Smartphone application for classification of motor impairment severity in Parkinson's disease

Abstract

Advanced hardware components embedded in modern smartphones have the potential to serve as widely available medical diagnostic devices, particularly when used in conjunction with custom software and tested algorithms. The goal of the present pilot study was to develop a smartphone application that could quantify the severity of Parkinson's disease (PD) motor symptoms, and in particular, bradykinesia. We developed an iPhone application that collected kinematic data from a small cohort of PD patients during guided movement tasks and extracted quantitative features using signal processing techniques. These features were used in a classification model trained to differentiate between overall motor impairment of greater and lesser severity using standard clinical scores provided by a trained neurologist. Using a support vector machine classifier, a classification accuracy of 0.945 was achieved under 6-fold cross validation, and several features were shown to be highly discriminatory between more severe and less severe motor impairment by area under the receiver operating characteristic curve (AUC > 0.85). Accurate classification for discriminating between more severe and less severe bradykinesia was not achieved with these methods. We discuss future directions of this work and suggest that this platform is a first step toward development of a smartphone application that has the potential to provide clinicians with a method for monitoring patients between clinical appointments.

Quantitative Measurement of Akinesia in Parkinson's Disease

“…Most of those systems are relatively invasive and time consuming and do not differentiate normal movement from tremor and high activity from dyskinesia . In the past few years, great interest has arisen in the use of smartphones application for the evaluation and treatment of patients with PD …”

“…[13][14][15][16][17][18][19][20][21][22][23][24] In the past few years, great interest has arisen in the use of smartphones application for the evaluation and treatment of patients with PD. [25][26][27][28][29][30] We developed a system for the evaluation of different features of tapping, such as rhythmicity, regularity of pressure, and speed of tapping (represented by the number of taps per 20 seconds), which are features of movement-related akinesia. Changes in regularity were greater while tapping, but the reduction of the number of taps was greater while "typing," suggesting that several tests should be performed to test different types of patients who have different degrees of severity.…”

Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial

“…Several pilot research studies have successfully developed smartphone applications for PD . In these pilot studies, proof of concept was typically established in a clinical setting by demonstrating significant differences between individuals with PD and healthy controls, and/or significant relationships between the sensor‐based measures and the International Parkinson and Movement Disorder Society–Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) clinical gold standard . For example, Kassavetis and colleagues tested 14 PD participants (mean disease duration = 3.7 years) with the MDS‐UPDRS and a custom Android application with the following active tests: resting, postural and kinetic tremor, pronation‐supination, leg agility, and finger‐tapping.…”

“…1,[9][10][11][12][13][14][15][16][17] In these pilot studies, proof of concept was typically established in a clinical setting by demonstrating significant differences between individuals with PD and healthy controls, and/or significant relationships between the sensor-based measures and the International Parkinson and Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) clinical gold standard. 14,[18][19][20][21][22][23][24] For example, Kassavetis and colleagues 18 tested 14 PD participants (mean disease duration 5 3.7 years) with the MDS-UPDRS 25 and a custom Android application with the following active tests: resting, postural and kinetic tremor, pronation-supination, leg agility, and fingertapping. For all tasks, the extracted sensor feature data significantly correlated with corresponding MDS-UPDRS 25 item scores (e.g., item 3.17, rest tremor amplitude).…”

The Emerging Wearable Solutions in mHealth

“…Through progressively miniaturized, smartphones are equipped with comparatively advanced sensing capabilities (i.e., accelerometer, gyroscope, magnetometer, camera, and many more) and powerful computing capabilities, making it the ideal platform for remote health monitoring without the extra expense of purchasing and inconvenience of using dedicated wearables. As a result, smartphone-based solutions have emerged most recently for fall detection and prevention [6], activity recognition [7], Parkinson's disease (PD) assessment [8], and cardiac rhythm measurement in mHealth.…”