Abstract
Group Activity Recognition (GAR) is a challenging research area in context-aware computing which has attracted much attention recently. Many studies have been conducted in the field of activity recognition (AR) along with their applications in domains such as health, smart homes, daily living and life logging. However, still many open issues exist. Lack of an energy-efficient approach is one of the most vital issues in the context of AR. GAR work often suffers from energy consumption issues for the reason that, apart from AR process, there is the requirement to have more interaction among members of the group and a need to run more complex recognition processes. Moreover, almost all work in GAR are technology-oriented and assume that our real-life environment remains fixed once the system has been established, but this may not be the case. Hence, we propose a framework called Group Sense for GAR towards addressing these issues. Also, a relatively simple scheme for GAR, with a protocol for the exchange of information required for GAR, has been implemented, tested and evaluated. We then conclude with lessons learnt for GAR.
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