as they are performed so the system can learn them, or rely on a trained device that expects a "typical" ideal user. The first approach is impractical, as the training process easily becomes time consuming, expensive, etc., while the second one drops the HAR precision for many non-typical users. In this work we propose a "crowdsourcing" method for building personalized models for HAR by combining the advantages of both user-dependent and general models, finding class similarities between the target user and the community users. We evaluated our approach on 4 different public datasets and showed that the personalized models outperformed the user-dependent and user-independent models when labeled data is scarce.
- 出版日期2017
