TremAn: A tool for measuring tremor frequency from video sequences

Uhríková, Zdenka; Růžička, Evžen; Hlaváč, Václav; Nugent, Chris

doi:10.1002/mds.22904

Cited by 7 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It does not require any laboratory equipment and/or material attached to the patient's body. It allows to analyze tremor in video recordings acquired in a routine clinical setting or even from archived video samples, with the only condition required being that of stable view and picture background (Uhríková et al, 2010). On the other hand, the method has several limitations.…”

Section: Discussionmentioning

confidence: 99%

“…In an effort to address some of the limitations with current approaches, we have recently presented TremAn, 1 a tool for the automatic computer analysis of tremor frequency from ordinary video sequences (Uhríková et al, 2010). This approach does not require any special equipment nor does it require anything to be 1 Freely available at http://cmp.felk.cvut.cz/ ∼uhrikz1/treman/.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Validation of a new tool for automatic assessment of tremor frequency from video recordings

Uhríková

Šprdlík

Hoskovcová

et al. 2011

Journal of Neuroscience Methods

Self Cite

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of a new tool for automatic assessment of tremor frequency from video recordings

Uhríková

Šprdlík

Hoskovcová

et al. 2011

Journal of Neuroscience Methods

Self Cite

View full text Add to dashboard Cite

“…Regions of interest in video clips corresponding to each tremulous limb were analyzed via Fast Fourier Transform in TremAn software to obtain tremor frequency. 10 Rest and postural tremor was also analyzed in both positions via surface electromyography (EMG) electrodes of appropriate muscles. A dual channel recording was made via Teca Synergy EMG (CareFusion, San Diego, CA).…”

Section: Methodsmentioning

confidence: 99%

Clinical Differentiation of Essential Tremor and Parkinson's Disease

Fekete

2013

Clin Med Insights Case Rep

View full text Add to dashboard Cite

We present clinical features and tremor characterization in a patient with Parkinson's disease (PD) as well as in two cases of essential tremor (ET) with some parkinsonian features but no evidence of dopaminergic terminal loss on 123 I-FP-CIT Single Photon Emission Computed Tomography (SPECT). Relatively slow frequency rest tremor and bilateral upper extremity bradykinesia without decrementing amplitude were observed in the ET cases, with unilaterally decreased arm swing in case 3. Alternating rest tremor and re-emergent tremor with 13 second latency was confirmed in the PD case. Re-emergent tremor had alternating characteristics, which to our knowledge has not been previously reported. The ET cases had synchronous postural tremor. Alternating re-emergent tremor in PD provides further evidence for re-emergent tremor as an analogue of rest tremor in PD. Two cases of ET with synchronous postural tremor and one to two year history of parkinsonian features had no evidence of dopaminergic terminal loss up to 40 years after the initial onset of ET. Tremor synchronicity characterization can assist in differential diagnosis between the two disorders.

show abstract

“…The most similar work to our work is the work performed in [31,32] where tremor frequency is measure from pixel intensities in the video. However these methods assumes the location of the body part at which the tremor is measured to be known in advance and moreover, the frequency is estimated over intensity values rather than detected hand location over time, or image phase-information over time, as we propose here.…”

Section: Motion Analysismentioning

confidence: 96%

Hand-Tremor Frequency Estimation in Videos

Pintea

Zheng

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We focus on the problem of estimating human hand-tremor frequency from input RGB video data. Estimating tremors from video is important for non-invasive monitoring, analyzing and diagnosing patients suffering from motor-disorders such as Parkinson's disease. We consider two approaches for hand-tremor frequency estimation: (a) a Lagrangian approach where we detect the hand at every frame in the video, and estimate the tremor frequency along the trajectory; and (b) an Eulerian approach where we first localize the hand, we subsequently remove the large motion along the movement trajectory of the hand, and we use the video information over time encoded as intensity values or phase information to estimate the tremor frequency. We estimate hand tremors on a new human tremor dataset, TIM-Tremor, containing static tasks as well as a multitude of more dynamic tasks, involving larger motion of the hands. The dataset has 55 tremor patient recordings together with: associated ground truth accelerometer data from the most affected hand, RGB video data, and aligned depth data.

show abstract

TremAn: A tool for measuring tremor frequency from video sequences

Cited by 7 publications

References 8 publications

Validation of a new tool for automatic assessment of tremor frequency from video recordings

Validation of a new tool for automatic assessment of tremor frequency from video recordings

Clinical Differentiation of Essential Tremor and Parkinson's Disease

Hand-Tremor Frequency Estimation in Videos

Contact Info

Product

Resources

About