## 6 Conclusions and Future Works  
In this paper, we proposed a novel robust framework, called the multi combined features of the HAR system, which recognizes human activities via the inertial measurements captured from wearable sensors. The multi combined features examined the spatiotemporal variation, optimal pattern, structural uncertainty, rehabilitation motion, and transitional activity features. These features are then passed through three classifiers with optimization algorithms including the Genetic Algorithm (GA) optimized by Ant Colony Optimization (ACO), the Decision Tree (DT) optimized by Binary \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dynamic Activities** & **STU** & **WLK** & **CLP** & **BOX** & **RUN** & **STD** \\ \hline STU & 95.50 & 0 & 0 & 0 & 0 & 4.50 \\ WLK & 2.50 & 97.00 & 0 & 0 & 0.50 & 0 \\ CLP & 0 & 0 & 98.50 & 1.00 & 0.50 & 0 \\ BOX & 0 & 2.50 & 1.00 & 94.50 & 2.00 & 0 \\ RUN & 0 & 0.50 & 0.50 & 0 & 99.00 & 0 \\ STD & 3.00 & 0 & 0.50 & 0 & 0 & 96.50 \\ \hline \multicolumn{7}{c}{Mean Accuracy = 96.83\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of IM-AccGyro dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Dynamic Activities** & **DWS** & **JOG** & **UPS** & **STT** & **WLK** & **STD** \\ \hline DWS & 89.00 & 1.00 & 5.50 & 0 & 4.50 & 0 \\ JOG & 0 & 98.50 & 0 & 1.50 & 0 & 0 & 0 \\ UPS & 0 & 0 & 88.00 & 9.50 & 0 & 2.50 \\ SIT & 0 & 0 & 15.50 & 84.50 & 0 & 0 \\ WLK & 3.50 & 0 & 0 & 0 & 86.0 & 10.50 \\ STD & 3.50 & 0 & 0 & 7.50 & 5.50 & 83.50 \\ \hline \multicolumn{7}{c}{Mean Accuracy = 88.25\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of MOTIONSENSE dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Dynamic Activities** & **WLK** & **CLS** & **STS** & **STR** & **WBF** & **CYC** & **JOG** & **RUN** & **JFB** & **KNB** & **FEA** & **LYD** \\ \hline WLK & 96.00 & 0 & 0 & 0 & 0 & 0 & 0 & 2.50 & 1.50 & 0 & 0 & 0 \\ CLS & 0 & 92.50 & 1.50 & 1.50 & 0.50 & 0.50 & 0.50 & 0 & 2.50 & 0 & 0 \\ STS & 0 & 0 & 98.50 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1.50 \\ STR & 0.50 & 0 & 0 & 95.50 & 0 & 0 & 0 & 0 & 0 & 2.50 & 1.50 \\ WBF & 0 & 0 & 2.50 & 0 & 93.50 & 0 & 0 & 0 & 3.50 & 0 & 0 & 0.50 \\ CYC & 0 & 2.50 & 0.50 & 0 & 0 & 93.00 & 0 & 0.50 & 1.00 & 0 & 2.50 & 0 \\ JOG & 0 & 0.50 & 0 & 0 & 1.50 & 97.00 & 0 & 0.50 & 0 & 0.50 & 0 \\ RUN & 0 & 0 & 0 & 2.50 & 0.50 & 0 & 96.50 & 0 & 0 & 0.50 & 0 \\ JFB & 0 & 0 & 0 & 0 & 0 & 2.50 & 0 & 97.50 & 0 & 0 & 0 \\ KNB & 5.50 & 0 & 0 & 3.50 & 4.00 & 0 & 0.50 & 0 & 0 & 86.00 & 0 & 0.50 \\ FEA & 2.50 & 1.50 & 2.50 & 0 & 0 & 1.00 & 0.50 & 0 & 2.00 & 0.50 & 88.0 & 0.50 \\ LYD & 0 & 0 & 0 & 2.50 & 0 & 0.50 & 3.50 & 0 & 0 & 0 & 0 & 93.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The classification results of three classifiers on the MHEALTH dataset. Grey Wolf Optimization (BGWO), and the Support Vector Machine (SVM) optimized by Particle Swarm Optimization (PSO). During the experiments, we used three challenging inertial sensors datasets including the MOTIONSENSE, MHEALTH, and a proposed self-annotated IM-AccGyro human-machine dataset. The recent work done by numerous researchers using state-of-the-art classifiers (SVM [41] and GA [42]) has shown good classification results against multiple benchmark HAR datasets. That is why we evaluated our proposed model against these classifiers. Our proposed method achieved remarkable recognition accuracy performance over the state-of-the-art methods. As future work, we will improve the efficiency of our multi combined features by adding wavelet and frequency-domain features. Additionally, we are planning to develop more complex activities for different scenarios such as smart homes, offices, and hospitals using various other wearable sensors. Conceptualization, M.B. ; methodology, M.B. and A.J. ; software, M.B. ; validation, A.J. ; formal analysis, K.K. ; resources, A.J. and K.K. ; writing--review and editing, A.J. and K.K. ; funding acquisition, A.J. and K.K.