A Real-Time Contactless Pulse Rate and Motion Status Monitoring System Based on Complexion Tracking.
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ABSTRACT:
SUBMITTER: Lin YC
PROVIDER: S-EPMC5539811 | biostudies-other | 2017 Jun
REPOSITORIES: biostudies-other
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Project description:PurposeThe objective of this work is to test the validity of the Gaussian approximation for prostate motion through characterization of its spatial distribution.Methods and materialsReal-time intrafraction prostate motion was observed using Calypso 4-dimensional (4D) nonradioactive electromagnetic tracking system. We report the results from a total of 1024 fractions from 31 prostate cancer patients. First, the correlation of prostate motion in right/left (RL), anteroposterior (AP), and superoinferior (SI) direction were determined using Pearson's correlation of coefficient. Then the spatial distribution of prostate motion was analyzed for individual fraction, individual patient including all fractions, and all patients including all fractions. The displacement in RL, AP, SI, oblique, or total direction is fitted into a Gaussian distribution, and a Lilliefors test was used to evaluate the validity of the hypothesis that the displacement is normally distributed.ResultsThere is high correlation in AP/SI direction (61% of fractions with medium or strong correlation). This is consistent with the longitudinal oblique motion of the prostate, and likely the effect from respiration on an organ confined within the genitourinary diaphragm with the rectum sitting posteriorly and bladder sitting superiorly. In all directions, the non-Gaussian distribution is more common for individual fraction, individual patient including all fractions, and all patients including all fractions. The spatial distribution of prostate motion shows an elongated shape in oblique direction, indicating a higher range of motion in the AP and SI directions.ConclusionsOur results showed that the prostate motion is highly correlated in AP and SI direction, indicating an oblique motion preference. In addition, the spatial distribution of prostate motion is elongated in an oblique direction, indicating that the organ motion dosimetric modeling using Gaussian kernel may need to be modified to account for the particular organ motion character of prostate.
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Project description:Physical therapy is an important component of gait recovery for individuals with locomotor dysfunction. There is a growing body of evidence that suggests that incorporating a motor learning task through visual feedback of movement trajectory is a useful approach to facilitate therapeutic outcomes. Visual feedback is typically provided by recording the subject's limb movement patterns using a three-dimensional motion capture system and displaying it in real-time using customized software. However, this approach can seldom be used in the clinic because of the technical expertise required to operate this device and the cost involved in procuring a three-dimensional motion capture system. In this paper, we describe a low cost two-dimensional real-time motion tracking approach using a simple webcam and an image processing algorithm in LabVIEW Vision Assistant. We also evaluated the accuracy of this approach using a high precision robotic device (Lokomat) across various walking speeds. Further, the reliability and feasibility of real-time motion-tracking were evaluated in healthy human participants. The results indicated that the measurements from the webcam tracking approach were reliable and accurate. Experiments on human subjects also showed that participants could utilize the real-time kinematic feedback generated from this device to successfully perform a motor learning task while walking on a treadmill. These findings suggest that the webcam motion tracking approach is a feasible low cost solution to perform real-time movement analysis and training.
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Project description:Accidents related to the administration of intravenous (IV) medication, such as drug overdose/underdose, drug/patient mis-identification, and delayed bag exchange, occur consistently in clinical fields. Several previous studies have suggested various contact-sensing and image-processing methodologies; however, most of them can increase the workload of nursing staffs during the long-term, continuous monitoring. In this study, we proposed a smart IV pole that can monitor the infusion status of up to four IV medications (patient/drug identification, and liquid residue) with various sizes and hanging positions to reduce IV-related accidents and improve patient safety with the least additional workload; the system consists of 12 cameras, one code scanner, and four controllers. Two types of deep learning models for automated camera selection (CNN-1) and liquid residue monitoring (CNN-2), and three drug residue estimation equations were implemented. The experimental results demonstrated that the accuracy of identification code-checking (60 tests) was 100%. The classification accuracy and the mean inference time of CNN-1 (1200 tests) were 100% and 140 ms. The mean average precision and the mean inference time of CNN-2 (300 tests) were 0.94 and 144 ms. The average error rates between the alarm setting (20, 30, and 40 mL) and the actual drug residue when the alarm first generated were 4.00%, 7.33%, and 4.50% for a 1,000 mL bag; 6.00%, 4.67%, and 2.50% for a 500 mL bag; and 3.00%, 6.00%, and 3.50% for a 100 mL bag, respectively. Our results suggest that the implemented AI-based prototype IV pole is a potential tool for reducing IV-related accidents and improving in-hospital patient safety.Supplementary informationThe online version contains supplementary material available at 10.1007/s13534-023-00292-w.
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Project description:BackgroundSurface-guided radiation therapy (SGRT) employs a non-invasive real-time optical surface imaging (OSI) technique for patient surface motion monitoring during radiotherapy. The main purpose of this study is to verify the real-time tracking accuracy of SGRT for respiratory motion and provide a fitting method to detect the time delay of gating.MethodsA respiratory motion phantom was utilized to simulate respiratory motion using 17 cosine breathing pattern curves with various periods and amplitudes. The motion tracking of the phantom was performed by the Catalyst™ system. The tracking accuracy of the system (with period and amplitude variations) was evaluated by analyzing the adjusted coefficient of determination (A_R2) and root mean square error (RMSE). Furthermore, 13 actual respiratory curves, which were categorized into regular and irregular patterns, were selected and then simulated by the phantom. The Fourier transform was applied to the respiratory curves, and tracking accuracy was compared through the quantitative analyses of curve similarity using the Pearson correlation coefficient (PCC). In addition, the time delay of amplitude-based respiratory-gating radiotherapy based on the OSI system with various beam hold times was tested using film dosimetry for the Elekta Versa-HD and Varian Edge linacs. A dose convolution-fitting method was provided to accurately measure the beam-on and beam-off time delays.ResultsA_R2 and RMSE for the cosine curves were 0.9990-0.9996 and 0.110-0.241 mm for periods ranging from 1 s to 10 s and 0.9990-0.9994 and 0.059-0.175 mm for amplitudes ranging from 3 mm to 15 mm. The PCC for the actual respiratory curves ranged from 0.9955 to 0.9994, which was not significantly affected by breathing patterns. For gating radiotherapy, the average beam-on and beam-off time delays were 1664 ± 72 and 25 ± 30 ms for Versa-HD and 303 ± 45 and 34 ± 25 ms for Edge, respectively. The time delay was relatively stable as the beam hold time increased.ConclusionsThe OSI technique provides high accuracy for respiratory motion tracking. The proposed dose convolution-fitting method can accurately measure the time delay of respiratory-gating radiotherapy. When the OSI technique is used for respiratory-gating radiotherapy, the time delay for the beam-on is considerably longer than the beam-off.
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Project description:Background and purposeIn this paper, we investigate the possibility to use X-ray based real time 2D/3D registration for non-invasive tumor motion monitoring during radiotherapy.Materials and methodsThe 2D/3D registration scheme is implemented using general purpose computation on graphics hardware (GPGPU) programming techniques and several algorithmic refinements in the registration process. Validation is conducted off-line using a phantom and five clinical patient data sets. The registration is performed on a region of interest (ROI) centered around the planned target volume (PTV).ResultsThe phantom motion is measured with an rms error of 2.56 mm. For the patient data sets, a sinusoidal movement that clearly correlates to the breathing cycle is shown. Videos show a good match between X-ray and digitally reconstructed radiographs (DRR) displacement. Mean registration time is 0.5 s.ConclusionsWe have demonstrated that real-time organ motion monitoring using image based markerless registration is feasible.
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Project description:Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.
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