Patient-Specific Pose Estimation in Clinical Environments

Chen, Kenny; Gabriel, Paolo; Alasfour, Abdulwahab; Gong, Chenghao; Doyle, Werner; Devinsky, Orrin; Friedman, Daniel; Dugan, Patricia; Melloni, Lucía; Thesen, Thomas; Gonda, David; Sattar, Shifteh; Wang, Sonya; Gilja, Vikash

doi:10.1109/jtehm.2018.2875464

Cited by 58 publications

(45 citation statements)

References 48 publications

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“…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%

Video-based quantification of human movement frequency using pose estimation

Stenum

Roemmich

2021

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Video-based quantification of human movement frequency using pose estimation

Stenum

Roemmich

2021

Preprint

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“…An emerging method for markerless vision-based pose estimation is the use of deep neural networks to estimate pose from 2D images [22,23,49,50], which could be promising for clinical environments. One study utilized this method in a hospital setting, but it only measured 2D pose and required manually labeled training data in each patient to account for environmental variations and to ensure proper tracking of a broad range of postures [23]. Further, only one study tracked 3D pose using a stereoscopic camera system in a laboratory environment, but accuracy was not rigorously evaluated [22].…”

Section: Discussionmentioning

confidence: 99%

Estimation of arm kinematics in stroke survivors using wearable sensors

Nie¹,

Nie²,

Hung³

et al. 2021

Preprint

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BackgroundStroke is the leading cause of long-term disability in the United States, often resulting in upper extremity (UE) motor impairment. Most existing outcome metrics of UE function in rehabilitation are insensitive to change or subject to observer bias. There is growing interest in using movement kinematics to measure UE motor function, since they can provide high-resolution, quantitative measurements. However, measuring arm kinematics in stroke survivors, particularly in the hospital or clinic, can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and a limited ability to perform required calibration poses. The aim of this study was to develop a general framework for accurate measurements of wrist position during reaching movements in people with stroke using relatively inexpensive wearable sensors.MethodsWe developed and presented two methods, one using inertial measurement units (IMUs) and using virtual reality (Vive) sensors, that practically estimate wrist position with respect to the shoulder. We then assessed the estimation accuracies of each method during a 3D reaching task by using a Vicon motion capture system. We also demonstrated each methods ability to track two kinematic metrics, sweep area and smoothness, in chronic stroke survivors. We computed Pearson correlation coefficients when appropriate.ResultsCompared to a traditional optical system, both systems tracked with high accuracy during 3D reaching, with mean absolute errors of 1.00 ± 0.80 cm and 1.09 ± 0.51 cm for the IMU and Vive, respectively. Furthermore, both methods’ estimated kinematics highly agreed with each other (p < 0.01).ConclusionsThese methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.Trial RegistrationThe clinical trial (ClinicalTrial.gov ID: NCT03401762) was registered on January 17, 2018 (https://clinicaltrials.gov/ct2/show/NCT03401762) and posted on January 1, 2018. The trial is scheduled to be completed by August 2023. The trial was updated on December 2, 2020 and is currently recruiting.

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“…An alternative strategy is to use video alone to monitor activity. Previous work has demonstrated the ability to track upper body joints in the Epilepsy Monitoring Unit from RGB-video [38]. This approach is advantageous because sensors do not touch the body, which removes the risk of skin irritation and other compilations such as neglecting to remove non-MRI compatible sensors prior to MRI.…”

Section: Discussionmentioning

confidence: 99%

Use of Accelerometry for Long Term Monitoring of Stroke Patients

Lucas

Hermiz

LaBuzetta

et al. 2019

IEEE J. Transl. Eng. Health Med.

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Stroke patients are monitored hourly by physicians and nurses in an attempt to better understand their physical state. To quantify the patients’ level of mobility, hourly movement (i.e. motor) assessment scores are performed, which can be taxing and time-consuming for nurses and physicians. In this paper, we attempt to find a correlation between patient motor scores and continuous accelerometer data recorded in subjects who are unilaterally impaired due to stroke. The accelerometers were placed on both upper and lower extremities of four severely unilaterally impaired patients and their movements were recorded continuously for 7 to 14 days. Features that incorporate movement smoothness, strength, and characteristic movement patterns were extracted from the accelerometers using time-frequency analysis. Support vector classifiers were trained with the extracted features to test the ability of the long term accelerometer recordings in predicting dependent and antigravity sides, and significantly above baseline performance was obtained in most instances ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$P < 0.05$ \end{document} ). Finally, a leave-one-subject-out approach was carried out to assess the generalizability of the proposed methodology, and above baseline performance was obtained in two out of the three tested subjects. The methodology presented in this paper provides a simple, yet effective approach to perform long term motor assessment in neurocritical care patients.

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Patient-Specific Pose Estimation in Clinical Environments

Cited by 58 publications

References 48 publications

Video-based quantification of human movement frequency using pose estimation

Video-based quantification of human movement frequency using pose estimation

Estimation of arm kinematics in stroke survivors using wearable sensors

Use of Accelerometry for Long Term Monitoring of Stroke Patients

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