Sang Kyong Kim

Unconstrained detection of freezing of Gait in Parkinson's disease patients using smartphone

Freezing of gait (FOG) is a common motor impairment to suffer an inability to walk, experienced by Parkinsons disease (PD) patients. FOG interferes with daily activities and increases fall risk, which can cause severe health problems. We propose a novel smartphone-based system to detect FOG symptoms in an unconstrained way. The feasibility of single device to sense gait characteristic was tested on the various body positions such as ankle, trouser pocket, waist and chest pocket. Using measured data from accelerometer and gyroscope in the smartphone, machine learning algorithm was applied to classify freezing episodes from normal walking. The performance of AdaBoost.M1 classifier showed the best sensitivity of 86% at the waist, 84% and 81% in the trouser pocket and at the ankle respectively, which is comparable to the results of previous studies.

Correction: Automatic Classification of Tremor Severity in Parkinson’s Disease Using a Wearable Device. Sensors 2017, 17, 2067

Hyoseon Jeon 1, Woongwoo Lee 2 ID , Hyeyoung Park 2, Hong Ji Lee 1, Sang Kyong Kim 1, Han Byul Kim 1, Beomseok Jeon 2 and Kwang Suk Park 3,* 1 The Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 03080, Korea; nulpurunhs@bmsil.snu.ac.kr (H.J.); hongjidan@bmsil.snu.ac.kr (H.J.L.); skkim@bmsil.snu.ac.kr (S.K.K.); hahanbyul@bmsil.snu.ac.kr (H.B.K.) 2 Department of Neurology and Movement Disorder Center, Seoul National University Hospital, Seoul 03080, Korea; w2pooh@daum.net (W.L.); 0907bluelove@naver.com (H.P.); brain@snu.ac.kr (B.J.) 3 Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 03080, Korea * Correspondence: pks@bmsil.snu.ac.kr; Tel.: +82-2-2072-3135; Fax: +82-2-3676-2821

Automatic Classification of Tremor Severity in Parkinson’s Disease Using a Wearable Device

Although there is clinical demand for new technology that can accurately measure Parkinsonian tremors, automatic scoring of Parkinsonian tremors using machine-learning approaches has not yet been employed. This study aims to fill this gap by proposing machine-learning algorithms as a way to predict the Unified Parkinson’s Disease Rating Scale (UPDRS), which are similar to how neurologists rate scores in actual clinical practice. In this study, the tremor signals of 85 patients with Parkinson’s disease (PD) were measured using a wrist-watch-type wearable device consisting of an accelerometer and a gyroscope. The displacement and angle signals were calculated from the measured acceleration and angular velocity, and the acceleration, angular velocity, displacement, and angle signals were used for analysis. Nineteen features were extracted from each signal, and the pairwise correlation strategy was used to reduce the number of feature dimensions. With the selected features, a decision tree (DT), support vector machine (SVM), discriminant analysis (DA), random forest (RF), and k-nearest-neighbor (kNN) algorithm were explored for automatic scoring of the Parkinsonian tremor severity. The performance of the employed classifiers was analyzed using accuracy, recall, and precision, and compared to other findings in similar studies. Finally, the limitations and plans for further study are discussed.

Distance estimation from acceleration for quantitative evaluation of Parkinson tremor

The purpose of this paper is to assess Parkinson tremor estimating actual distance amplitude. We propose a practical, useful and simple method for evaluating Parkinson tremor with distance value. We measured resting tremor of 7 Parkinson Disease (PD) patients with triaxial accelerometer. Resting tremor of participants was diagnosed by Unified Parkinsons Disease Rating Scale (UPDRS) by neurologist. First, we segmented acceleration signal during 7 seconds from recorded data. To estimate a displacement of tremor, we performed double integration from the acceleration. Prior to double integration, moving average method was used to reduce an error of integral constant. After estimation of displacement, we calculated tremor distance during 1s from segmented signal using Euclidean distance. We evaluated the distance values compared with UPDRS. Averaged moving distance during 1 second corresponding to UPDRS 1 was 11.52mm, that of UPDRS 2 was 33.58mm and tremor distance of UPDRS 3 was 382.22 mm. Estimated moving distance during 1s was proportional to clinical rating scale — UPDRS.

Clinicians’ Tendencies to Under-Rate Parkinsonian Tremors in the Less Affected Hand

The standard assessment method for tremor severity in Parkinson’s disease is visual observation by neurologists using clinical rating scales. This is, therefore, a subjective rating that is dependent on clinical expertise. The objective of this study was to report clinicians’ tendencies to under-rate Parkinsonian tremors in the less affected hand. This was observed through objective tremor measurement with accelerometers. Tremor amplitudes were measured objectively using tri-axis-accelerometers for both hands simultaneously in 53 patients with Parkinson’s disease during resting and postural tremors. The videotaped tremor was rated by neurologists using clinical rating scales. The tremor measured by accelerometer was compared with clinical ratings. Neurologists tended to under-rate the less affected hand in resting tremor when the contralateral hand had severe tremor in Session I. The participating neurologists corrected this tendency in Session II after being informed of it. The under-rating tendency was then repeated by other uninformed neurologists in Session III. Kappa statistics showed high inter-rater agreements and high agreements between estimated scores derived from the accelerometer signals and the mean Clinical Tremor Rating Scale evaluated in every session. Therefore, clinicians need to be aware of this under-rating tendency in visual inspection of the less affected hand in order to make accurate tremor severity assessments.

Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network

Tremor is a commonly observed symptom in patients of Parkinsons disease (PD), and accurate measurement of tremor severity is essential in prescribing appropriate treatment to relieve its symptoms. We propose a tremor assessment system based on the use of a convolutional neural network (CNN) to differentiate the severity of symptoms as measured in data collected from a wearable device. Tremor signals were recorded from 92 PD patients using a custom-developed device (SNUMAP) equipped with an accelerometer and gyroscope mounted on a wrist module. Neurologists assessed the tremor symptoms on the Unified Parkinsons Disease Rating Scale (UPDRS) from simultaneously recorded video footages. The measured data were transformed into the frequency domain and used to construct a two-dimensional image for training the network, and the CNN model was trained by convolving tremor signal images with kernels. The proposed CNN architecture was compared to previously studied machine learning algorithms and found to outperform them (accuracy = 0.85, linear weighted kappa = 0.85). More precise monitoring of PD tremor symptoms in daily life could be possible using our proposed method.

High-accuracy automatic classification of Parkinsonian tremor severity using machine learning method.

MOTIVATION Although clinical aspirations for new technology to accurately measure and diagnose Parkinsonian tremors exist, automatic scoring of tremor severity using machine learning approaches has not yet been employed. OBJECTIVE This study aims to maximize the scientific validity of automatic tremor-severity classification using machine learning algorithms to score Parkinsonian tremor severity in the same manner as the unified Parkinsons disease rating scale (UPDRS) used to rate scores in real clinical practice. APPROACH Eighty-five PD patients perform four tasks for severity assessment of their resting, resting with mental stress, postural, and intention tremors. The tremor signals are measured using a wristwatch-type wearable device with an accelerometer and gyroscope. Displacement and angle signals are obtained by integrating the acceleration and angular-velocity signals. Nineteen features are extracted from each of the four tremor signals. The optimal feature configuration is decided using the wrapper feature selection algorithm or principal component analysis, and decision tree, support vector machine, discriminant analysis, and k-nearest neighbour algorithms are considered to develop an automatic scoring system for UPDRS prediction. The results are compared to UPDRS ratings assigned by two neurologists. MAIN RESULTS The highest accuracies are 92.3%, 86.2%, 92.1%, and 89.2% for resting, resting with mental stress, postural, and intention tremors, respectively. The weighted Cohens kappa values are 0.745, 0.635 and 0.633 for resting, resting with mental stress, and postural tremors (almost perfect agreement), and 0.570 for intention tremors (moderate). SIGNIFICANCE These results indicate the feasibility of the proposed system as a clinical decision tool for Parkinsonian tremor-severity automatic scoring.

CHANGES IN BILATERAL PHASE SYNCHRONIZATION IN PARKINSONIAN TREMOR RELATED TO AMPLITUDE DIFFERENCE

Tremor, which is a manifestation of the Parkinsons disease, is rhythmic and involuntary oscillation in the frequency range from 3 to 8 Hz. Physiological mechanisms of parkinsonian tremor have not been clearly revealed even though there have been many related studies. In this study, we attempted to analyze bilateral phase synchronization between both hands in order to interpret parkinsonian tremor dynamics which can be helpful to speculate the mechanisms of parkinsonian tremor. Eighteen subjects with Parkinsons disease participated in this study. Tremor was measured for 30 seconds by three axis accelerometer placed over the middle finger with sampling frequency of 64Hz and 12 bit A/D converter while subjects were resting on a chair and relaxing both hands on their knees. Three kinds of synchronization indexes, and , were employed to assess the synchronization strength for tremor between both hands. As a result, when bilateral difference of tremor amplitude became larger than specific value, phase synchronization strength was significantly increased. Therefore, we may suppose that the dynamics of parkinsonian tremor have two modes of the non-phase synchronization and phase synchronization between both limbs.

Measurement System of Hand Tapping Capacity for Quantitative Diagnosis of Parkinson’s Disease

The Unified Parkinson’s Disease Rating Scale (UPDRS) is commonly used to measure the patients’ disorder in clinical therapeutics. However, UPDRS scores can be somewhat variable and subjective. Especially, there are only 5 scales at each item. It is necessary to develop a quantitative measurement of movement ability in PD patients. Hand Tapping test system was developed in this purpose.