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In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ON TM , a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ON TM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ON TM are presented., Competing Interests: DR-M, JCab, CP-L, AC, AS, JCal, MP, and AR-M are shareholders of Sense4Care, the company that markets STAT-ON. DR-M, JCal, MP, and CC are employed by Sense4Care., (Copyright © 2022 Rodríguez-Martín, Cabestany, Pérez-López, Pie, Calvet, Samà, Capra, Català and Rodríguez-Molinero.)

[This corrects the article on p. 431 in vol. 8, PMID: 28919877.].

Background: Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson's (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III)., Method: Seventy-five patients suffering from Parkinson's disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient's home. Convergence between the algorithm and the scale was evaluated by using the Spearman's correlation coefficient., Results: Correlation with the UPDRS-III was moderate (rho -0.56; p  < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho -0.73; p  < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: "axial function, balance, and gait." The correlation between the algorithm outputs and this factor of the UPDRS-III was -0.67 ( p  < 0.01)., Conclusion: The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson's disease and motor fluctuations.