## 4 Results
### Execution Time Difference Results  
Basically, microcontrollers are in a 'wake-up' state when processing or transmitting data, and are in a'sleep' state when not. The duration of time during which the microcontroller is in a 'wake-up' state is when it consumes its battery. The algorithm contributes to reducing the battery consumption by reducing the packet size of transmitted data, because the transmitting data time is reduced. However, battery consumption not only depends on the transmission time (transmission time needed per packet, \(tt\)), but also on the processor load (processing time needed per packet, \(pt\)). The higher the processor load, the higher the consumption, because the processing time (wake-up time) is increased. Since the algorithm increases the processor load, there is some trade-off between transmission time and processing time. Therefore, an assessment of the complex algorithm's contribution to the overall effect in computing time (total computing operations time per packet, \(ct\)) is needed, and is thus evaluated in this section. The relation between \(pt\), \(tt\), and \(ct\) follows Equation (3), and is shown in an example situation in Figure 7.  
\[\left\{ \begin{array}{l}ct=pt\ (if\ pt>tt)\\ ct=tt\ (if\ pt<tt)\end{array} \right. \tag{3}\]  
Two conditions A and B must be compared for an evaluation of the algorithm's effect on \(ct\). A is the time per packet needed to process and transmit data packets using the original system, and B is the time per packet needed to process and transmit data packets using the algorithm that is applied to the original system. The specific steps used to assess A and B are depicted in Figure 8.  
To assess the \(ct\) of A, first set the timer on, input the EMG data, packetize for transmission, transmit the data packets, unpacketize the data, output the EMG data, and set the timer off. On the other hand, to assess the \(ct\) of B, also set the timer on, input the EMG data, compress the data using EUMTS, packetize for transmission, transmit the data packets, unpacketize the data, uncompress the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Related Researches** & **PRD** & **CR** & **Lossy/Lossless** & **Real-Time** \\ \hline [PERSON] [14] & 0.10 & 2.00 & Lossy & Not Able \\ [PERSON] [15] & 0.00 & 1.69 & Lossless & Not Able \\ [PERSON] [16] & 3.90 & 3.33 & Lossy & Not Able \\ [PERSON] [17] & 1.79 & 3.33 & Lossy & Not Able \\ [PERSON] [18] & 1.21 & 3.33 & Lossy & Not Able \\ [PERSON] [7] & 2.12 & 3.33 & Lossy & Not Able \\ Proposed algorithm & 0.00 & 3.61 & Lossless & Able \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of \(CR\) with related researches. data using an EUMTS decoder, output the EMG data, and set the timer off. Note that the packet size used in this experiment is 20 samples, because Bluetooth usually sends one packet per second, and because the database used here is 20 Hz. Results of the A and B time comparison in milliseconds are shown in Table 7.  
The processor used for evaluation was a recent updated version of microcontroller 8051 that is suitable for wearable operating systems. The network environment was Bluetooth 4.2 Low Energy. Using the database mentioned in Section 4.1, 12 channels of EMG data containing 1000 packets (20,000 samples) in each channel were used in this experiment (size approximately 2 MB). The results showed that, naturally, the complexity of the proposed algorithm increased the processing time needed per packet from 2.0 ms to 870.0 ms (_pt_). However, EUMTS contributed to the largely decreasing transmission time needed per packet from 2340.6 ms to 648.4 ms, by efficiently compressing the data packet size (_tt_). In conclusion, in spite of a trade-off between the processing time and transmission time, the overall computation time decreased from 2340.6 ms to 870.0 ms (_ct_), thereby contributing to reducing the battery consumption of microcontrollers.