# Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition
## 3 Data Collection and Processing  
Easy acquisition and availability to the general population is the key to simulating real ADL. The ADL camera, available to most users must have the following basic prerequisites: resolution of 640 by 480, length of recorded video from 10 to 20 s and Frames Per Second (FPS) = 30. These conditions are essential for practical purposes. Therefore, the following criteria are set for the evaluations: (1) the video capture to the subject angle and distance can vary by 30 degrees from left to right, front to back; (2) the subjects can range in height 160-180 cm with a mean height around 173 cm. This range includes 80% of the population in Taiwan. (3) The top 10 most common ADL are selected including standing, bending, squatting, sitting, eating, raising one hand, raising two hands, sitting plus drinking, standing plus drinking, and falling [31; 32; 33]. Each subject performs one common ADL for 10 to 20 s for 4 times, giving 40 videos in total for training. Snap shots of each common ADL are illustrated in Figure 1. Based on the policy requested by Ministry of Science and Technology (MOST), Taiwan, every individual participating the research has signed the agreement and been permitted.  
For indoor ADL determination one usually needs to consider consistent changes in the position of the human body as well as human skeletal information even for the same individual. It is necessary to normalize the skeletal information for pre-processing to generate features to facilitate neural network identification. The process of database normalization is divided into two parts, data translation and data scaling. Data translation is the translation of the neck as the new origin \(O_{t}^{\prime}\), where each joint point \(j_{x}(t)\) is subtracted from the coordinates of the neck \(j_{ Neck}(t)\) to perform translation, substituting the relevant nodes into the new joint point \(j_{x}^{\prime}(t)\). It can be expressed as follows:  
\[j_{x}^{\prime}(t)=j_{x}(t)-j_{ Neck}(t), \tag{1}\]  
where \(j_{x}^{\prime}(t)\) is the new coordinate of the joint point x at time t; \(j_{x}(t)\) is the coordinate of the joint point at time t; \(j_{ Neck}(t)\) is the coordinate point of the neck at time \(t\). During data scaling the skeleton is captured on a 2D image. The original length of the limbs is not  
Figure 1: Ten most common ADL.  
known but the coordinates of the skeleton on the 2D image are pointed out. Consider the distance from the center of the hip joint to the neck at time \(t\) as the unit length. The algorithm is formulated as in Equation (2) where it can be seen that the proportion of \(h(t)\) in the whole body when bending over is smaller than that in the whole body when standing:  
\[h(t)=\parallel\frac{\left(j_{LHip}(t)+j_{RHip}(t)\right)}{2}-j_{Neck}\parallel. \tag{2}\]  
Now, \(s(t)\) is solved for in Equation (3) using the two limb lengths as the basis for normalization:  
\[s(t)=\parallel\frac{j_{LShoulder}(t)+j_{RShoulder}(t)}{2}-j_{Neck}(t)\parallel. \tag{3}\]  
The maximum value for \(s(t)\) and \(h(t)\) is expressed as the unit length \(U(t)\) in Equation (4). By dividing the skeletal coordinate points, the new zooming joint coordinate point \(S_{x}(t)\) can be obtained in Equation (5):  
\[U(t)=max(h(t),s(t)); \tag{4}\]  
\[S_{x}(t)=(\frac{\frac{\prime}{x}(t)}{U(t)}). \tag{5}\]  
The computation of the joint angle and changes in length from wrist to joint for extracting the features including the angle of the limb joint, angle between the end of the limb and the horizontal, and change in length from wrist to joint are described below.