Depth ambiguity is one of the main challenges of three-dimensional (3D) human pose estimation (HPE). The recent strategies of disambiguating have brought significant progress and remarkable breakthroughs in the field of 3D human pose estimation (3D HPE). This survey extensively reviews the causes and solutions of the depth ambiguity. The solutions are systematically classified into four categories: camera parameter constraints, temporal consistency constraints, kinematic constraints, and image cues constraints. This paper summarizes the performance comparison, challenges, main frameworks, and evaluation metrics, and discusses some promising future research directions.

Background: Hand, foot, and mouth disease (HFMD) is a highly contagious viral illness. Understanding the long-term trends of HFMD incidence and its epidemic characteristics under the circumstances of the enterovirus 71 (EV71) vaccination program and the outbreak of COVID-19 is crucial for effective disease surveillance and control. Objective: We aim to give an overview of the trends of HFMD over the past decades and evaluate the impact of the EV71 vaccination program and the COVID-19 pandemic on the epidemic trends of HFMD. Methods: Using official surveillance data from the Yunnan Province, China, we described long-term incidence trends and severity rates of HFMD as well as the variation of enterovirus proportions among cases. We conducted the autoregressive integrated moving average (ARIMA) of time series analyses to predict monthly incidences based on given subsets. The difference between the actual incidences and their counterfactual predictions was compared using absolute percentage errors (APEs) for periods after the EV71 vaccination program and the COVID-19 pandemic, respectively. Results: The annual incidence of HFMD fluctuated between 25.62 cases per 100,000 people in 2008 and 221.52 cases per 100,000 people in 2018. The incidence for men ranged from 30 to 250 cases per 100,000 people from 2008 to 2021, which was constantly higher than that for women. The annual incidence for children aged 1 to 2 years old ranged from 54.54 to 630.06 cases per 100,000 people, which was persistently higher than that for other age groups. For monthly incidences, semiannual peaks were observed for each year. All actual monthly incidences of 2014 to 2015 fell within the predicted 95% CI by the ARIMA(1,0,1)(1,1,0)[12] model. The average APE was 19% for a 2-year prediction. After the EV71 vaccination program, the actual monthly incidence of HFMD was consistently lower than the counterfactual predictions by ARIMA(1,0,1)(1,1,0)[12], with negative APEs ranging from −11% to −229% from January 2017 to April 2018. In the meantime, the proportion of EV71 among the enteroviruses causing HFMD decreased significantly, and the proportion was highly correlated (r=0.73, P=.004) with the severity rate. After the onset of the COVID-19 pandemic in 2020, the actual monthly incidence of HFMD consistently maintained a lower magnitude compared to the counterfactual predictions—ARIMA(1,0,1)(0,1,0)[12]—from February to September 2020, with considerable negative APEs (ranging from −31% to −2248%). Conclusions: EV71 vaccination alleviated severe HFMD cases and altered epidemiological trends. The HFMD may also benefit from nonpharmaceutical interventions during outbreaks such as the COVID-19 pandemic. Further development of a multivalent virus vaccine is crucial for effectively controlling HFMD outbreaks. Policymakers should implement nonpharmaceutical interventions and emphasize personal hygiene for routine prevention when appropriate.

Aiming at the problems of the low evaluation efficiency of the existing traditional cardiopulmonary resuscitation (CPR) training mode and the considerable development of machine vision technology, a quality evaluation algorithm for chest compressions (CCs) based on the OpenPose human pose estimation (HPE) model is proposed. Firstly, five evaluation criteria are proposed based on major international CPR guidelines along with our experimental study on elbow straightness. Then, the OpenPose network is applied to obtain the coordinates of the key points of the human skeleton. The algorithm subsequently calculates the geometric angles and displacement of the selected joint key points using the detected coordinates. Finally, it determines whether the compression posture is standard, and it calculates the depth, frequency, position and chest rebound, which are the critical evaluation metrics of CCs. Experimental results show that the average accuracy of network behavior detection reaches 94.85%, and detection speed reaches 25 fps.

Traditional cardiopulmonary resuscitation (CPR) training faces limitations such as instructor dependency, low efficiency, and subjective assessment. To address these issues, this study proposes a novel computer vision-based method for the automation and objective evaluation of artificial respiration, shifting focus to the long-overlooked ventilation component. We developed an evaluation framework integrating human pose estimation and spatio-temporal graph convolution network (ST-GCN): first, OpenPose is utilized to extract skeletal keypoints of the rescuer, followed by action classification and recognition-including chest compressions, airway opening, and artificial breathing via a ST-GCN. Based on the American Heart Association (AHA) guidelines, this research defines and implements five quantitative metrics for ventilation quality, including CPR operation procedure, chin-frontal angle, interruption time, ventilation time, and ventilation frequency. An automated scoring model was established accordingly. Validated on a self-constructed dataset containing multi-source videos, the model achieved an accuracy of 87.64% in recognizing artificial respiration actions and 84.47% in evaluating action standardization. Experimental results demonstrate that the system can effectively and objectively evaluate the quality of artificial respiration. Compared with traditional instructor-dependent approaches, this study provides a low-cost, scalable technical solution, offering a new pathway for promoting high-quality CPR training.