# Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition
## 4 DNN-Based Systems for ADL Recognition  
The most commonly used neural networks to solve practical problems are the single layer BPNN, multi-layer BPNN, CNN, and DNN. This study develops a recognition system for ADL using DNN and compares it with the single layer BPNN, multi-layer BPNN, and CNN methods to evaluate the feasibility of the proposed system. We start with consideration of the parameters for these neural networks, using the following suggested parameter settings [34; 35; 36]: (1) activation functions = sigmoid; (2) learning rate = 0.8 for BP neural networks and 0.01 for DNN; (3) optimal method = gradient correction for BP neural networks and Adam optimization for DNN; (4) five hidden layers with 5-50 neurons in each layer for DNN, e.g., DNN with a [20; 20; 20; 20; 20] classifier; (5) two fully connected classifiers for CNN to deal with 2048 dimensions, 384 neurons for the first hidden layers, 192 neurons for the second hidden layers since the kernel size of convolution layer = 5 by 5 with step = 1 by 1 and pooling layer = 3 by 3 with step = 2 by 2.  
The feature extraction for recognition of ADL is examined for comparison among the four methods. Comparison is conducted using 3-fold cross-validation. As can be seen in Figure 5, all four methods have recognition accuracy rates of greater than 98% at least. The DNN with a [30; 30; 30; 30; 30] classifier and CNN systems perform even better, reaching and accuracy rate of over 99%. Next, we carry out multi angle tests using these four methods. In reality, it is not guaranteed that we can obtain front facing video of every subject. We shoot side and front footage twice in each practice session, and then randomly mix these videos together for system recognition. As can be seen in Figure 6, DNN with a [30; 30; 30; 30; 30] classifier and CNN systems have almost the same test results, with an accuracy rate of approximately 97.5%, slightly better than for the multi- and single layer BPNNs. To check whether the ADL recognition process works for most subjects, to meet the initial three requirements, we randomly select video footage of different subjects with different combinations of movement for 3-fold cross-validation. Frankly speaking, all the NN applications perform well for ADL recognition. As shown in Figure 7, all four systems have high accuracy rates greater than 98% for training. The DNN system with a [30; 30; 30; 30; 30] classifier has the highest accuracy rate, around 95%, for testing, among these four systems. The results confirm that the DNN-based system for ADL recognition performs the best among the major NN applications.  
Figure 4: Sliding window concept.  
Figure 5: Feature extraction comparison among the four major NN methods.  
Figure 6: Multi shooting angle comparison among the four major NN methods.  
An important step to ensure better efficiency and accuracy for DNN-based recognition system is to determine the size of the sliding window. The frame in the Figure 4 is an example for a 3-click window. A small window can reduce the computation/processing time but may not be able to yield high accuracy. On the other hand, a relatively large window may provide a higher accuracy rate but will usually cause system lags in recognition, sometimes significant enough to render the system impractical. Figure 8 exhibits a [30; 30; 30; 30; 30] DNN classifier with window sizes of 1, 3, 5, 7, and 9. When the click number increases, the computational penalty for the size of the window leads to computational lag, because too much information may contain enough noise to reduce the accuracy of the results. It is clear that the best accuracy is obtained with a 7-click sliding window. The developed ADL recognition system is completed using a [30; 30; 30; 30; 30] DNN classifier with a 7-click sliding window to filter out noise, to facilitate recognition, to increase efficiency, and to yield higher accuracy.  
Figure 8: Accuracy comparison among 1–9 click sliding windows.  
Figure 7: Scenario comparison among the four major NN methods.