finger tapping
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2022 ◽  
Author(s):  
Zhu Li ◽  
lu kang ◽  
Miao Cai ◽  
Xiaoli Liu ◽  
Yanwen Wang ◽  
...  

Abstract PurposeThe assessment of dyskinesia in Parkinson's disease (PD) based on Artificial Intelligence technology is a significant and challenging task. At present, doctors usually use MDS-UPDRS scale to assess the severity of patients. This method is time-consuming and laborious, and there are subjective differences. The evaluation method based on sensor equipment is also widely used, but this method is expensive and needs professional guidance, which is not suitable for remote evaluation and patient self-examination. In addition, it is difficult to collect patient data in medical research, so it is of great significance to find an objective and automatic assessment method for Parkinson's dyskinesia based on small samples.MethodsIn this study, we design an automatic evaluation method combining manual features and convolutional neural network (CNN), which is suitable for small sample classification. Based on the finger tapping video of Parkinson's patients, we use the pose estimation model to obtain the action skeleton information and calculate the feature data. We then use the 5-folds cross validation training model to achieve optimum trade-of between bias and variance, and finally make multi-class prediction through fully connected network (FCN). ResultsOur proposed method achieves the current optimal accuracy of 79.7% in this research. We have compared with the latest methods of related research, and our method is superior to them in terms of accuracy, number of parameters and FLOPs. ConclusionThe method in this paper does not require patients to wear sensor devices, and has obvious advantages in remote clinical evaluation. At the same time, the method of using motion feature data to train CNN model obtains the optimal accuracy, effectively solves the problem of difficult data acquisition in medicine, and provides a new idea for small sample classification.


2022 ◽  
pp. 1-8
Author(s):  
Alex Page ◽  
Norman Yung ◽  
Peggy Auinger ◽  
Charles Venuto ◽  
Alistair Glidden ◽  
...  

<b><i>Background:</i></b> Smartphones can generate objective measures of Parkinson’s disease (PD) and supplement traditional in-person rating scales. However, smartphone use in clinical trials has been limited. <b><i>Objective:</i></b> This study aimed to determine the feasibility of introducing a smartphone research application into a PD clinical trial and to evaluate the resulting measures. <b><i>Methods:</i></b> A smartphone application was introduced part-way into a phase 3 randomized clinical trial of inosine. The application included finger tapping, gait, and cognition tests, and participants were asked to complete an assessment battery at home and in clinic alongside the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). <b><i>Results:</i></b> Of 236 eligible participants in the parent study, 88 (37%) consented to participate, and 59 (27 randomized to inosine and 32 to placebo) completed a baseline smartphone assessment. These 59 participants collectively completed 1,292 batteries of assessments. The proportion of participants who completed at least one smartphone assessment was 61% at 3, 54% at 6, and 35% at 12 months. Finger tapping speed correlated weakly with the part III motor portion (<i>r</i> = −0.16, left hand; <i>r</i> = −0.04, right hand) and total (<i>r</i> = −0.14) MDS-UPDRS. Gait speed correlated better with the same measures (<i>r</i> = −0.25, part III motor; <i>r</i> = −0.34, total). Over 6 months, finger tapping speed, gait speed, and memory scores did not differ between those randomized to active drug or placebo. <b><i>Conclusions:</i></b> Introducing a smartphone application midway into a phase 3 clinical trial was challenging. Measures of bradykinesia and gait speed correlated modestly with traditional outcomes and were consistent with the study’s overall findings, which found no benefit of the active drug.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Noreen Akram ◽  
Haoxuan Li ◽  
Aaron Ben-Joseph ◽  
Caroline Budu ◽  
David A. Gallagher ◽  
...  

AbstractDisability in Parkinson’s disease (PD) is measured by standardised scales including the MDS-UPDRS, which are subject to high inter and intra-rater variability and fail to capture subtle motor impairment. The BRadykinesia Akinesia INcoordination (BRAIN) test is a validated keyboard tapping test, evaluating proximal upper-limb motor impairment. Here, a new Distal Finger Tapping (DFT) test was developed to assess distal upper-limb function. Kinetic parameters of the test include kinesia score (KS20, key taps over 20 s), akinesia time (AT20, mean dwell-time on each key) and incoordination score (IS20, variance of travelling time between key taps). To develop and evaluate a new keyboard-tapping test for objective and remote distal motor function in PD patients. The DFT and BRAIN tests were assessed in 55 PD patients and 65 controls. Test scores were compared between groups and correlated with the MDS-UPDRS-III finger tapping sub-scores. Nine additional PD patients were recruited for monitoring motor fluctuations. All three parameters discriminated effectively between PD patients and controls, with KS20 performing best, yielding 79% sensitivity for 85% specificity; area under the receiver operating characteristic curve (AUC) = 0.90. A combination of DFT and BRAIN tests improved discrimination (AUC = 0.95). Among three parameters, KS20 showed a moderate correlation with the MDS-UPDRS finger-tapping sub-score (Pearson’s r = − 0.40, p = 0.002). Further, the DFT test detected subtle changes in motor fluctuation states which were not reflected clearly by the MDS-UPDRS-III finger tapping sub-scores. The DFT test is an online tool for assessing distal movements in PD, with future scope for longitudinal monitoring of motor complications.


PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261450
Author(s):  
Hannah L. Cornman ◽  
Jan Stenum ◽  
Ryan T. Roemmich

Assessment of repetitive movements (e.g., finger tapping) is a hallmark of motor examinations in several neurologic populations. These assessments are traditionally performed by a human rater via visual inspection; however, advances in computer vision offer potential for remote, quantitative assessment using simple video recordings. Here, we evaluated a pose estimation approach for measurement of human movement frequency from smartphone videos. Ten healthy young participants provided videos of themselves performing five repetitive movement tasks (finger tapping, hand open/close, hand pronation/supination, toe tapping, leg agility) at four target frequencies (1–4 Hz). We assessed the ability of a workflow that incorporated OpenPose (a freely available whole-body pose estimation algorithm) to estimate movement frequencies by comparing against manual frame-by-frame (i.e., ground-truth) measurements for all tasks and target frequencies using repeated measures ANOVA, Pearson’s correlations, and intraclass correlations. Our workflow produced largely accurate estimates of movement frequencies; only the hand open/close task showed a significant difference in the frequencies estimated by pose estimation and manual measurement (while statistically significant, these differences were small in magnitude). All other tasks and frequencies showed no significant differences between pose estimation and manual measurement. Pose estimation-based detections of individual events (e.g., finger taps, hand closures) showed strong correlations (all r>0.99) with manual detections for all tasks and frequencies. In summary, our pose estimation-based workflow accurately tracked repetitive movements in healthy adults across a range of tasks and movement frequencies. Future work will test this approach as a fast, quantitative, video-based approach to assessment of repetitive movements in clinical populations.


2021 ◽  
Vol 18 ◽  
Author(s):  
Linlin Zhao ◽  
Guanghua Liu ◽  
Lingli Zhang ◽  
Yuxiang Du ◽  
Le Lei ◽  
...  

Background: Alzheimer's disease (AD) is a chronic neurodegenerative disease which has been characterized by progressive development of long onset early disease with complicated etiology, and may cause memory loss, cognitive impairment, and behavioral changes. Physical exercise may play a preventive role in AD. In the present study, we investigated the impact of longer-term physical exercise on finger tapping of AD patient by comparing the finger tapping of AD patients and heathy controls without AD. Methods: In this study, 140 subjects who aged ≥ 60 years were enrolled. Group A consisted of 70 subjects (27 males and 43 females) without exercise habits who selected from Yangpu District (Shanghai, China). Group B consisted of 70 subjects (27 males and 43 females) who selected from Minxing District (Shanghai, China). All the subjects were right-handed as well. The subjects’ data, including subjects’ age, weight, height, Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), and finger tapping frequency were measured. Results: The subjects were matched in age, weight, and height. The AD subjects’ MoCA and MMSE scores were noticeably lower than healthy subjects’ scores (P<0.001); besides, AD patients with exercise have significantly lower MoCA and MMSE scores than healthy controls with exercise (P<0.001). The finger tapping of AD subjects’ left hands was significantly lower than that of healthy subjects without AD (P<0.01), and AD subjects with exercise tapped significantly slower with left hand than healthy subjects with exercise (P<0.01). Meanwhile, AD subjects with exercise tapped significantly faster with left hand than AD subjects (P<0.05). The right hands of AD subjects tapped remarkably less than healthy subjects (P<0.01) with or without exercise. Meanwhile, subjects with exercise tapped significantly faster with right hand than healthy subjects (P<0.05), and AD subjects with exercise tapped significantly faster with right hand than AD subjects (P<0.05). Conclusion: Long-term physical exercises can improve finger tapping frequency, especially patients with AD. Finger tapping frequency may be used as an index to monitor cognitive decline in ageing AD patients.


2021 ◽  
Author(s):  
Hanna Suominen ◽  
Mehika Manocha ◽  
Jane Desborough ◽  
Anne Parkinson ◽  
Deborah Apthorp

Parkinson’s Disease (PD) is a progressive chronic disorder with a high misdiagnosis rate. Because finger-tapping tasks correlate with its fine-motor symptoms, they could be used to help diagnose and assess PD. We first designed and developed an Android application to perform finger-tapping tasks without trained supervision, which is not always feasible for patients. Then, we conducted a preliminary user evaluation in Australia with six patients clinically diagnosed with PD and sixteen controls without PD. The application could be used in research and healthcare for regular symptom and progression assessment and feedback.


PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260783
Author(s):  
Soma Makai-Bölöni ◽  
Eva Thijssen ◽  
Emilie M. J. van Brummelen ◽  
Geert J. Groeneveld ◽  
Robert J. Doll

Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects almost 2% of the population above the age of 65. To better quantify the effects of new medications, fast and objective methods are needed. Touchscreen-based tapping tasks are simple yet effective tools for quantifying drug effects on PD-related motor symptoms, especially bradykinesia. However, there is no consensus on the optimal task set-up. The present study compares four tapping tasks in 14 healthy participants. In alternate finger tapping (AFT), tapping occurred with the index and middle finger with 2.5 cm between targets, whereas in alternate side tapping (AST) the index finger with 20 cm between targets was used. Both configurations were tested with or without the presence of a visual cue. Moreover, for each tapping task, within- and between-day repeatability and (potential) sensitivity of the calculated parameters were assessed. Visual cueing reduced tapping speed and rhythm, and improved accuracy. This effect was most pronounced for AST. On average, AST had a lower tapping speed with impaired accuracy and improved rhythm compared to AFT. Of all parameters, the total number of taps and mean spatial error had the highest repeatability and sensitivity. The findings suggest against the use of visual cueing because it is crucial that parameters can vary freely to accurately capture medication effects. The choice for AFT or AST depends on the research question, as these tasks assess different aspects of movement. These results encourage further validation of non-cued AFT and AST in PD patients.


2021 ◽  
Vol 15 ◽  
Author(s):  
Jingjing Li ◽  
Zheng Liu ◽  
Zhongquan Du ◽  
Ningning Zhu ◽  
Xueqing Qiu ◽  
...  

The finger tapping task (FTT) is commonly used in the evaluation of dyskinesia among patients with Parkinson's disease (PD). Past research has indicated that cortical activation during FTT is different between self-priming and cue-priming conditions. To evaluate how priming conditions affect the distribution of brain activation and the reorganization of brain function, and to investigate the differences in brain activation areas during FTT between PD patients and healthy control (HC) participants, we conducted an activation likelihood estimation (ALE) meta-analysis on the existing literature. Analyses were based on data from 15 independent samples that included 181 participants with PD and 164 HC participants. We found that there was significantly more activation in the middle frontal gyrus, precentral gyrus, post-central gyrus, superior parietal lobe, inferior parietal lobule, cerebellum, and basal ganglia during FTT in PD patients than in HCs. In self-priming conditions, PD patients had less activation in the parietal lobe and insular cortex but more activation in the cerebellum than the HCs. In cue-priming conditions, the PD patients showed less activation in the cerebellum and frontal-parietal areas and more activation in the superior frontal gyrus and superior temporal gyrus than the HCs. Our study illustrates that cue-priming manipulations affect the distribution of activity in brain regions involved in motor control and motor performance in PD patients. In cue-priming conditions, brain activity in regions associated with perceptual processing and inhibitory control was enhanced, while sensory motor areas associated with attention and motor control were impaired.


2021 ◽  
Vol 15 ◽  
Author(s):  
Filip Grill ◽  
Jarkko Johansson ◽  
Jan Axelsson ◽  
Patrik Brynolfsson ◽  
Lars Nyberg ◽  
...  

Striatal dopamine is involved in facilitation of motor action as well as various cognitive and emotional functions. Positron emission tomography (PET) is the primary imaging method used to investigate dopamine function in humans. Previous PET studies have shown striatal dopamine release during simple finger tapping in both the putamen and the caudate. It is likely that dopamine release in the putamen is related to motor processes while dopamine release in the caudate could signal sustained cognitive component processes of the task, but the poor temporal resolution of PET has hindered firm conclusions. In this study we simultaneously collected [11C]Raclopride PET and functional Magnetic Resonance Imaging (fMRI) data while participants performed finger tapping, with fMRI being able to isolate activations related to individual tapping events. The results revealed fMRI-PET overlap in the bilateral putamen, which is consistent with a motor component process. Selective PET responses in the caudate, ventral striatum, and right posterior putamen, were also observed but did not overlap with fMRI responses to tapping events, suggesting that these reflect non-motor component processes of finger tapping. Our findings suggest an interplay between motor and non-motor-related dopamine release during simple finger tapping and illustrate the potential of hybrid PET-fMRI in revealing distinct component processes of cognitive functions.


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