# Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition
## 5 Evaluation and Results  
The proposed system is empirically evaluated by adopting lengthier video footage with various ADL than that used in the previous section. A 5-min video containing all 10 types of the most common ADL was used. Each type of ADL appeared randomly 3 to 5 times in the footage. Each ADL was manually marked and compared with the evaluation results In Table 1. The evaluation results include the accuracy rate and mis-classification rate. The average accuracy rate is 95.1%. The proposed system performs perfectly (100% accuracy rate) for 5 types of ADL: standing, raising one hand, raising two hands, sitting plus drinking, and standing plus drinking. The proposed system also works effectively with an accuracy recognition rate greater than 95% for the ADL of sitting, eating, and falling. It is the industry health care standard that the proposed system detects falling with a high accuracy rate at 97%, as falling is especially dangerous for seniors. There are two types of ADL (bending and squatting) that are relatively easy for the proposed system to misclassify: bending can be misclassified as squatting or sitting, and squatting may be misclassified as sitting or bending. Compared with Figure 1, there was one case where eating was misclassified as bending perhaps due to the similarity of the bending of the arm(s) or leg(s) to the eating ADL while sitting. The reason for misclassification is mainly lies the similarity of skeletal positioning in certain postures, as shown in Table 1 and Figure 1. Our proposed methodology combines human behavior recognition, image processing, movement calculation, and DNN for smart care applications. The originality of our approach lies in the calculation of the limb joint angle, angle between the limb and the horizontal axis, changes in length for the wrist joint, and using the sliding click method to filter out video noise. We have demonstrated the originality of our approach and it feasibility for both practical and academic applications.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{**Standing**} & \multicolumn{1}{c}{**Bending**} & \multicolumn{1}{c}{**Squatting**} & \multicolumn{1}{c}{**Sitting**} & \multicolumn{1}{c}{**Eating**} & \multicolumn{1}{c}{**Raising**} & \multicolumn{1}{c}{**Raising**} & \multicolumn{1}{c}{**Sitting**} & \multicolumn{1}{c}{**Standing**} & \multicolumn{1}{c}{**Standing**} & \multicolumn{1}{c}{**Claus**} \\  & & & & & **Hand** & **Hands** & **Drink-** & **Drink-** & **Drink-** & **Drink-** & **Drink-** & **Drink-** \\ \hline Standing & 100 & - & - & - & - & - & - & - & - & - & 100 \\ \hline Bending & 4 & 80 & 7 & 6 & 1 & - & - & - & - & - & 80 \\ \hline Squatting & 1 & 6 & 82 & 11 & - & - & - & - & - & - & 82 \\ \hline Sitting & - & - & 3 & 97 & - & - & - & - & - & - & 97 \\ \hline Eating & - & - & - & - & 95 & - & - & 1 & 4 & - & 95 \\ \hline Raising & & & & & & & & & & & \\ one & - & - & - & - & - & 100 & - & - & - & - & 100 \\ hand & & & & & & & & & & & \\ \hline Raising & & & & & & & & & & & \\ two & - & - & - & - & - & - & 100 & - & - & - & 100 \\ hands & & & & & & & & & & & \\ \hline Sitting & & & & & & & & & & & \\ plus & - & - & - & - & - & - & - & 100 & - & - & - & 100 \\ drink- & & & & & & & & & & & \\ ing & & & & & & & & & & & \\ \hline Standing & & & & & & & & & & & \\ plus & - & - & - & - & - & - & - & - & - & 100 & - & 100 \\ drink- & & & & & & & & & & & \\ ing & & & & & & & & & & \\ \hline Falling & - & 2 & - & - & 1 & - & - & - & - & 97 & 97 \\ \hline \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation result for DNN-based system.