Accuracy of Smartphone Video for Contactless Measurement of Hand Tremor Frequency

Williams, Stefan B.; Fang, Hui; Relton, Samuel D.; Wong, David C.; Alam, Taimour; Alty, Jane

doi:10.1002/mdc3.13119

Cited by 30 publications

(42 citation statements)

References 24 publications

(41 reference statements)

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“…Additionally, a study was conducted to record tremors with video processing technology on videos taken by smartphones. Results showed a −0.38 to +0.35 Hz difference from results recorded by an accelerometer [28]. These data, however, are only for hand rest and postural position, and there is a need for additional customization for leg tremor classifications.…”

Section: Discussionmentioning

confidence: 80%

A Portable Non-Contact Tremor Vibration Measurement and Classification Apparatus

et al. 2022

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Tremors are the most common type of movement disorder and affect the lives of those experiencing them. The efficacy of tremor therapies varies according to the aetiology of the tremor and its correct diagnosis. This study develops a portable measurement device capable of non-contact measurement of the tremor, which could assist in tremor diagnosis and classification. The performance of this device was assessed through a validation process using a shaker at a controlled frequency to measure human tremors, and the device was able to measure vibrations of 50 Hz accurately, which is more than twice the frequency of tremors produced by humans. Then, the device is tested to measure the tremors for two different activation conditions: rest and postural, for both hand and leg. The measured non-contact tremor vibration data successfully led to tremor classification in the subjects already diagnosed using a contact accelerometer.

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Section: Discussionmentioning

confidence: 80%

A Portable Non-Contact Tremor Vibration Measurement and Classification Apparatus

et al. 2022

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“…Machine learning applied to video recordings of tremor behaviour provides an exciting opportunity to bring precision measurements into the clinic [ 54 ]. Tremor frequency can be quantified from smartphone video recordings in ET and PD [ 54 ], with an accuracy within 0.5 Hz of an accelerometer.…”

Section: Can Clinically Accessible Technologies Help Us Disentangle L...mentioning

confidence: 99%

New horizons in late-onset essential tremor: a pre-cognitive biomarker of dementia?

Wang

George

Bai³

et al. 2022

Age and Ageing

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Essential tremor (ET) is the most common cause of tremor in older adults. However, it is increasingly recognised that 30–50% of ET cases are misdiagnosed. Late-onset ET, when tremor begins after the age of 60, is particularly likely to be misdiagnosed and there is mounting evidence that it may be a distinct clinical entity, perhaps better termed ‘ageing-related tremor’. Compared with older adults with early-onset ET, late-onset ET is associated with weak grip strength, cognitive decline, dementia and mortality. This raises questions around whether late-onset ET is a pre-cognitive biomarker of dementia and whether modification of dementia risk factors may be particularly important in this group. On the other hand, it is possible that the clinical manifestations of late-onset ET simply reflect markers of healthy ageing, or frailty, superimposed on typical ET. These issues are important to clarify, especially in the era of specialist neurosurgical treatments for ET being increasingly offered to older adults, and these may not be suitable in people at high risk of cognitive decline. There is a pressing need for clinicians to understand late-onset ET, but this is challenging when there are so few publications specifically focussed on this subject and no specific features to guide prognosis. More rigorous clinical follow-up and precise phenotyping of the clinical manifestations of late-onset ET using accessible computer technologies may help us delineate whether late-onset ET is a separate clinical entity and aid prognostication.

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“…Some advances have recently been made in using video for assessment of tremor frequency in a research setting. 10 While these first steps are encouraging, application in other movement disorders is still lacking. Moreover, until now, investigations of machine learning in movement disorders have mainly focused on quantification of the hyperkinetic movements 11 and optimisation of adaptive deep brain stimulation.…”

Section: Open Accessmentioning

confidence: 99%

Next move in movement disorders (NEMO): developing a computer-aided classification tool for hyperkinetic movement disorders

Stouwe

Tuitert

Giotis³

et al. 2021

BMJ Open

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IntroductionOur aim is to develop a novel approach to hyperkinetic movement disorder classification, that combines clinical information, electromyography, accelerometry and video in a computer-aided classification tool. We see this as the next step towards rapid and accurate phenotype classification, the cornerstone of both the diagnostic and treatment process.Methods and analysisThe Next Move in Movement Disorders (NEMO) study is a cross-sectional study at Expertise Centre Movement Disorders Groningen, University Medical Centre Groningen. It comprises patients with single and mixed phenotype movement disorders. Single phenotype groups will first include dystonia, myoclonus and tremor, and then chorea, tics, ataxia and spasticity. Mixed phenotypes are myoclonus-dystonia, dystonic tremor, myoclonus ataxia and jerky/tremulous functional movement disorders. Groups will contain 20 patients, or 40 healthy participants. The gold standard for inclusion consists of interobserver agreement on the phenotype among three independent clinical experts. Electromyography, accelerometry and three-dimensional video data will be recorded during performance of a set of movement tasks, chosen by a team of specialists to elicit movement disorders. These data will serve as input for the machine learning algorithm. Labels for supervised learning are provided by the expert-based classification, allowing the algorithm to learn to predict what the output label should be when given new input data. Methods using manually engineered features based on existing clinical knowledge will be used, as well as deep learning methods which can detect relevant and possibly new features. Finally, we will employ visual analytics to visualise how the classification algorithm arrives at its decision.Ethics and disseminationEthical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases.

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Accuracy of Smartphone Video for Contactless Measurement of Hand Tremor Frequency

Cited by 30 publications

References 24 publications

A Portable Non-Contact Tremor Vibration Measurement and Classification Apparatus

A Portable Non-Contact Tremor Vibration Measurement and Classification Apparatus

New horizons in late-onset essential tremor: a pre-cognitive biomarker of dementia?

Next move in movement disorders (NEMO): developing a computer-aided classification tool for hyperkinetic movement disorders

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