# Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells
## 6 Conclusions  
In this paper, two methods of classification were used to identify the modes of dumbbell exercise in real time. Working of these methods has successfully implemented these algorithms, and combination of the two methods resulted in perfect results. For the ROM method, monitoring the activity of each mode was based on the behavior of the person performing the dumbbell exercise. The SVMs with a non-linear kernel are extremely powerful classifiers. The results showed that the accuracy rates of six mode detections were 100%, 98%, 100%, 99%, 100%, and 97%, respectively. Additionally, we developed an estimated cycle for a task using a small amount of data to detect the action. Further, we have not only used the acceleration and velocity of the angle but also the angle of the Kalman filter to create an algorithm that may obtain an accuracy result of 100%. The classification of the action mode has a significant benefit for dumbbell exercises in that humans will not be bored by interacting with each mode of motion. In the future, it may replace the personal trainer for assisting human training of dumbbell exercisers.  
In future research, we aim to work on creating an algorithm for all modes of exercise with dumbbells. Additionally, we would like to allow people to correct the activity through following a guide on the monitor of the human interface machine. The research may open a new direction in which exercise testing can be considered a non/invasive diagnostic technique applied in clinical practice. Through the recognition of motion status with dumbbells, the increase/decrease of movement speed or parameters of action can be analyzed. As a result, abnormal data associated with repeatedly wrong movements is detected, which may contribute to clinical implications in diagnosing the disorder symptoms including exercise-induced asthma, growth hormone deficiency, congenital heart defects, and so on.  
In this paper, the author contributions were: conceptualization, Y.S.; methodology, Y.S. and T.H.; software, T.H. and P.-Y.C.; validation, Y.S. and T.H.; formal analysis, Y.S. and T.H.; investigation, T.H. and P.-Y.C.; data curation, T.H. and P.-Y.C.; writing--original draft preparation, T.H.; writing--review and editing, Y.S.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology (MOST), Taiwan, grant number 108-2622-8-027-005.  
Ethical review and approval were waived for this study, due to all subject involved in this study being authors or lab members.  
Written informed consent has been obtained from all subjects to publish this paper.  
We thank the financial support from Ministry of Science and Technology (MOST), Taiwan (Project number: MOST 108-2622-8-027-005 and 108-2221-E-027-097.)  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**USING Support** & **Biceps** & **Deltoid** & **Triceps** & **Shoulder** & **Squat** &
\begin{tabular}{c} **Side** \\ **Lunge** \\ \end{tabular} \\ \cline{2-6}  & \(1\) & \(2\) & \(3\) & \(4\) & \(5\) & \(6\) \\ \hline Biceps & \(1\) & \(100\%\) & & & & \\ \hline Deltoid & \(2\) & & \(98\%\) & & & \\ \hline Triceps & \(3\) & & \(100\%\) & & & \\ \hline Shoulder & \(4\) & & & \(99\%\) & & \(3\%\) \\ \hline Squat & \(5\) & & & & \(100\%\) & \\ \hline Side lunge & \(6\) & & \(2\%\) & \(1\%\) & & \(97\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The matrix fusion of Support vector machine method.