# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 6 Conclusions  
The aim of this study is to propose a system for monitoring PD subjects during daily life, in order to evaluate how responses to medication change over time and to allow doctors to optimize medical therapy for each patient, improving his/her quality of life.  
Figure 7: Bland–Altman plots for: (**a**) bout count; (**b**) step count. The difference (D) between the algorithm and the video recording as the y–value, and the mean (M) between the two measurements as the x–value. While solid line in each plot represents the mean count difference between the algorithm and video, dashed lines represent 95% limits of agreement.  
This work presents a system composed of a single wearable sensor placed on the lower back and an algorithm for real-time gait parameters monitoring in PD subjects. In order to evaluate the performance of the proposed system, the developed algorithm was tested in an experimental protocol in which PD subjects performed tasks in constrained and unconstrained environments. In the laboratory scenario, several tasks in a constrained environment were carried out by seven PD subjects and a comparison between the steps detected by the algorithm and steps detected by video recording was performed. Considering every walking bout performed by each subject, we obtained a percentage error low of 2% and high accuracy in terms of sensitivity and specificity, 99.13% and 100%, respectively. Moreover, the proposed algorithm was tested in an unsupervised environment such as the home scenario where the same seven PD subjects have carried out their common ADLs during the day. In the home scenario, the results revealed high reliability, usability and accordance with the video recordings and diaries. In fact, the algorithm successfully detected walking bouts and their respective step counts during daily life. These results confirm the accuracy in the use of a single wearable sensor placed on the lower back, and the accuracy of the proposed algorithm for free-living gait analysis. In fact, the algorithm presented in this work has achieved good results for successfully detecting the walking bouts and the steps both in constrained and unsupervised environments. Although a small group was examined in the experimental protocol, the preliminary results obtained in this work allow us to verify the reliability of the parameters extracted from the proposed algorithm. The analysis of these extracted parameters as a function of time allows us to evaluate how the subject responds to the medication administered by the doctor and to detect the on/off states. Therefore, this work is the beginning of a wide study to develop a processing algorithm for the creation of a clinical and home tool for ADL monitoring in PD subjects but is essential before proceeding with the identification of the subject's on/off states. In fact, future work will focus on tracking how motor symptoms and their responses to medication change over time for on/off states identification.  
Conceptualization, P.P., L.P., A.B. and S.R.; methodology, P.P. and A.B.; software, L.P. and S.R.; validation, P.P., L.P., A.B., O.B. and S.R.; formal analysis, A.B. and L.P.; investigation, P.P. and L.P.; data curation, S.R., L.P. and M.P.; writing--original draft preparation, S.R.; writing--review and editing, S.R., A.B., M.P. and O.B.; visualization, A.B., O.B. and L.P.; supervision, P.P., M.P., O.B. and L.P.; project administration, P.P. and L.P.; funding acquisition, P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work is supported by Marche Region in implementation of the financial programme FOR MARCHE FESR 2014-2020, project \"Miracle\" (Marche Innovation and Research fAcilities for Connected and sustainable Living Environments), CUP B28I19000330007.  
The authors declare no conflict of interest.