High-quality 3D reconstruction is an important topic in computer graphics and computer vision with many applications, such as robotics and augmented reality. The advent of consumer RGB-D cameras has made a profound advance in indoor scene reconstruction. For the past few years, researchers have spent significant effort to develop algorithms to capture 3D models with RGB-D cameras. As depth images produced by consumer RGB-D cameras are noisy and incomplete when surfaces are shiny, bright, transparent, or far from the camera, obtaining high-quality 3D scene models is still a challenge for existing systems. We here review high-quality 3D indoor scene reconstruction methods using consumer RGB-D cameras. In this paper, we make comparisons and analyses from the following aspects: (i) depth processing methods in 3D reconstruction are reviewed in terms of enhancement and completion, (ii) ICP-based, feature-based, and hybrid methods of camera pose estimation methods are reviewed, and (iii) surface reconstruction methods are reviewed in terms of surface fusion, optimization, and completion. The performance of state-of-the-art methods is also compared and analyzed. This survey will be useful for researchers who want to follow best practices in designing new high-quality 3D reconstruction methods.

Background. Quantitative assessment of motor function is extremely important for poststroke patients as it can be used to develop personalized treatment strategies. This study aimed to propose an evaluation method for upper limb motor function in stroke patients. Methods. Thirty-four stroke survivors and twenty-five age-matched healthy volunteers as the control group were recruited for this study. Inertial sensor data and surface electromyography (sEMG) signals were collected from the upper limb during voluntary upward reaching. Five features included max shoulder joint angle, peak and average speeds, torso balance calculated from inertial sensor data, and muscle synergy similarity extracted from sEMG data by the nonnegative matrix factorization algorithm. Meanwhile, the Fugl–Meyer score of each patient was graded by professional rehabilitation therapist. Results. Statistically significant differences were observed among severe, mild-to-moderate, and control group of five features ( 0.001). The features varied as the level of upper limb motor function changes since these features significantly correlated with the Fugl–Meyer assessment scale ( 0.001). Moreover, the Bland–Altman method was conducted and showed high consistency between the evaluation method of five features and Fugl–Meyer scale. Therefore, the five features proposed in this paper can quantitatively evaluate the motor function of stroke patients which is very useful in the rehabilitation process.

This paper presents a dataset for vision-based autonomous Functional Movement Screen (FMS) collected from 45 human subjects of different ages (18–59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, quaternions, 3D human skeleton joints and 2D pixel trajectories of 32 joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 190 GB. This dataset provides the opportunity for automatic action quality evaluation of FMS.

Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player’s style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016–2019 CSL were used. Match ratings, and 10 types of match events with the corresponding coordinates for all the line-up players whose on-pitch time exceeded 45 minutes were extracted. Players were first clustered into eight positions. A player vector was constructed for each player in each match based on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF process was run on the player vectors to extract different types of playing styles. The resulting player vectors discovered 18 different playing styles in the CSL. Six performance indicators of each style were investigated to observe their contributions. In general, the playing styles of forwards and midfielders are in line with football performance evolution trends, while the styles of defenders should be reconsidered. Multifunctional playing styles were also found in high-rated CSL players.

Background: Functional movement screening (FMS) allows for the rapid assessment of an individual’s physical activity level and the timely detection of sports injury risk. However, traditional functional movement screening often requires on-site assessment by experts, which is time-consuming and prone to subjective bias. Therefore, the study of automated functional movement screening has become increasingly important. Methods: In this study, we propose an automated assessment method for FMS based on an improved Gaussian mixture model (GMM). First, the oversampling of minority samples is conducted, the movement features are manually extracted from the FMS dataset collected with two Azure Kinect depth sensors; then, we train the Gaussian mixture model with different scores (1 point, 2 points, 3 points) of feature data separately; finally, we conducted FMS assessment by using a maximum likelihood estimation. Results: The improved GMM has a higher scoring accuracy (improved GMM: 0.8) compared to other models (traditional GMM = 0.38, AdaBoost.M1 = 0.7, Naïve Bayes = 0.75), and the scoring results of improved GMM have a high level of agreement with the expert scoring (kappa = 0.67). Conclusions: The results show that the proposed method based on the improved Gaussian mixture model can effectively perform the FMS assessment task, and it is potentially feasible to use depth cameras for FMS assessment.

Postural abnormalities have become a prevalent issue affecting individuals of all ages, resulting in a diminished quality of life. Easy-use and reliable posture assessment tools can aid in screening for and correcting posture deviation at an early stage. In this study, we present a depth camera-based static posture assessment system to screen for common postural anomalies such as uneven shoulders, pelvic tilt, bowlegs and knock-knees, forward head, scoliosis, and shoulder blade inclination. The system consists of an Azure Kinect camera, a laptop, and evaluation software. Our system accurately measures skeleton and posture indexes and shows favorable agreement with a golden standard optical infrared motion capture system. The findings indicate that the system is a low-cost posture assessment tool with high precision and accuracy, suitable for initial screening of postural abnormalities in individuals of all ages.