## 4 Discussion  
The first novelty of the method lies in the gravity acceleration estimation step which replaced the straightforward subtraction of 1g usually applied with the ENMO algorithm. It is illustrated in Figure 3 where the standard version of the ENMO overestimates the activity intensity during the second Rest phase. To maintain this paper conciseness, the complete  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject** & **Rest** & **Low** & **Moderate** & **High** & \(CV\) \\ \hline
1 & \(-\)1.9 & 2.4 & 0.4  
original method results were not presented but it was nonetheless assessed and produced an average \(CV\) of 93%. This is mainly due to the imprecise calibration of the consumer accelerometers which leads to a gravity acceleration value slightly different from 1g. The addition of our gravity acceleration estimation method proved to drastically improve the results by decreasing the average \(CV\) to 16.2%. Table 3 demonstrates that the method had difficulties with the discrimination of Rest and Low intensity activity. This was expected as in both cases the acceleration magnitudes are similarly small, with very tenuous difference between desk work and resting on a chair. This is in line with the conclusion of [PERSON] et al. [18]. However, we have implemented a workaround to improve the Rest vs Low intensity discrimination. Averaged levels of activity are similar but their dynamics differ. Therefore, the proposed step, based on an information fusion between the ENMEG detection and a sliding standard deviation detection, helped by decreasing the average \(CV\) to 10.6%, as illustrated in Table 4.  
Finally, merging both intensities improved the results further by producing an average \(CV\) of 3.7%. This is possible under the reasonable hypothesis that there are no drastic metabolic differences between Resting and Low intensity activities. Analysing Table 4, one can observe that subject 4 results are significantly worse than the others. During the experiment the subject had to deviate from the planned tasks and climbed stairs. Therefore, during approximately 10 min, subject 4 was not resting but the motion intensity of their arms did not correlate with the intensity of the activity. Hence, despite being labeled as moderate activity, the algorithm detected this part as low intensity activity. The same flaw was observed for physical activities where the arms did not have large amplitudes of motion, e.g., riding a bike. This limitation is directly linked to the locational placement of the sensor on the body. Here, the detectability of activities is tightly bound to arm movements. Close examination of Tables 3 and 4 will reveal that although the overall performance of the method is improved, for some subjects (4, 7 and 9) it decreases, notably for Rest and Low intensities. This phenomenon can be explained by visually inspecting the signals where it is observable that some of the labelled segments did not exactly correspond to the instructions. This is a limitation of experiments performed in an uncontrolled environment. Nonetheless, we decided to include those subjects in the study anyway as these divergences were minimal and were later negated by the last step of the method. The main weakness of the study is the data set size, as 11 subjects are not enough to draw conclusion on efficiency or precision. Although it is more prone to biases, it is enough to demonstrate a proof of concept. However, as in many studies in the field of human activity recognition or quantification, the data set used to develop the method was collected on healthy subjects. Its performances are expected to be slightly decreased when applied to aged and/or impaired subjects, as described in [22]. However, [PERSON] et al. showed that simple metrics as duration of activities are more robust than others like steps counting and it is expected that the adaptive thresholds will help in personalizing the method to each subject [23].