Abstract
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.
Highlights
One of the crucial problems for people suffering from Parkinson’s disease is the difficulty to precisely adjust and tune pharmaceutical treatment involving both dosage and intake frequency [19]
These form a vector (11) used for machine learning of length dependent on nc – number of last doses considered for prediction
Based on the results collected during the IQPharma CTA application testing phase it was possible to compare and correlate patient’s health state reported in conventional surveys with the results of reasoning, based on wearable sensor captured movement and muscle activity
Summary
One of the crucial problems for people suffering from Parkinson’s disease is the difficulty to precisely adjust and tune pharmaceutical treatment involving both dosage and intake frequency [19]. Chmielewski: Algorithmic Approach for Quantitative Evaluation of Parkinson’s Disease Symptoms trials [25] and their methodology have been constructed as a process of testing new drugs in comparison to reference pharmaceuticals and placebo products, the main problem is the subjective evaluation of the drug effects made by patients. Developed mobile system is using wearable sensors and exercises to assist clinical trials in order to supplement used assessments with quantitative approach evaluating registered symptoms Such approach is a supplement for patient’s subjective evaluation of health state. For the purpose of the research presented in the paper sensor data has been collected from 3 selected template patients (P1, P2 and P3 on Fig. 3) experiencing the Parkinson’s tremor of the Medical Center Pratia in Warsaw based on informed consent. The whole process, since the registration to completion of first examination is presented using a sequence diagram on Fig. 4
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