## 6 Conclusions  
This article explores the use of wearable sensors to extract digital biomarkers to assess motor symptoms of Parkinson's disease (PD). Findings show these digital biomarkers can accurately track severity symptoms, monitor the response to treatment, and distinguish between patients with PD (PwPD) and healthy controls. Although these tools can provide objective and continuous information for improved monitoring, findings highlight the importance of standardized data collection and analysis methods to ensure reliable integration into clinical practice. Among the reviewed studies, a total of 25 different devices (20 out of 22 articles used a single type of device) were used for developing digital biomarkers for motor symptoms of PD (DB-MS-PD), with the inertial measurement units (IMU) being the most commonly used, followed by smartphones. In terms of sensor types, the most frequently used sensors were triaxial accelerometers and gyroscopes. DB-MS-PD were extracted primarily through three approaches: combining multiple features with machine learning (ML), deep learning (DL) algorithms, or evaluating single features using statistical methods. To extract these digital biomarkers, different motor tasks were assessed, with finger tapping and gait being the most frequent motor activities. Finger tapping, particularly in terms of tap intervals, spatial accuracy, and total taps, consistently stands out as a significant indicator for the diagnosis and monitoring of PD progression. Performance metrics indicate that finger tapping tasks, which involve active participation, are effective for both laboratory and home environments, with accuracies ranging from 0.75 to 0.84 and area under the curve (AUC) values between 0.74 and 0.95. Gait analysis, on the other hand, using measurements such as speed, variability, and stride length was also important to assess motor function in both supervised and unsupervised contexts. Furthermore, the results indicate that gait tasks are similarly effective in the diagnosis of PD and in evaluating treatment responses. AUC values for gait analysis range from 0.76 to 0.98, with the highest accuracy (0.94 to 0.98) observed in controlled laboratory settings. The use of wearable devices can provide many advantages for traditional clinical monitoring and assessment, as they can generate a high volume of data in a relatively short time, provide objective opinions on the patient's actual condition, or be able to assess or account for aspects that traditionally have not been possible, such as falls or freezing. In addition, they introduce an unexploited aspect, such as the possibility of monitoring a patient in a home environment without the pressure of feeling observed by the neurologist. However, in order to develop remote patient monitoring systems, several issues identified in the review need to be addressed. One of the most relevant challenges is the standardization of data collection, analysis, processing, evaluation, and reporting of DB-MS-PD in order to facilitate comparability and replicability of results. In the absence of general guidelines for experimental development, each trial can be conducted in a different way and managed differently. This idea of standardization has been pursued in other fields such as neuroimaging in Alzheimer [90] or in the use of public tools for database publication [91]. The review highlighted multiple issues and constraints within the included studies, offering suggestions for future research to overcome these limitations and improve PD assessment. Rapid progress in sensing and data analysis technologies is expected to significantly expedite the integration of wearable devices in this field. These DB-MS-PD can be used to obtain objective and measurable information on the status of certain disease symptoms and could be used in continuous patient monitoring systems. Conceptualization: C.P.F., L.S. and I.P. ; Data curation: C.P.F. ; Formal analysis: C.P.F., L.S., L.B. and I.P. ; Funding acquisition: C.A., J.M.L., G.A.A. and I.P.