Real-time Capture Research Articles

Estimated solar-neutrino capture rates of 131Xe: implications for multi-tonne Xe-based experiments

Various large-scale experiments for double beta decay or dark matter are based on xenon. Current experiments are on the tonne scale, but future ideas also aim for even larger sizes. Here we study the potential of the isotope 131Xe to allow real-time capture measurements of solar pp-chain neutrinos, besides classical neutrino-electron scattering. Here we use improved nuclear-structure calculations to determine the cross sections of solar neutrinos on 131Xe. Our updated capture-rate estimate is (80 ± 22) SNU, with neutrino survival probabilities taken into account. According to our calculations, the 8B neutrinos are the dominant contribution to the total capture rate. Due to our more accurate treatment of the phase-space factor the computed capture rate, (60 ± 19) SNU, is significantly larger than what was expected based on previous calculations. This improves considerably the prospects of real-time monitoring of pp-chain neutrinos for long periods of time.

Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies

Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail.

Factors influencing peri-operative delays in neurosurgery operating theatres: a prospective study

BackgroundReduction of perioperative delays optimises theatre efficiency, and may provide an opportunity to improve the quality of healthcare delivered. The aim of this quality improvement project was to elucidate the prevalence, causes and impact of perioperative delays in Neurosurgery theatres at our local institution. MethodsA prospective audit analysing elective Neurosurgery cases was conducted from 1st Sept ‘18 to 28th Feb ’19 (6 months).Quantitative and qualitative analysis was performed on data from the Operating Room Scheduling Office System (ORSOS) real-time data capture forms and a custom-made survey filled out by theatre staff. NHS Benchmarking Network national neurosurgery theatre data were utilised as a control group. ResultsA total of 461 (90%) cases started late (average delay of 49 minutes and 21 seconds), 32% (n=164) cases finished early and 31 minutes was the average turnaround time. There was 16.8%(n=124) cancellation rate and 76% theatre utilisation. The most commonly cited causes for late start were lack of patient readiness (62.1%), change in list order (5.3%), anaesthetic delay (5.3%), surgeon delay (4.5%), turnaround delay (2.3%), bed issues (2.3%), emergency (1.5%), fire alarm (0.8%), kit delay (0.8%) and multi-factorial (15.2%). The total cost of late start delays over the study period was £304,266.18. ConclusionsThis study showed late-starts and early-finishes as part of sub-optimal theatre utilisation. Cost-saving could be immense if corporate factors improve such as availability of appropriate beds along with clinical ‘human’ factors. To ameliorate these delays, we are currently implementing strategies to improve compliance of lumbar spinal surgery as day-case guideline. In order to ensure long-term sustainability, this implementation will be re-audited using a Plan-Do-Study-Act cycle in 6 months’ time.

Compliance With Mobile Ecological Momentary Assessment of Self-Reported Health-Related Behaviors and Psychological Constructs in Adults: Systematic Review and Meta-analysis.

