Abstract
Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients’ daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients’ daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.
Highlights
The severity of resting and action tremor is analyzed during routine clinical visits using Part III of the UPDRS scale
Once the best classifiers were identified, predictions were made with the whole system and their performances were evaluated by comparing them with the clinical evaluation made by a neurologist according to the UPDRS
Three data representations have been used for the evaluation of this classifier and several classification algorithms have been evaluated such as AdaBoost (100 estimators), Gradient Boost (100 estimators), a Convolutional neural networks (CNNs) model trained with triaxial raw signals (128 samples × 3 channels) as indicated in Section 3.5.1, and two CNN multiout models trained with raw signals and FFT, respectively
Summary
The severity of resting and action tremor is analyzed during routine clinical visits using Part III of the UPDRS scale. While the patient performs these tasks, the maximum amplitude produced by the tremors was analyzed and rated on a scale ranging from 0 to 4 (where 0 implies that there is no presence of tremors and 4 indicates tremors with an amplitude of up to 10 cm). In a similar way to the other sections of the UPDRS, the constancy is qualified on a discrete scale from 0 to 4. This type of evaluation is a widespread method, visits to the specialist are spaced several months apart and often fail to capture the full spectrum of symptoms that the patients with PD may experience in their daily lives [61]. Tools for remote monitoring could help to improve treatments by collecting data in-home settings, reducing the number of clinic visits in situations similar to those produced by the COVID-19 pandemic emergency, where medical appointments have experienced a significant reduction
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