Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study (original) (raw)
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The aim of this study is to accurately distinguish Parkinson's disease (PD) participants from healthy controls using self-administered tests of gait and postural sway. Using consumer-grade smartphones with in-built accelerometers, we objectively measure and quantify key movement severity symptoms of Parkinson's disease. Specifically, we record tri-axial accelerations, and extract a range of different features based on the time and frequencydomain properties of the acceleration time series. The features quantify key characteristics of the acceleration time series, and enhance the underlying differences in the gait and postural sway accelerations between PD participants and controls. Using a random forest classifier, we demonstrate an average sensitivity of 98.5% and average specificity of 97.5% in discriminating PD participants from controls.
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
The aim of this study is to accurately distinguish Parkinson's disease (PD) participants from healthy controls using self-administered tests of gait and postural sway. Using consumer-grade smartphones with in-built accelerometers, we objectively measure and quantify key movement severity symptoms of Parkinson's disease. Specifically, we record tri-axial accelerations, and extract a range of different features based on the time and frequencydomain properties of the acceleration time series. The features quantify key characteristics of the acceleration time series, and enhance the underlying differences in the gait and postural sway accelerations between PD participants and controls. Using a random forest classifier, we demonstrate an average sensitivity of 98.5% and average specificity of 97.5% in discriminating PD participants from controls.
Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data
ArXiv, 2020
Clinical methods that assess gait in Parkinson's Disease (PD) are mostly qualitative. Quantitative methods necessitate costly instrumentation or cumbersome wearable devices, which limits their usability. Only few of these methods can discriminate different stages in PD progression. This study applies machine learning methods to discriminate six stages of PD. The data was acquired by low cost walker-mounted sensors in an experiment at a movement disorders clinic and the PD stages were clinically labeled. A large set of features, some unique to this study are extracted and three feature selection methods are compared using a multi-class Random Forest (RF) classifier. The feature subset selected by the Analysis of Variance (ANOVA) method provided performance similar to the full feature set: 93% accuracy and had significantly shorter computation time. Compared to PCA, this method also enabled clinical interpretability of the selected features, an essential attribute to healthcare ap...
Journal of NeuroEngineering and Rehabilitation
Background Parkinson’s disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether balance and gait variables obtained with wearable inertial motion sensors can be utilized to differentiate between PD and ET using machine learning. Additionally, we compared classification performances of several machine learning models. Methods This retrospective study included balance and gait variables collected during the instrumented stand and walk test from people with PD (n = 524) and with ET (n = 43). Performance of several machine learning techniques including neural networks, support vector machine, k-nearest neighbor, decision tree, random forest, and gradient boosting, were compared with a dummy model or logistic regression using F1-scores. Results Machine learning models classifi...
Parkinson's disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether gait and balance variables obtained with wearable sensors can be utilized to differentiate between PD and ET using machine learning techniques. Additionally, we compared classification performances of several machine learning models. A balance and gait data set collected from 567 people with PD or ET was investigated. Performance of several machine learning techniques including neural networks (NN), support vector machine (SVM), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), and gradient boosting (GB), were compared using F1-scores. Machine learning models classified PD and ET based on balance and gait characteristics better than chance or logistic regression. The highest F1-score ...
Building a Machine-Learning Framework to Remotely Assess Parkinson's Disease Using Smartphones
IEEE Transactions on Biomedical Engineering, 2020
Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semicontinuously, track disease fluctuations, and avoid rater dependency. Methods: Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale crossmodality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization. Results: Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD. Significance: Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD.
Using mobile phones for activity recognition in Parkinson's patients
Frontiers in neurology, 2012
Mobile phones with built-in accelerometers promise a convenient, objective way to quantify everyday movements and classify those movements into activities. Using accelerometer data we estimate the following activities of 18 healthy subjects and eight patients with Parkinson's disease: walking, standing, sitting, holding, or not wearing the phone. We use standard machine learning classifiers (support vector machines, regularized logistic regression) to automatically select, weigh, and combine a large set of standard features for time series analysis. Using cross validation across all samples we are able to correctly identify 96.1% of the activities of healthy subjects and 92.2% of the activities of Parkinson's patients. However, when applying the classification parameters derived from the set of healthy subjects to Parkinson's patients, the percent correct lowers to 60.3%, due to different characteristics of movement. For a fairer comparison across populations we also applied subject-wise cross validation, identifying healthy subject activities with 86.0% accuracy and 75.1% accuracy for patients. We discuss the key differences between these populations, and why algorithms designed for and trained with healthy subject data are not reliable for activity recognition in populations with motor disabilities.
npj Parkinson's Disease
Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson’s Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random Forest algorithms were utilised together with different routines of automatic feature selection or factorisation, resulting in seven different machine learning algorithms to estimate the clinical rati...
Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring
Sensors
Parkinson’s disease is a neurodegenerative disorder impacting patients’ movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population’s movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson’s disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using...
Hawaiian International Conference on System Sciences, 2019
Parkinson’s Disease is a worldwide health problem, causing movement disorder and gait deficiencies. Automatic noninvasive techniques for Parkinson's disease diagnosis is appreciated by patients, clinicians and neuroscientists. Gait offers many advantages compared to other biometrics specifically when data is collected using wearable devices; data collection can be performed through inexpensive technologies, remotely, and continuously. In this study, a new set of gait features associated with Parkinson’s Disease are introduced and extracted from accelerometer data. Then, we used a feature selection technique called maximum information gain minimum correlation (MIGMC). Using MIGMC, features are first reduced based on Information Gain method and then through Pearson correlation analysis and Tukey post-hoc multiple comparison test. The ability of several machine learning methods, including Support Vector Machine, Random Forest, AdaBoost, Bagging, and Naïve Bayes are investigated across different feature sets. Similarity Network analysis is also performed to validate our optimal feature set obtained using MIGMC technique. The effect of feature standardization is also investigated. Results indicates that standardization could improve all classifiers’ performance. In addition, the feature set obtained using MIGMC provided the highest classification performance. It is shown that our results from Similarity Network analysis are consistent with our results from the classification task, emphasizing on the importance of choosing an optimal set of gait features to help objective assessment and automatic diagnosis of Parkinson’s disease. Results illustrate that ensemble methods and specifically boosting classifiers had better performances than other classifiers. In summary, our preliminary results support the potential benefit of accelerometers as an objective tool for diagnostic purposes in PD.