BackgroundMobile ecological momentary assessment (mEMA) permits real-time capture of self-reported participant behaviors and perceptual experiences. Reporting of mEMA protocols and compliance has been identified as problematic within systematic reviews of children, youth, and specific clinical populations of adults.ObjectiveThis study aimed to describe the use of mEMA for self-reported behaviors and psychological constructs, mEMA protocol and compliance reporting, and associations between key components of mEMA protocols and compliance in studies of nonclinical and clinical samples of adults.MethodsIn total, 9 electronic databases were searched (2006-2016) for observational studies reporting compliance to mEMA for health-related data from adults (>18 years) in nonclinical and clinical settings. Screening and data extraction were undertaken by independent reviewers, with discrepancies resolved by consensus. Narrative synthesis described participants, mEMA target, protocol, and compliance. Random effects meta-analysis explored factors associated with cohort compliance (monitoring duration, daily prompt frequency or schedule, device type, training, incentives, and burden score). Random effects analysis of variance (P≤.05) assessed differences between nonclinical and clinical data sets.ResultsOf the 168 eligible studies, 97/105 (57.7%) reported compliance in unique data sets (nonclinical=64/105 [61%], clinical=41/105 [39%]). The most common self-reported mEMA target was affect (primary target: 31/105, 29.5% data sets; secondary target: 50/105, 47.6% data sets). The median duration of the mEMA protocol was 7 days (nonclinical=7, clinical=12). Most protocols used a single time-based (random or interval) prompt type (69/105, 65.7%); median prompt frequency was 5 per day. The median number of items per prompt was similar for nonclinical (8) and clinical data sets (10). More than half of the data sets reported mEMA training (84/105, 80%) and provision of participant incentives (66/105, 62.9%). Less than half of the data sets reported number of prompts delivered (22/105, 21%), answered (43/105, 41%), criterion for valid mEMA data (37/105, 35.2%), or response latency (38/105, 36.2%). Meta-analysis (nonclinical=41, clinical=27) estimated an overall compliance of 81.9% (95% CI 79.1-84.4), with no significant difference between nonclinical and clinical data sets or estimates before or after data exclusions. Compliance was associated with prompts per day and items per prompt for nonclinical data sets. Although widespread heterogeneity existed across analysis (I2>90%), no compelling relationship was identified between key features of mEMA protocols representing burden and mEMA compliance.ConclusionsIn this 10-year sample of studies using the mEMA of self-reported health-related behaviors and psychological constructs in adult nonclinical and clinical populations, mEMA was applied across contexts and health conditions and to collect a range of health-related data. There was inconsistent reporting of compliance and key features within protocols, which limited the ability to confidently identify components of mEMA schedules likely to have a specific impact on compliance.

Ecological momentary assessment to explore fatigue, mood and physical activity levels in people receiving peritoneal dialysis: A study protocol.

Fatigue is a frequent and debilitating symptom for people with end-stage kidney disease (ESKD) receiving dialysis. Ecological momentary assessment (EMA) allows real-time data capture of day-to-day and diurnal variations. EMA has been used to study haemodialysis-related fatigue but not in people receiving peritoneal dialysis who are unique in their physical, environmental and logistical characteristics. The aim of this study is to explore the real-time associations between fatigue and mood (EMA mobile application) and objective physical activity levels (accelerometry) in people with EKSD receiving peritoneal dialysis. A 7-day intensive longitudinal study will be conducted. People receiving peritoneal dialysis within South Australia will be invited to participate. Five times throughout the day, participants will be prompted to answer 18 questions relating to fatigue (Visual Analogue Scale to Evaluate Fatigue Severity) and a single question for mood (Visual Analogue Mood Scale). Participants will continuously wear a GENEActiv accelerometer to capture physical activity levels during the 7-day period. At the completion of the data collection, participants will answer questions to evaluate the feasibility and acceptability of using EMA. This study will be the first to explore the real-time relationships between fatigue, mood and physical activity in people with ESKD receiving peritoneal dialysis. Understanding the fluctuations people experience and the relationships between mood and physical activity and fatigue will inform clinical management and well-being intervention development.

Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies

Autonomous vehicles require fleet-wide data collection for continuous algorithm development and validation. The smart black box (SBB) intelligent event data recorder has been proposed as a system for prioritized high-bandwidth data capture. This paper extends the SBB by applying anomaly detection and action detection methods for generalized event-of-interest (EOI) detection. An updated SBB pipeline is proposed for the real-time capture of driving video data. A video dataset is constructed to evaluate the SBB on real-world data for the first time. SBB performance is assessed by comparing the compression of normal and anomalous data and by comparing our prioritized data recording with an FIFO strategy. The results show that SBB data compression can increase the anomalous-to-normal memory ratio by ∼25%, while the prioritized recording strategy increases the anomalous-to-normal count ratio when compared to an FIFO strategy. We compare the real-world dataset SBB results to a baseline SBB given ground-truth anomaly labels and conclude that improved general EOI detection methods will greatly improve SBB performance.

