## 4 Discussion  
This preliminary study presents a postural transition (PT) evaluation process that has clear strengths: the first of them is its simple set-up. The choice of an IMU sensor for motion analysis offers, in fact, several advantages: this technology is easy to use, even by clinicians who work with patients on a daily basis; cost-effective; non-invasive for patients; and applicable directly to the patient without the need for a lengthy preparation time for the subject or the environment (e.g., undressing and placing markers on the subject or a lengthy time for instrument calibration). Moreover, the use of a single IMU sensor with a properly selected location further reduces the impact of the set-up on the subjects' freedom of movement, allowing natural performance of different tasks and therefore enabling the outline of a framework compatible with the needs of continuous monitoring of a subject's movement condition. The chosen position for the placement of the sensor represents, in this sense, a good compromise between the capacity of the sensor to properly read the desired signal on the one side and the invasiveness of the IMU sensor for the user on the other across all the performed PTs. Other positioning strategies could provide better results if considering a specific PT only. Focusing on the analysis of the collected signals, a second relevant key point is in the proposed data processing, which implements an automatic detection strategy for the identification of the start and stop events for the movement that could be applied to all the PTs evaluated. The method is based on the analysis of \(a_{g}\), i.e., the gravitational contribution to the acceleration signal, which provides information about the focal movement performed by the subject, meant as the main movement, i.e., the specific PT, that the subject is realizing. This strategy proved to be a reasonably robust procedure for the analyzed PTs: in fact, the \(a_{g}\) signal also revealed good results in start and stop detection for acquisitions affected by non-ideal effects, like in the presence of noise or thorax movements due to heartbeats or breathing cycles (see Figure 6). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Functional Parameter** & **Roll** & **SiSt** & **StSi** & **SiSu** & **SuSi** \\ \hline \multirow{4}{*}{Traditional Definition (\_B)} & Total Time & \(TT\) & \textless{}0.001 \({}^{*}\) & 0.376 & 0.019 \({}^{*}\) & \textless{}0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \cline{2-7}  & Smoothness & \(I_{B}\) & \textless{}0.001 \({}^{*}\) & 0.754 & 0.167 & 0.009 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \cline{2-7}  & Fluency & \(Fl_{B}\) & \textless{}0.001 \({}^{*}\) & 0.053 & 0.985 & 0.009 \({}^{*}\) & \textless{}0.001 \({}^{*}\) \\ \cline{2-7}  & Velocity RMS & \(RMS_{G,B}\) & 0.034 \({}^{*}\) & 0.094 & 0.311 & 0.217 & 0.034 \({}^{*}\) \\ \hline \multirow{4}{*}{Adopted Definition} & Smoothness & \(J\) & 0.005 \({}^{*}\) & 0.053 & 0.010 \({}^{*}\) & 0.001 \({}^{*}\) & 0.005 \({}^{*}\) \\ \cline{2-7}  & Fluency & \(Fl\) & 0.003 \({}^{*}\) & 0.001 \({}^{*}\) & \textless{}0.001 \({}^{*}\) & 0.004 \({}^{*}\) & 0.003 \({}^{*}\) \\ \cline{2-7}  & Velocity RMS & \(RMS_{G}\) & 0.034 \({}^{*}\) & 0.094 & 0.277 & 0.217 & 0.034 \({}^{*}\) \\ \cline{2-7}  & Jerk RMS & \(RMS_{j}\) & 0.008 \({}^{*}\) & 0.021 \({}^{*}\) & 0.053 & 0.002 \({}^{*}\) & 0.008 \({}^{*}\) \\ \cline{2-7}  & Max Jerk Variation & \(\Delta j\) & 0.049 \({}^{*}\) & 0.068 & 0.985 & 0.019 \({}^{*}\) & 0.049 \({}^{*}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(p\)-values of functional parameter differences between healthy and pathological populations; significant results are reported with (*). Nevertheless, Figures 3 and 4 depict that the subject may still make movements, likely representing postural adjustments, also beyond the end of the focal movement, i.e., beyond the end limit for the estimation of the total task time \(TT\).