Automatic Resting Tremor Assessment in Parkinson’s Disease Using Smartwatches and Multitask Convolutional Neural Networks

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.

Remote Assessments of Hand Function in Neurological Disorders: Systematic Review

“…However, 28% (21/74) of studies [23,25,36,37,42,46,47,61,63,64,66,[70][71][72][73][74][75][76][77][78][79][80] remotely monitored participants' hand function longitudinally. The duration of the remote monitoring period was 3 days [37] to 3 years [77].…”

“…Approximately 12% (9/74) of studies examining external devices reported high, statistically significant correlations with well-established assessments [19,20,47,50,52,72,73,83,91]. In addition, 8% (6/74) of studies using smartphone assessments [28,49,52,66,79,84] and 1% (1/74) of studies using telerehabilitation [69] found moderate to high, statistically significant correlations with well-established assessments. • Finger dexterity (r=0.9; P<.001) --MDS-UPDRS Ferreira et al [23] -MDS-UPDRS Giancardo et al [39] • Finger tapping (AUC=0.75) -MDS-UPDRS Giuffrida et al [24] • Hand tremor (r=0.…”

A Survey of Human Gait-Based Artificial Intelligence Applications

“…The domain of smart gait devices and environments is exciting, brave, creative, extensive, and ever-growing (See Table 12 ). SG devices include wearable shoes ( Zou et al, 2020 ), socks ( Zhang et al, 2020f ), kneepads and anklets ( Totaro et al, 2017 ), insoles ( Low et al, 2020 ), as well as devices attached to the body, such as smartphones ( Poniszewska-Maranda et al, 2019 ), smartwatches ( San-Segundo et al, 2018 ), ( Sigcha et al, 2021 ), etc., implantable medical devices such as ActiGait ( Sturma et al, 2019 ), wearable robotics ( Shi et al, 2019 ) such as prosthetics ( Gao et al, 2020 ) orthotics ( Zhang et al, 2020e ), ( Choo et al, 2021 ), assistive devices such as smart walkers ( Jimenez et al, 2018 ), and environmental devices such as smart tiles ( Daher et al, 2017 ). SG devices use gait data to facilitate health monitoring, including passive mental health assessment ( Rabbi et al, 2011 ) and transfer data to control devices for health, sports, security, and entertainment applications.…”

A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes

“…A sensor-based system, using embedded triaxial accelerometers from consumer smartwatches and multitask classification models, was presented to assess the amplitude and constancy of resting tremor in PD. The system was based on a deep learning multitask approach combined with the data acquired from PD patients with results showing a high agreement between the amplitude and constancy measurements obtained from a smartwatch in comparison with those obtained in a clinical assessment [37]. A multimodal deep learning model for discriminating between people with PD and without PD is shown using two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder models [38].…”

“…Simultaneous electrocatalytic determination of ascorbic acid (AA), dopamine (DA) and uric acid (UA) [21] Nanosized copper oxide/multiwall carbon nanotubes Electrocatalytic oxidation of dopamine monitoring [23] Microneedle sensing platform Electrochemical monitoring of levodopa (enzymatic-amperometric and nonenzymatic voltammetric detection) [25] Electroanalytical assay using alpha-synuclein modified electrodes a-synuclein detection through autoantibodies sampling [27] Semiconductor quantum dots (CdSe/ZnS) Mitochondrial complex I activity fluorescence monitoring [28] DNA electrochemical biosensor through an imprinted polymer layer fabricated on a gold electrode Nucleic acid degradation products determination (8-hydroxyguanine) [29] Antibody-based biosensor on multiblock nanorods (Au and Ag)/biotinylated aptamers immobilization Dopamine detection [30,31] A segmented double-integration algorithm Calculation of step length and step time from wearable inertial measurement units, spatiotemporal gait parameters measurement [36] Embedded triaxial accelerometers from consumer smartwatches and multitask classification models Assessment of the amplitude and constancy of resting tremor [37] MCPD-Net, a multimodal deep learning model using visions accelerometer sensors Effective representations of human movements prediction [38] mKinetikos, a mobile-based system (mHealth system) Continuous and remote monitoring of PD patients' functional mobility and global clinical status [39] Flexible wearable sensors attached to the hands, arms and thighs Detection of bradykinesia and tremor in the upper extremities [40]…”