Parkinson’s Disease (PD) is a neurodegenerative disease associated with the extrapyramidal motor system disorder currently being the second most common neurodegenerative disorder. The first clinical symptoms can manifest themselves long before the retirement age and inevitably lead to reducing the possibility of continuing work. However, PD is sometimes misdiagnosed. In this article, we discuss the typical misdiagnosed cases and propose a second opinion system based on wearable sensors and artificial intelligence. For this reason, we designed a number of common tasks and recorded the movement data using wearable sensors worn by individuals with PD and other extrapyramidal disorders. PD patients are differentiated against other patients with similar diseases and not against healthy subjects. This allows one to measure the true specificity of wearable technologies with regard to detecting PD. Data analysis and classification of the types of tremor using machine learning methods (feature extraction, dimensionality reduction, classification) helps significantly improve the accuracy of PD diagnosis. Our results show that, when considering bradykinesia and tremor together, the accuracy of distinguishing PD from similar diseases increases (f1 score 0.88). This closed-loop configuration makes it possible to tune exercises to maximize the diagnostic value of the entire routine. We report approbation on 56 patients.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder that affects the extrapyramidal motor system. The initial clinical symptoms can appear long before the retirement age, affecting the patients’ ability to continue their work. However, the modern healthcare lacks the apparatus to detect the early signs of the disease, with only selected experts being able to spot the onset. Moreover, even if the disease is detected on time, the current clinical routine has no means of monitoring the progression of the disease, nor the effect of the therapy. In this work, we perform a comparative study of three patient-driven monitoring approaches, including wearable sensors data, video of the routine exercises performed by the patients, and their handwriting – all fine-tuned to detect PD. Aspiring to create an inexpensive clinical workflow, we collect the sensor data using a commercial wearable wireless sensor, and the video and the handwriting data are recorded using a basic smartphone. In the analysis stage, we rely on machine and deep learning methods, using f1 score for gauging the performance of the patient-driven data acquisition approaches. The sensor data analysis demonstrates the best f1 score of 0.93. At the same time, the handwriting and remote video recordings are more comfortable and less time-consuming, offering a sensible trade-off for the patients at the later stages of PD.

Our society exhibits a worldwide trait of a quickly growing cohort of patients with neurodegenerative diseases, such as Parkinson’s disease (PD). According to the analysts, there is a plausible “PD pandemic” to occur within the next two decades. Nowadays, the research in the area focuses on how to detect, predict, or classify PD and similar diseases without addressing the point of what activities or exercises a subject should do to improve the performance of these tasks. In this article, we propose a method based on machine learning (ML) and wearable sensors to identify the optimal exercises for the efficient detection of PD in patients. We first define 15 common tasks that are typically used to diagnose PD in modern clinical practice. However, these exercises still carry a high risk of misdiagnosis and, moreover, not all of them work well in the scope of existing ML solutions to support the diagnosis. Herein, we collect the data in a real clinical setting using a compact wearable wireless sensor node entailing a board gyroscope, accelerometer, and magnetometer. Application of ML methods to the collected data reveals three “most efficient” exercises to assist diagnosticians with the highest discriminating power (0.9 ROC AUC in each task). The proposed solution can be implemented as a medical decision support system for real-time PD diagnostics.

Parkinson’s Disease (PD) is currently the fastest growing neurodegenerative disease. It decreases the quality of life for patients, especially when not diagnosed properly and timely. Accurate diagnostic of PD is complicated by the fact that there exist several neurodegenerative diseases with similar motor symptoms, e.g. essential tremor. In this work, we report on a second opinion system based on the video analysis and classification of subjects using machine learning methods including feature extraction, dimensionality reduction and classification. Our approach serves for avoiding a typical misdiagnosis of PD by essential tremor. Consequently, we designed 15 common tasks and recorded the movement video. Video data was collected from 89 subjects at a medical center and labeled by doctors. We first demonstrate classification between the healthy subjects and subjects with PD suspected case followed by the classification between the subjects with true PD and the subjects with essential tremor. We achieved f1 score 0.90 for the first classification and f1 score 0.84 for the second classification. The proposed unobtrusive approach demonstrated its feasibility through a pilot study. It opens up wide vista for differentiating PD patients against other patients and not against a cohort of healthy subjects.

Machine Learning (ML) algorithms is an emerging tool helping automatically solve detection and classification tasks in myriads of applications. ML is extensively used in medical applications and, in particular, for detecting the Parkinson’s Disease (PD). However, the inference is typically made relying on a single data source. This work explores the results of combining more than one data source type for the diagnosis of PD. Data from the Commercial Off-the-Shelf (COTS) wearable sensors (accelerometer, gyroscope and magnetometer), along with video recordings from 83 patients completing a series of 15 tasks was analyzed with the use of ML methods. Statistical and frequency features were extracted and used to train Random Forest and XGBoost Classifiers. We investigate two use cases on classifying (i) healthy individuals and individuals with the PD, and (ii) PD and essential tremor. The experiment showed that using both the data from wearable sensors and video provided the increase of f1 score up to 18% for differentiating between healthy and PD classes and 21% for differentiating PD and essential tremor classes. At the same time, usage of COTS and ML opens wide vista for patient driven data acquisition and healthcare.

Immunosenescence is a process of remodeling the immune system under the influence of chronic inflammation during aging. Parkinson’s disease (PD) is a common age-associated neurodegenerative disorder and is frequently accompanied by neuroinflammation. On the other hand, cytomegalovirus (CMV), one of the most spread infections in humans, may induce chronic inflammation which contributes to immunosenescence, differentiation and the inflation of T cells and NK cells. Currently, there is no clear understanding of immunosenescence severity in PD patients infected with CMV. In this study, we analyzed differentiation stages and immunosenescence characteristics of T cells and NK cells in 31 patients with mild and moderate PD severity, 33 age-matched and 30 young healthy donors. The PD patients were 100% CMV-seropositive compared to 76% age-matched and 73% young CMV-infected healthy donors. The proportion of effector memory T cells re-expressing CD45RA, CD57⁺CD56⁻ T cells and CD57⁺CD56⁺ T cells was significantly reduced in PD patients compared with CMV-seropositive age-matched healthy individuals. The CD57⁺CD56⁻ T cell proportion in PD patients was similar to that of CMV-seropositive young healthy donors. Thus, PD is characterized by reduced peripheral blood T cell immunosenescence, even against the background of CMV infection.