## 9 Conclusions  
The availability of mobile phones for monitoring the state of PD patients can have a profound impact on clinical practice by giving physicians access to long-term data. This additional data can help doctors to gain a fuller and more objective understanding of symptoms and fluctuations in symptoms of their patients, therefore enabling more accurate diagnoses and treatment regimens.  
As a result of this work, we determined that the proposed neural network was able to generalize data on the angles of rotation and tilt of a mobile phone, in order to identify PD symptoms. A comparative analysis of two methods for constructing neural network architectures was considered and carried out. Adding data on voice parameters in combination with other parameters to the analysis system of a patient with Parkinson's disease and applying a semi-automated approach to building a neural network gave the best result. Neural networks were built in Python using the TensorFlow [31] and Keras libraries [32].  
It was found that the use of LSTM, instead of GRU, blocks can lead to higher accuracy for the neural network. Future research will focus on building neural networks with more parameters and on a larger sample of initial data, which will lead to the better training of the neural network, such that it may return even more accurate diagnosis results. To train and evaluate the constructed neural networks, we used preliminary assessments of the states of the patients. These estimates were given by the patients themselves, and are not always correct assessments. This is a problem that we are contemplating how to solve.  
Much more data--both in terms of volume and number of parameters--are needed to more accurately determine the robustness of machine learning and smartphone tests, in terms of these confounding factors.  
Y.S.: Conceptualization, Methodology, Writing--Original Draft; Y.I.: Evaluation of graphs, Testing; E.S.: Software, Formal analysis, Visualization; A.d.J.P.S.: Data pre-processing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Higher Education of the Russian Federation by the Agreement No 075-15-2020-933 dated 13 November 2020, on the provision of a grant in the form of subsidies from the federal budget for the implementation of state support for the establishment and development of the world-class scientific center: Pavlov center <<Integrative physiology for medicine, high-tech healthcare, and stress-resilience technologies>>.  
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved at the meeting of the Academic Council of the N.P.Bechtereva Institute of the Human Brain of the Russian Academy of Sciences dated 17 September 2015. (No. 29).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.