## 5 Conclusions  
This study proposed an action identification system for daily home upper extremity rehabilitation. In this section, we will discuss the key contributions of this study. First, in Skeleton Point Establishment phase, we set up those skeleton points not only according to the changes of image features, but also consider the real movements of joints and muscles. Hence, we set up and correct those skeleton points following the principles of anatomy of actions and principles of muscles testing, and those principles also be applied to set rehabilitation action programs. We have preset seven kinds of rehabilitation movements for specific muscle groups in the system to provide a guide for users to perform the actions. The selection of these rehabilitation actions was based on their importance for rehabilitating the ability to perform daily living activities that require the use of the upper extremity such as dressing oneself or reaching for things. Each rehabilitation action program corresponds to training of specific \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Title** & **dpi** &
\begin{tabular}{c} **No. of Skeleton** \\ **Points** \\ \end{tabular} & **ms** & **fps** & **Accuracy Rate** \\ \hline Real-time hand-pair gesture recognition & 640 \(\times\) 480 & 2 & 40 & 25 & 93\% \\ using a stereo webcam [33] & 640 \(\times\) 480 & 3 & 32 & 32 & 98\% \\ \hline Real-time body gesture recognition using depth camera [27] & 640 \(\times\) 480 & 7 & 27 & 37 & 98\% \\ \hline proposed & 320 \(\times\) 240 & 7 & 8 & 125 & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison with two studies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Sequence** & **No. of Actual** & **No. of Correctly** & **Accuracy Rate of Identifying** \\  & **Determined Actions** & **Determined Actions** & **Rehabilitation Action** \\ \hline position 1 & 151 & 149 & 98.7\% \\ position 2 & 150 & 146 & 97.3\% \\ position 3 & 156 & 155 & 99.4\% \\ position 4 & 190 & 184 & 96.8\% \\ position 5 & 120 & 119 & 99.2\% \\ position 6 & 186 & 182 & 97.8\% \\ Total & 953 & 935 & 98.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of identification of rehabilitation actions. muscle groups such as the biceps, triceps, or deltoid muscles. Second, the hardware used in the upper extremity rehabilitation system comprises a personal computer with a Kinect depth camera that can be set up similarly to a study desk or computer table at home; thus, the system provides a familiar and convenient environment for rehabilitation users, and the patient can perform the rehabilitation routine even in a limited space without extraneous equipment. Third, patients who cannot stand for a long periods of time, such as stroke, hemiplegia, muscular dystrophy, advanced age, etc., can perform rehabilitation actions in a sitting position, which reduces energy expenditure and enables the patient to focus on the motion control training of the upper extremities without worrying about falling. Fourth, the execution speed of the proposed system can reach 8 ms at a resolution of \(320\times 240\), handle a frame rate equivalent to 125 FPS, and achieve 98% accuracy when identifying rehabilitation actions. Fifth, the proposed upper extremity rehabilitation system operates in real time, achieves efficient vision-based action identification, and consists of low-cost hardware and software. In light of these enumerated benefits, we contend that this system can be effectively used by rehabilitation patients to perform daily exercises, and can reduce the burden of transportation and the overall cost of rehabilitation. The current limitations of the proposed system are: (1) We combined the skin color detection into skeletonizing process in order to allow the system to be more accurate in establishing the skeleton.