Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques (original) (raw)

Towards unobtrusive Parkinson's disease detection via motor symptoms severity inference from multimodal smartphone-sensor data

2019

Objective: To provide clinically-corroborated evidence of the Parkinson's disease (PD) diagnostic potential of machine learning-based approaches for motor symptoms severity inference via multimodal data, passively captured during the natural use of smartphones. Background: PD symptoms can be mild in the early stages and they usually go unnoticed, leaving the disease undiagnosed for years [1]. Subtle motor manifestations may start five to six years prior to PD clinical diagnosis and thereafter progress quickly [2]. Motor impairment affects daily activities and can severely impact patients' quality over the course of the disease. Information derived from mobile electronic sensors can provide, via algorithmic transformation, objective and dense information of an individual's motor status, allowing for frequent relevant symptoms early screening and subsequent monitoring.<br> Methodology: We analyzed longitudinal recordings of tri-axial accelerometer, voice and keystrok...

Deep Learning for Daily Monitoring of Parkinson’s Disease Outside the Clinic Using Wearable Sensors

Sensors

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson’s disease (PD). However, the unsupervised and “open world” nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these “walk-like” events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification ac...

Granular Motor State Monitoring of Free Living Parkinson's Disease Patients via Deep Learning

ArXiv, 2019

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide and affects around 1% of the (60+ years old) elderly population in industrial nations. More than 80% of PD patients suffer from motor symptoms, which could be well addressed if a personalized medication schedule and dosage could be administered to them. However, such personalized medication schedule requires a continuous, objective and precise measurement of motor symptoms experienced by the patients during their regular daily activities. In this work, we propose the use of a wrist-worn smart-watch, which is equipped with 3D motion sensors, for estimating the motor fluctuation severity of PD patients in a free-living environment. We introduce a novel network architecture, a post-training scheme and a custom loss function that accounts for label noise to improve the results of our previous work in this domain and to establish a novel benchmark for nine-level PD motor state estimation.

Cueing of Parkinson's Disease Patients by Standard Smart Devices and Deep Learning Approach

Acta Polytechnica Hungarica, 2023

Parkinson's disease is one of the most common neurological diseases. The patients suffer from different symptoms, e.g., tremor, bradykinesia, or walk gait disorders. One of the gait disorders which affects Parkinson's disease patients is freezing of gait, which shows as a sudden gait interruption without the ability to take the next step. It is hard to manage this symptom by medication. However, there are ways to address this symptom by applying different visual or vibration aids. This work presents a system for automatic detection and cue of freezing of gait events provided by ordinary smart devices. We used a deep learning approach to detect such events automatically. The test results and the doctors' opinions on the practical experience with the patients suggest the benefits of the provided solution.

Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016

The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.

Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization

ArXiv, 2018

Parkinson's Disease (PD) is characterized by disorders in motor function such as freezing of gait, rest tremor, rigidity, and slowed and hyposcaled movements. Medication with dopaminergic medication may alleviate those motor symptoms, however, side-effects may include uncontrolled movements, known as dyskinesia. In this paper, an automatic PD motor-state assessment in free-living conditions is proposed using an accelerometer in a wrist-worn wearable sensor. In particular, an ensemble of convolutional neural networks (CNNs) is applied to capture the large variability of daily-living activities and overcome the dissimilarity between training and test patients due to the inter-patient variability. In addition, class activation map (CAM), a visualization technique for CNNs, is applied for providing an interpretation of the results.

Deep learning Parkinson's from smartphone data

2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2017

The cloudUPDRS app is a Class I medical device, namely an active transient non-invasive instrument, certified by the Medicines and Healthcare products Regulatory Agency in the UK for the clinical assessment of the motor symptoms of Parkinson's Disease. The app follows closely the Unified Parkinson's Disease Rating Scale which is the most commonly used protocol in the clinical study of PD; can be used by patients and their carers at home or in the community; and, requires the user to perform a sequence of iterated movements which are recorded by the phone sensors. This paper discusses how the cloudUPDRS system addresses two key challenges towards meeting essential consistency and efficiency requirements, namely: (i) How to ensure high-quality data collection especially considering the unsupervised nature of the test, in particular, how to achieve firm user adherence to the prescribed movements; and (ii) How to reduce test duration from approximately 25 minutes typically required by an experienced patient, to below 4 minutes, a threshold identified as critical to obtain significant improvements in clinical compliance. To address the former, we combine a bespoke design of the user experience tailored so as to constrain context, with a deep learning approach used to identify failures to follow the movement protocol while at the same time limiting false positives to avoid unnecessary repetition. We address the latter by developing a machine learning approach to personalise assessments by selecting those elements of the UPDRS protocol that most closely match individual symptom profiles and thus offer the highest inferential power hence closely estimating the patent's overall UPRDS score. • Personalised tests reducing the time required to carry out an assessment to less than 4 minutes. These so-called quick tests are created using machine learning to select a subset of UPDRS observations that closely estimate the motor performance of a particular patient.

A Deep Learning Approach for Automatic and Objective Grading of the Motor Impairment Severity in Parkinson’s Disease for Use in Tele-Assessments

Sensors

Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurological conditions such as Parkinson’s disease (PD). This study examined the ability of deep learning approaches to grade the motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. We hypothesized that expanding training datasets with motion data from healthy older adults (HOAs) and initializing classifiers with weights learned from unsupervised pre-training would lead to an improvement in performance when classifying lower vs. higher motor impairment relative to a baseline deep learning model (XceptionTime). This study evaluated the change in classification performance after using expanded training datasets with HOAs and transferring weights from unsupervised pre-training compared to a baseline deep learning model (XceptionTime) using both upper extrem...

Screening of Parkinsonian subtle fine-motor impairment from touchscreen typing via deep learning

Scientific Reports, 2020

Fine-motor impairment (FMI) is progressively expressed in early Parkinson’s Disease (PD) patients and is now known to be evident in the immediate prodromal stage of the condition. The clinical techniques for detecting FMI may not be robust enough and here, we show that the subtle FMI of early PD patients can be effectively estimated from the analysis of natural smartphone touchscreen typing via deep learning networks, trained in stages of initialization and fine-tuning. In a validation dataset of 36,000 typing sessions from 39 subjects (17 healthy/22 PD patients with medically validated UPDRS Part III single-item scores), the proposed approach achieved values of area under the receiver operating characteristic curve (AUC) of 0.89 (95% confidence interval: 0.80–0.96) with sensitivity/specificity: 0.90/0.83. The derived estimations result in statistically significant ($$p<0.05$$ p < 0.05 ) correlation of 0.66/0.73/0.58 with the clinical standard UPDRS Part III items 22/23/31, re...

Deep Learning-Aided Parkinson's Disease Diagnosis from Handwritten Dynamics

2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2016

Parkinson's Disease (PD) automatic identification in early stages is one of the most challenging medicine-related tasks to date, since a patient may have a similar behaviour to that of a healthy individual at the very early stage of the disease. In this work, we cope with PD automatic identification by means of a Convolutional Neural Network (CNN), which aims at learning features from a signal extracted during the individual's exam by means of a smart pen composed of a series of sensors that can extract information from handwritten dynamics. We have shown CNNs are able to learn relevant information, thus outperforming results obtained from raw data. Also, this work aimed at building a public dataset to be used by researchers worldwide in order to foster PD-related research.