# Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study
## 5 Results of Implementation
### Results of Human Activity Recognition
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Activity Name** & **Real Activities** & **Simulated Activities** \\ \hline Sleeping & from 00:03:50 to 05:40:43 & from 00:03:50 to 05:40:27 \\  & from 05:43:45 to 08:01:12 & from 05:43:45 to 08:00:21 \\ \hline  & from 5:40:51 to 05:43:30 & from 05:40:51 to 05:43:24 \\  & from 08:57:48 to 09:02:48 & from 13:32:00 to 13:33:24 \\  & & from 15:21:21 to 15:22:49 \\  & & from 23:38:36 to 23:42:36 \\ \hline  & from 15:47:48 to 16:10:33 & from 15:47:48 to 16:10:23 \\ Work & from 17:55:24 to 17:57:47 & from 17:55:24 to 17:57:46 \\  & from 18:06:57 to 18:09:03 \\ \hline  & from 09:29:23 to 09:34:05 & from 08:08:38 to 08:10:40 \\  & from 14:46:25 to 15:13:24 & from 09:30:10 to 09:34:01 \\ Relax & from 16:15:57 to 16:21:58 & from 14:46:25 to 15:13:25 \\  & from 17:06:21 to 17:27:12 & from 16:15:57 to 16:18:30 \\  & from 17:28:26 to 17:33:54 & from 16:18:33 to 16:21:53 \\  & from 17:06:17 to 17:19:50 \\ \hline  & from 09:56:41 to 09:59:04 & from 09:36:08 to 09:44:04 \\  & from 09:59:47 to 10:02:48 & from 09:56:41 to 10:03:17 \\  & from 15:25:35 to 15:28:42 & from 10:46:25 to 10:48:37 \\ Eating & from 17:35:16 to 17:37:11 & from 11:35:35 to 11:38:11 \\  & & from 15:25:35 to 15:28:39 \\  & & from 17:35:16 to 17:37:08 \\  & & from 17:44:13 to 17:45:19 \\ \hline  & from 08:11:09 to 08:27:02 & from 08:16:28 to 08:24:27 \\  & from 08:33:52 to 08:35:45 & from 09:03:00 to 09:26:41 \\  & from 09:48:52 to 09:53:02 & from 09:48:54 to 09:50:46 \\  & from 09:54:58 to 09:56:27 & from 13:54:44 to 14:13:19 \\ Meal Preparation & from 15:23:00 to 15:25:33 & from 16:25:02 to 16:27:24 \\  & from 16:21:59 to 16:31:26 & from 16:47:37 to 16:48:54 \\  & from 16:32:53 to 16:34:13 & from 17:50:38 to 17:51:42 \\  & from 16:36:10 to 17:06:00 & from 18:20:05 to 19:45:14 \\  & from 17:27:13 to 17:27:55 & from 21:36:36 to 23:25:59 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Real and Simulated Activities with Time Intervals. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Recall** & **F-Score** & **Absolute Error** \\ \hline Sleeping (1) & 1 & 0.99 & 0.99 & 9 sec \\ \hline Sleeping (2) & 0.99 & 0.99 & 0.99 & 78 sec \\ \hline Average error & 0.995 & 0.99 & 0.99 & 43.5 sec \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance Metrics Detection Algorithm on 4 November 2010. As a comparison, the recognition performance (i.e., the average F-score) for 8 activities (sleeping, bed to toilet, eating, housekeeping, relax, work, enter home, and leave home) of the proposed algorithm is 0.766 [11], while this value is equal to 0.73 using the decision tree technique presented in [26], 0.765 using sensor event-based windowing technique [10] and 0.760 using a combination of SWMI (fixed length sensor windows with mutual information based weighting of sensor events) and SWTW (fixed length sensor windows with time-based weighting of the sensor events) techniques [10], and a value of 0.773 using SWTW technique [10] and a value of 0.83 using the Naive Bayes tree technique presented in [26]. Despite certain biases of the dataset and the topology of the house, we are therefore naturally not as good as the algorithms in the field due to our compromises for the proposed simplifications, but we are not bad either. In the following, the proposed framework is implemented on three hardware architectures. Results are discussed in Section 4 and compared with a desktop PC with OS.