Automatic detection and quantification of hand movements toward development of an objective assessment of tremor and bradykinesia in Parkinson's disease

Pang, Yan; Christenson, Jake; Jiang, Feng; Lei, Tim C.; Rhoades, Remy; Kern, Drew S.; Thompson, John A.; Liu, Chao

doi:10.1016/j.jneumeth.2019.108576

Cited by 39 publications

(33 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rationale for the use of wavelet reconstruction is due to its ability to perform a pattern-oriented analysis [35]. In Methods C and D, the selection of the type of mother wavelet Coiflets is performed by resemblance process [36].…”

Section: Data Processingmentioning

confidence: 99%

Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb

Sapone

Martin

Mansour

et al. 2020

Sensors

View full text Add to dashboard Cite

The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.

show abstract

Section: Data Processingmentioning

confidence: 99%

Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb

Sapone

Martin

Mansour

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Interest in pose estimation has increased rapidly in the machine learning and neuroscience communities (9)(10)(11)(12)(13)(14)27); however, clinical applications in humans are limited (18,(22)(23)(24)28). The pros and cons of using currently available pose estimation algorithms for measurement of human movement have been discussed at length (29), and we will discuss our own impressions and suggestions below.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we used OpenPose (a freely available human pose estimation algorithm (11,15,16) to measure the frequencies of repetitive upper and lower extremity movements using smartphone videos. Previous studies have used OpenPose to study human gait (17)(18)(19)(20)(21) and quantify tremor severity in persons with PD (22). Other pose estimation algorithms have been used to measure the degree of levodopa-induced dyskinesias (23) and finger tapping frequency in PD that correlated well with clinical ratings (24,25).…”

Section: Introductionmentioning

confidence: 99%

Video-based quantification of human movement frequency using pose estimation

Stenum

Roemmich

2021

Preprint

View full text Add to dashboard Cite

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 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 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, low-cost, accessible approach to quantitative assessment of repetitive movements in clinical populations.

show abstract

“…They tracked hand localization points (including fingertips and thumb tips) by evaluating network capabilities, and extracted amplitude, distance, and rhythm to quantify finger tapping. Pang et al [23] used discrete wavelet transform (DWT) to extract the (3D) motion features of each finger joint. The severity of each finger joint tremor was quantified by analyzing the frequency of motion changes.…”

Section: Index Terms-finger Taps Non-contact Phase Difference Svmmentioning

confidence: 99%