**7. Conclusions**

In this work, we proposed an activity recognition framework for indoor environments, composed of off-the-shelf smart watches and BLE beacons. A mobile phone is responsible for gathering smart watch and beacon data and transmitting them to a server where the processing and classification takes place. Our approach uses location information revealed by the beacon data, to enhance the classification accuracy of the machine learning algorithms we employ. Our experimental results have shown that there is a clear improvement in the performance of our system when beacon data are used. However, the extent to which the location information can be advantageous depends on the type of classifier. LR cannot take full advantage of location information, while KNN and RF benefit more from the fusion of beacon data. SVM exhibits the highest performance gain when using beacon data. Furthermore, we observe that the more unique the location of an activity is with respect to the others, the higher the benefit in activity recognition performance. However, we must highlight that even subtle differences in activity locations are sufficient for a significant improvement in the classification accuracy (e.g., working on different parts of a workbench inside the same sector). Finally, location information can make the system more adaptive, as it allows for smaller window sizes, which results in less time required to collect and classify data.

In future work, we will further investigate human activities that can take place in an indoor setting, such as building emergency management [52,53]. This could prove beneficial for an emergency operation, as it could improve situational awareness with respect to the activities of building occupants in the instances before or after an incident took place. Finally, we will investigate a wider range of machine learning algorithms and consider the use of neural networks and deep learning for further improving our system's performance.

**Acknowledgments:** This work was supported by the University of Greenwich Research & Enterprise Investment Programme.

**Author Contributions:** Avgoustinos Filippoupolitis conceived the experiments. Avgoustinos Filippoupolitis and George Loukas designed the experiments. William Oliff and Babak Takand developed the mobile application and performed the experiments. Avgoustinos Filippoupolitis analysed the data. All authors have participated in writing the paper. Avgoustinos Filippoupolitis and George Loukas edited the paper.

**Conflicts of Interest:** The authors declare no conflict of interest.
