Is "Frequency Distribution" Enough to Detect Tremor in PD Patients Using a Wrist Worn Accelerometer?

Ahlrichs, Claas; Samà, Albert

doi:10.4108/icst.pervasivehealth.2014.254928

Cited by 10 publications

(13 citation statements)

References 24 publications

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“…Thus it is possible to detect the tremor according to the frequency difference [4], [7]. It has also been proved that a tremor can be detected with the acceleration value collected from the wrist [3]. Therefore, in our case, the acceleration values collected from the smartwatch are used to detect tremor.…”

Section: (S)mentioning

confidence: 90%

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Detection and analysis of Tremor using a system based on smart device and NoSQL database

Zheng

Ordieres‐Meré

2015

2015 International Conference on Industrial Engineering and Systems Management (IESM)

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Abstract-Tremor is the most common symptoms of Parkinson's Disease (PD) and Essential Tremor (ET). Its detection and analysis during daily living plays a crucial role in the treatment of PD and ET patients. It is typically assessed in the clinic with certain tremor rating scales, which are qualitative, subjectdependent and do not necessarily reflect the real situation of the patient. In this paper, a system composed of a smartwatch, a smartphone and a NoSQL database sever is used to monitor the movements of the patients. A novel data analysis method is proposed to detect tremor and identify the connected actions. Tremor can be detected on the basis of the movement frequency difference and voluntary actions can also be recognized based on the rich information from the collected data. It helps clinicians to analyze the relationship between the tremor and a certain action. A series of simulated experiments are conducted to demonstrate the feasibility of the proposed system and data analysis method. The result shows that tremor happened during different situations can be detected with an adequate accuracy with the data collected by the proposed system. The actions around the tremor can also be identified.

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Section: (S)mentioning

confidence: 90%

“…It affects the movement of those suffering from the disease and it is typically characterized by a loss of motor function, increased slowness and rigidity [3]. Essential Tremor (ET) is another widely known disorder involving an action/posture tremor.…”

Section: Introductionmentioning

confidence: 99%

Detection and analysis of Tremor using a system based on smart device and NoSQL database

Zheng

Ordieres‐Meré

2015

2015 International Conference on Industrial Engineering and Systems Management (IESM)

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“…A large number of feature sets have been proposed for PD detection. The vast majority rely on time domain features (such as the mean, range, or cross-correlation) [ 13 , 14 ], frequency domain features (such as the dominant frequency, energy content in a particular band, or signal entropy) [ 15 , 16 ], or a combination of the two [ 17 , 18 , 19 , 20 ]. Some authors have shown that features that are traditionally used for speech processing (e.g., Mel frequency, Cepstral coefficients) are also effective for classifying human motion from accelerometer data [ 21 , 22 ].…”

Section: Related Workmentioning

confidence: 99%

Parkinson’s Disease Tremor Detection in the Wild Using Wearable Accelerometers

San-Segundo

Zhang

Cebulla

et al. 2020

Sensors

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Continuous in-home monitoring of Parkinson’s Disease (PD) symptoms might allow improvements in assessment of disease progression and treatment effects. As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerometer system to detect PD tremor in the wild (uncontrolled scenarios). We evaluate the performance of several feature sets and classification algorithms for robust PD tremor detection in laboratory and wild settings. We report results for both laboratory data with accurate labels and wild data with weak labels. The best performance was obtained using a combination of a pre-processing module to extract information from the tremor spectrum (based on non-negative factorization) and a deep neural network for learning relevant features and detecting tremor segments. We show how the proposed method is able to predict patient self-report measures, and we propose a new metric for monitoring PD tremor (i.e., percentage of tremor over long periods of time), which may be easier to estimate the start and end time points of each tremor event while still providing clinically useful information.

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“…The frequency of ET patients is higher in subjects without ET. Ahlrichs and Samà [7] introduced a system leveraging acceleration data obtained from subject’s wrist and classified tremors and non-tremors. Other studies such as [8] , [9] , and [10] used various kinds of tremor data obtained using acceleration sensors to identify the tremors.…”

Section: Related Workmentioning

confidence: 99%

Detection of Essential Tremor at the $S$ -Band

Yang

Shah

Ren

et al. 2018

IEEE J. Transl. Eng. Health Med.

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Essential tremor (ET) is a neurological disorder characterized by rhythmic, involuntary shaking of a part or parts of the body. The most common tremor is seen in the hands/arms and fingers. This paper presents an evaluation of ETs monitoring based on finger-to-nose test measurement as captured by small wireless devices working in shortwave or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$S$ \end{document}-band frequency range. The acquired signals in terms of amplitude and phase information are used to detect a tremor in the hands. Linearly transforming raw phase data acquired in the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$S$ \end{document}-band were carried out for calibrating the phase information and to improve accuracy. The data samples are used for classification using support vector machine algorithm. This model is used to differentiate the tremor and nontremor data efficiently based on secondary features that characterize ET. The accuracy of our measurements maintains linearity, and more than 90% accuracy rate is achieved between the feature set and data samples.

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Is "Frequency Distribution" Enough to Detect Tremor in PD Patients Using a Wrist Worn Accelerometer?

Cited by 10 publications

References 24 publications

Detection and analysis of Tremor using a system based on smart device and NoSQL database

Detection and analysis of Tremor using a system based on smart device and NoSQL database

Parkinson’s Disease Tremor Detection in the Wild Using Wearable Accelerometers

Detection of Essential Tremor at the $S$ -Band

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