A qualitative exploration of the patient experience of erosive and non-erosive hand osteoarthritis

BackgroundMany patients with hand osteoarthritis (HOA) experience reduced health-related quality of life. This study sought to better understand the disease and treatment experience of individuals with HOA, explore any differences in experiences between erosive and non-erosive HOA sub-types, and evaluate content validity of the Michigan Hand Outcomes Questionnaire (MHQ) in HOA.MethodsThirty subjects from the United States (n = 15 erosive HOA; n = 15 non-erosive HOA) participated in semi-structured interviews: concept elicitation explored symptoms/impacts important to patients; cognitive interviews assessed understanding and relevance of the MHQ. A sub-sample participated in real-time data capture (RTDC) activities via a smartphone/tablet app over 7 days. Verbatim transcripts were coded using Atlas.ti software and thematically analyzed. Concept saturation and MHQ content validity were evaluated.ResultsMost participants reported experiencing pain, swelling and stiffness, symptoms that most commonly had a direct impact on physical functioning. Substantial impacts on activities of daily living, emotional functioning, sleep and work were also reported. RTDC findings corroborated concept elicitation findings. There were no notable differences between erosive and non-erosive HOA, except nodules were reported more frequently in erosive disease. Most participants used analgesic treatments, but effects were short-lived. Pain was the symptom most frequently reported as most bothersome and important to treat. Concept saturation was achieved. MHQ items and instructions were well understood and relevant to most participants; stiffness and swelling were reported as important symptoms not included in the MHQ.ConclusionsThis study characterizes key symptoms of HOA which are burdensome for patients and not well controlled by current therapies, highlighting an unmet treatment need. Although the study is limited by a small sample size that may not be representative of the broader erosive and non-erosive HOA population, concept saturation was achieved, and our findings suggest that disease experience is similar for patients with erosive and non-erosive HOA. Evaluation of stiffness and swelling items in conjunction with the MHQ may enhance relevance and improve measurement precision to assess important domains of HQRoL in an HOA population.

Grasping Prediction Algorithm Based on Full Convolutional Neural Network

Robot grasping is a very frontier and important research direction in the field of robotics. In order to solve the problem of the robot's real-time capture, reduce the time of the visual processing, we proposed a two-stage Convolutional Neural Network structure whose design is simple, with less training parameters, improving the efficiency of the visual system. Using rotation and translation to expand the Cornell fetching dataset. The best model at Cornell grasp test set has achieved 88% of forecast accuracy compared with 73% accuracy rate on one stage network. Moreover, our model size is 0.51MB, speed at 30 FPS on GPU inferencing.

Control of object prediction using smart optimized water indication (sonic) algorithm for flood detection

This paper presents the development of a new algorithm in the field of image processing that enables the detection of flood disasters quickly and accurately, using the SONIC (Smart water indication optimizer) method. Concentrated detection in online real-time camera systems has been performed by several tests consisting of classifying camera objects, thermal cameras, and learning cameras. The introduction to the RTC web enables real-time and multiplatform data delivery systems on devices comprised of computers and android gadgets, on object classification using the SONIC algorithm. The object consists of humans, yellow balls, and green balls, with each sample having 50 points of view. The experiments showed test results up to 100% per age with real-time camera capture speeds.

Ultraviolet-light-triggered isomerization of Rydberg-excited propanal: Real-time capture of ultrafast structural evolution and dynamics investigation.

Structure rearrangement processes, such as isomerization, are attracting extensive interest as a potential carrier in molecular scale electronics design. UV-light-triggered isomerization of Rydberg-excited propanal with two UV photons has been investigated with time-resolved photoelectron spectroscopy. By following the photoionization from 3s Rydberg states in the time domain, the ultrafast structural evolution and the corresponding photoisomerization dynamics are observed and tracked in real-time. The conversion barrier for isomerization from cis-propanal to gauche isomer is estimated to be about 1500 ± 100 cm-1 experimentally. Both the photoisomerization yield and the conversion rate have shown strong dependence on the excitation energy. It is observed that whether vibration modes are selectively excited or not, cis-to-gauche photoisomerization of propanal in 3s Rydberg state occurs once the excitation energy is higher than the conversion barrier without any vibrational excitation specificity. This yields a powerful approach to studying structural evolution dynamics in large molecules, which may have applications in molecular devices.

