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The paper presents a novel sensor-based disease symptoms evaluation method which can be applied in the domain of neurological treatment monitoring and efficiency analysis. The main purpose of the method is to provide a quantitative approach for symptoms recognition and their intensity, which can be used for efficient medicine intake planning for Parkinson's Disease patients. This work presents an innovative method, which enables to objectify the process of clinical trials. The developed solution implements sensor data fusion method, which analyses time correlated wearable sensor biomedical data and symptoms survey. We have merged two separate methods of recognizing and assessing the intensity of Parkinson's Disease (PD) symptoms using time-constrained survey as well as sensor and interaction-based algorithms, which enable to objectively assess the intensity of disease symptoms. Based on process-based analysis and clinical trials observations, a set of requirements for validating symptoms of neurological diseases have been formulated. Proposed solution concentrates on PD indicators connected with arms movement and mental reaction delays, which can be registered using wearable sensors. Since 2017 the tool has been tested by a group of four selected neurologists and 10 users, 3 of which are PD patients. To meet the project's supplementary (efficiency, security) requirements, a test clinical trial has been performed involving 3 patients executing trials which lasted two weeks and was supported by the continuous application usage. After successful deployment the method and software tools has been presented for commercial use and further development in order to adjust its usage for other neurological disorders.

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An Algorithmic Approach for Quantitative Evaluation of Parkinson&#x2019;s Disease Symptoms and Medical Treatment Utilizing Wearables and Multi-Criteria Symptoms Assessment