# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 8 Conclusions  
In this paper, we proposed a method to leverage the synergy between location and motion information to solve the problem of human simultaneous tracking and activity recognition (STAR) [16]. To show the potential of simple PIR sensors for automatic surveillance, the coding scheme of the FOV was designed to capture the spatio-temporal information of the human subject. We also designed useful features for human activities recognition, including human location, locomotion speed, and  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Methods** & **Sensor Type** & **Activity Types** & **Mean Accuracy** \\ \hline [PERSON] et al. [11] & Wearable + Optical Tracker & Lying, Sitting, Standing, Walking, & about 85\% \\  & & Transitional Activities & \\ \hline [PERSON] et al. [37] & Depth Video & Smart Home Activities & 92.33 \% \\  & & Smart Office Activities & 93.58 \% \\  & & Smart Hospital Activities & 90.33\% \\ \hline [PERSON] et al. [38] & Wearable & Housekeeping Tasks & 90.67\% \\  & & Activity Level Classification & 94.35\% \\ \hline [PERSON] et al. [39] & Fisheye Cameras & Walking, Standing, Sitting & about 94\% \\ \hline [PERSON] et al. [40] & Video & Walking, Standing, Sitting, & 77.86\% \\  & & Interaction with Table, Sleeping & \\ \hline Proposed Method & PIR Sensors & Lying, Sitting, Standing, Walking, & 92.51\% \\  & & Transitional Activities & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of human activity recognition (HAR) systems.  
the duration of the successive activity. A two-layer RF framework was used to model these features and to output the final classification result. We conducted experiments in a mock apartment. The accuracy and standard deviation of activity recognition results were evaluated and compared with other algorithms. Besides high recognition accuracy, our system can significantly reduce the burden of data communication and the complexity of data processing. It is crucial in resource-deprived scenarios, such as WSNs. In our future work, we will focus on multiple human localization and fine granularity activity recognition. The coding scheme of the PIR sensors will be further investigated, and the reference structure will be redesigned to facilitate the data association problem of multiple targets. More sophisticated and hierarchical classification models such as conditional random field (CRF) [41] and HMM [42] will be employed to model the sequential constraints of successive activities.  
This work is supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61301294 and 61601523, the Natural Science Foundation of Guangdong under Grant No. 2016A030310238 and Youth Elite Project of Guangzhou University of Chinese Medicine.  
Study concept and design: [PERSON] and [PERSON]. Acquisition of data: [PERSON], [PERSON] and [PERSON]. Analysis and interpretation of data: [PERSON] and [PERSON]. Drafting of the manuscript: [PERSON] and [PERSON]. Discussion and reviewing the manuscript: [PERSON] and [PERSON]. All authors read and approved the final manuscript.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.