The VITOM® exoscope in oculoplastic surgery: the 5 year Coventry experience.

BackgroundIntraoperative imaging is a desirable tool in oculoplastic surgery; however, there are few modalities which allow surgeons to capture and edit the images in real time without compromising the sterility of the surgical field. We describe the set-up of the VITOM® exoscope system based on our 5 years’ experience of using it as an effective intraoperative video imaging and teaching modality for extraocular surgery.MethodsThe VITOM® is a specially designed exoscope mounted onto a versatile mechanical arm. It is attached to a high definition (HD) digital camera displayed on the HD video monitor of a standard endoscopy stack. This technology has been utilised in other surgical subspecialties, but there is no documented use within extraocular surgery.ResultsThe exoscope was simple to set-up and allowed real-time recording and editing with an HD image display system. The theatre team were able to view the precise surgical steps, contributing to improved theatre flow. Trainees reported that the VITOM® images significantly improved their visualisation and understanding of key surgical anatomy and steps.ConclusionsOur experience showed that the VITOM® exoscope is an excellent intraoperative video imaging and teaching aid, as it allows real-time capture and editing of open surgery and seems to improve theatre flow. With newer models using 3D stereoscopic vision, it could be further evaluated as a heads-up viewing system within extraocular and oculoplastic surgery.

Strength Training Characteristics of Different Loads Based on Acceleration Sensor and Finite Element Simulation.

Deep squat, bench press and hard pull are important ways for people to improve their strength. The use of sensors to measure force is rare. Measuring strength with sensors is extremely valuable for people to master the intensity of exercise to scientifically effective exercise. To this end, in this paper, we used a real-time wireless motion capture and mechanical evaluation system of the wearable sensor to measure the dynamic characteristics of 30 young men performing deep squat, bench press and hard pull maneuvers. The data of tibia were simulated with AnyBody 5.2 and ANSYS 19.2 to verify the authenticity. The result demonstrated that the appropriate force of the deep squat elbow joint, the hip joint and the knee joint is 40% 1RM, the appropriate force of the bench press is 40% 1RM and the appropriate force of the hard pull is 80% 1RM. The external force is the main factor of bone change. The mechanical characteristics of knee joint can be simulated after the Finite Element Analysis and the simulation of AnyBody model are verified.

A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study.

BackgroundChest examination by auscultation is essential in patients with COVID-19, especially those with poor respiratory conditions, such as severe pneumonia and respiratory dysfunction, and intensive cases who are intubated and whose breathing is assisted with a ventilator. However, proper auscultation of these patients is difficult when medical workers wear personal protective equipment and when it is necessary to minimize contact with patients.ObjectiveThe objective of our study was to design and develop a low-cost electronic stethoscope enabling ear-contactless auscultation and digital storage of data for further analysis. The clinical feasibility of our device was assessed in comparison to a standard electronic stethoscope.MethodsWe developed a prototype of the ear-contactless electronic stethoscope, called Auscul Pi, powered by Raspberry Pi and Python. Our device enables real-time capture of auscultation sounds with a microspeaker instead of an earpiece, and it can store data files for later analysis. We assessed the feasibility of using this stethoscope by detecting abnormal heart and respiratory sounds from 8 patients with heart failure or structural heart diseases and from 2 healthy volunteers and by comparing the results with those from a 3M Littmann electronic stethoscope.ResultsWe were able to conveniently operate Auscul Pi and precisely record the patients’ auscultation sounds. Auscul Pi showed similar real-time recording and playback performance to the Littmann stethoscope. The phonocardiograms of data obtained with the two stethoscopes were consistent and could be aligned with the cardiac cycles of the corresponding electrocardiograms. Pearson correlation analysis of amplitude data from the two types of phonocardiograms showed that Auscul Pi was correlated with the Littmann stethoscope with coefficients of 0.3245-0.5570 for healthy participants (P<.001) and of 0.3449-0.5138 among 4 patients (P<.001).ConclusionsAuscul Pi can be used for auscultation in clinical practice by applying real-time ear-contactless playback followed by quantitative analysis. Auscul Pi may allow accurate auscultation when medical workers are wearing protective suits and have difficulties in examining patients with COVID-19.Trial RegistrationChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971.

Characteristics of Aerobic Resistance Strength Training With Different Load Intensity Based on Acceleration Sensor

Objective: To compare the changes of kinematics and dynamics indexes of deep squat, bench press and hard pull in order to provide basis for the application of these three kinds of resistance strength training methods. Methods: The kinematic and dynamic characteristics of 30 young men in deep squat, bench press and hard pull were measured by the real-time wireless sensor motion capture and mechanical evaluation system of the wearable FAB. Results: In terms of kinematic parameters under 40%1RM and 80%1RM load, there were significant differences(P < 0.05) in flexion angle of deep squat hip joint, knee joint, hard pull hip joint and knee joint. There were significant differences in the angular velocity of trunk forward flexion and trunk extension, shoulder extension angle velocity of horizontal push, and flexion angular velocity of hard pull knee joint (P < 0.05), and significant differences were found in hip joint flexion angle acceleration, elbow extension angular acceleration in horizontal push, elbow flexion and knee joint flexion angle acceleration (P < 0.05). Conclusion: Hip joint and knee joint are the most important in deep squat and hard pull angle, angular velocity and angular acceleration, elbow joint is the most important one in bench press angle, angular velocity and angular acceleration.

Electronic application to improve management of infections in low-income neonatal units: pilot implementation of the NeoTree beta app in a public sector hospital in Zimbabwe

There are 2. 4 million annual neonatal deaths worldwide. Simple, evidence-based interventions such as temperature control could prevent approximately two-thirds of these deaths. However, key problems in implementing these interventions...

Development and Validation of an Automatic Image-Recognition Endoscopic Report Generation System: A Multicenter Study.

INTRODUCTION:Conventional gastrointestinal (GI) endoscopy reports written by physicians are time consuming and might have obvious heterogeneity or omissions, impairing the efficiency and multicenter consultation potential. We aimed to develop and validate an image recognition–based structured report generation system (ISRGS) through a multicenter database and to assess its diagnostic performance.Methods:First, we developed and evaluated an ISRGS combining real-time video capture, site identification, lesion detection, subcharacteristics analysis, and structured report generation. White light and chromoendoscopy images from patients with GI lesions were eligible for study inclusion. A total of 46,987 images from 9 tertiary hospitals were used to train, validate, and multicenter test (6:2:2). Moreover, 5,699 images were prospectively enrolled from Qilu Hospital of Shandong University to further assess the system in a prospective test set. The primary outcome was the diagnosis performance of GI lesions in multicenter and prospective tests.Results:The overall accuracy in identifying early esophageal cancer, early gastric cancer, early colorectal cancer, esophageal varices, reflux esophagitis, Barrett’s esophagus, chronic atrophic gastritis, gastric ulcer, colorectal polyp, and ulcerative colitis was 0.8841 (95% confidence interval, 0.8775–0.8904) and 0.8965 (0.8883–0.9041) in multicenter and prospective tests, respectively. The accuracy of cecum and upper GI site identification were 0.9978 (0.9969–0.9984) and 0.8513 (0.8399–0.8620), respectively. The accuracy of staining discrimination was 0.9489 (0.9396–0.9568). The relative error of size measurement was 4.04% (range 0.75%–7.39%).DISCUSSION:ISRGS is a reliable computer-aided endoscopic report generation system that might assist endoscopists working at various hospital levels to generate standardized and accurate endoscopy reports (http://links.lww.com/CTG/A485).

Application Analysis Of Smart Wearable Devices in Students’ Physical Activity Under the Background Of Big Data

With the rapid development of information technology, big data is gradually being popularized and promoted in various industries. Smart wearable devices have attracted much attention for their objective, accurate and continuous measurement of the amount of exercise. With the continuous improvement of intelligent equipment performance, large data analysis and intelligent wearable device technology can real-time capture the movement of the students in class data: trajectory, heart rate, speed, cost, etc., through data analysis, to monitor and intervene student’s sports activities in the class at the same time, can help the teacher more scientific system of the training plan, a more comprehensive understanding of the students in the class level and performance, enhance the level of student’s physical stamina and physical health and will bring far-reaching influence to sports research.

The prevalence of dyads in social life.

A salient objective feature of the social environment in which people find themselves is group size. Knowledge of group size is highly relevant to behavioural scientists given that humans spend considerable time in social settings and the number of others influences much of human behaviour. What size of group do people actually look for and encounter in everyday life? Here we report four survey studies and one experience-sampling study (total N = 4,398) which provide evidence for the predominance of the dyad in daily life. Relative to larger group sizes, dyads are most common across a wide range of activities (e.g., conversations, projects, holidays, movies, sports, bars) obtained from three time moments (past activities, present, and future activities), sampling both mixed-sex and same-sex groups, with three different methodological approaches (retrospective reports, real-time data capture, and preference measures) in the United States and the Netherlands. We offer four mechanisms that may help explain this finding: reciprocity, coordination, social exclusion, and reproduction. The present findings advance our understanding of how individuals organize themselves in everyday life.

A perspective on the use of ecological momentary assessment and intervention to promote stroke recovery and rehabilitation

ABSTRACT Motivated by recent advances in technologies, ecological momentary assessment (EMA), and ecological momentary intervention (EMI) have seen a rise in behavioral medicine research that in real-time, informs the context for the behavior and prompts interventions to change that behavior in the natural setting when necessary. However, EMA and EMI have yet to be fully embraced in the field of stroke rehabilitation. Our objective is to provide a theoretically based perspective for the combined and synergistic use of EMA and EMI to promote person-centered, recovery-based durable changes in functional movement behaviors of stroke survivors. Research abounds for non-stroke populations with emerging evidence for the benefits of using real-time data capture techniques (i.e. EMA) coupled with EMI to better customize the content and timing of interventions to the inherent fluctuations in state and context that encompass the target behavior. We review existing EMA and EMI literature broadly in behavioral medicine and psychological science to identify how real-time repeated sampling technology has been used in the context of stroke rehabilitation and to delineate the pros and cons of this approach in general with non-stroke populations. We propose a coupled EMA and EMI strategy be used in conjunction with existing stroke recovery and rehabilitation practices. There is tremendous potential to effectively personalize recovery-promoting interventions to achieve durable behavior change, and importantly, shift the focus of rehabilitation practice from the health-care provider and clinical environment to the individual and their lived experience in the home and community.

PhysCap

Marker-less 3D human motion capture from a single colour camera has seen significant progress. However, it is a very challenging and severely ill-posed problem. In consequence, even the most accurate state-of-the-art approaches have significant limitations. Purely kinematic formulations on the basis of individual joints or skeletons, and the frequent frame-wise reconstruction in state-of-the-art methods greatly limit 3D accuracy and temporal stability compared to multi-view or marker-based motion capture. Further, captured 3D poses are often physically incorrect and biomechanically implausible, or exhibit implausible environment interactions (floor penetration, foot skating, unnatural body leaning and strong shifting in depth), which is problematic for any use case in computer graphics. We, therefore, present PhysCap , the first algorithm for physically plausible, real-time and marker-less human 3D motion capture with a single colour camera at 25 fps. Our algorithm first captures 3D human poses purely kinematically. To this end, a CNN infers 2D and 3D joint positions, and subsequently, an inverse kinematics step finds space-time coherent joint angles and global 3D pose. Next, these kinematic reconstructions are used as constraints in a real-time physics-based pose optimiser that accounts for environment constraints ( e.g. , collision handling and floor placement), gravity, and biophysical plausibility of human postures. Our approach employs a combination of ground reaction force and residual force for plausible root control, and uses a trained neural network to detect foot contact events in images. Our method captures physically plausible and temporally stable global 3D human motion, without physically implausible postures, floor penetrations or foot skating, from video in real time and in general scenes. PhysCap achieves state-of-the-art accuracy on established pose benchmarks, and we propose new metrics to demonstrate the improved physical plausibility and temporal stability.