Action Sport Cameras as an Instrument to Perform a 3D Underwater Motion Analysis

Determining Validity and Reliability of an In-Field Performance Analysis System for Swimming

To permit the collection of quantitative data on start, turn and clean swimming performances in any swimming pool, the aims of the present study were to (1) validate a mobile in-field performance analysis system (PAS) against the Kistler starting block equipped with force plates and synchronized to a 2D camera system (KiSwim, Kistler, Winterthur, Switzerland), (2) assess the PAS's interrater reliability and (3) provide percentiles as reference values for elite junior and adult swimmers. Members of the Swiss junior and adult national swimming teams including medalists at Olympic Games, World and European Championships volunteered for the present study (n = 47; age: 17 ± 4 [range: 13-29] years; World Aquatics Points: 747 ± 100 [range: 527-994]). All start and turn trials were video-recorded and analyzed using two methods: PAS and KiSwim. The PAS involves one fixed view camera recording overwater start footage and a sport action camera that is moved underwater along the side of the pool perpendicular to the swimming lane on a 1.55 m long monostand. From a total of 25 parameters determined with the PAS, 16 are also measurable with the KiSwim, of which 7 parameters showed satisfactory validity (r = 0.95-1.00, p < 0.001, %-difference < 1%). Interrater reliability was determined for all 25 parameters of the PAS and reliability was accepted for 21 of those start, turn and swimming parameters (ICC = 0.78-1.00). The percentiles for all valid and reliable parameters provide reference values for assessment of start, turn and swimming performance for junior and adult national team swimmers. The in-field PAS provides a mobile method to assess start, turn and clean swimming performance with high validity and reliability. The analysis template and manual included in the present article aid the practical application of the PAS in research and development projects as well as academic works.

Validation of machine vision and action sport cameras for 3D motion analysis model reconstruction

The study investigated the feasibility of using action sport cameras for motion analysis research. Data acquired from two different marker-based motion capture systems and six different camera combinations were analyzed for motion reconstruction accuracy. Two different calibration procedures were used to determine the influence on marker position reconstruction. Static and dynamic calibration mean merit score differences between the reference and experimental camera systems were 0.4 mm and 1.3 mm, respectively. Angular displacement difference between the reference and experimental camera systems range between 0.1 and 2.0 degrees. A systematic bias (- 0.54 to 0.19 degrees) was determined between the reference and the experimental camera systems for range of motion. The mean of the multi-trial findings suggests the machine vision camera system calibrated with a dynamic procedure generated highly accurate three-dimensional reconstructed ROM data (0.5 degree) followed closely by the four action sport cameras implementing a static calibration procedure (0.5 degree). The overall findings suggest the selected machine vision and action sport camera systems produced comparable results to the reference motion analysis system. However, the combination of camera type, processing software, and calibration procedure can influence motion reconstruction accuracy.

Development of a Methodology for Low-Cost 3D Underwater Motion Capture: Application to the Biomechanics of Horse Swimming

Hydrotherapy has been utilized in horse rehabilitation programs for over four decades. However, a comprehensive description of the swimming cycle of horses is still lacking. One of the challenges in studying this motion is 3D underwater motion capture, which holds potential not only for understanding equine locomotion but also for enhancing human swimming performance. In this study, a marker-based system that combines underwater cameras and markers drawn on horses is developed. This system enables the reconstruction of the 3D motion of the front and hind limbs of six horses throughout an entire swimming cycle, with a total of twelve recordings. The procedures for pre- and post-processing the videos are described in detail, along with an assessment of the estimated error. This study estimates the reconstruction error on a checkerboard and computes an estimated error of less than 10 mm for segments of tens of centimeters and less than 1 degree for angles of tens of degrees. This study computes the 3D joint angles of the front limbs (shoulder, elbow, carpus, and front fetlock) and hind limbs (hip, stifle, tarsus, and hind fetlock) during a complete swimming cycle for the six horses. The ranges of motion observed are as follows: shoulder: 17 ± 3°; elbow: 76 ± 11°; carpus: 99 ± 10°; front fetlock: 68 ± 12°; hip: 39 ± 3°; stifle: 68 ± 7°; tarsus: 99 ± 6°; hind fetlock: 94 ± 8°. By comparing the joint angles during a swimming cycle to those observed during classical gaits, this study reveals a greater range of motion (ROM) for most joints during swimming, except for the front and hind fetlocks. This larger ROM is usually achieved through a larger maximal flexion angle (smaller minimal angle of the joints). Finally, the versatility of the system allows us to imagine applications outside the scope of horses, including other large animals and even humans.

A comparison of stride parameters and carpal and tarsal joint angles during terrestrial and swimming locomotion in domestic dogs

In recent years, canine hydrotherapy has become increasingly popular to treat a range of conditions despite a lack of empirical evidence. It is currently unclear whether joint angles and limb movements performed by dogs during swimming are quantifiably beneficial for healthy animals. This study investigated the swimming kinematics of healthy dogs to establish baseline data for this activity and compare limb kinematics to that of overground locomotion. Kinematic data were recorded from eight healthy dolichocephalic dogs (mean age: 3.4±2.2) of a variety of breeds. Overground data were collected prior to swimming and consisted of dogs trotting on a flat surface. Swimming data were collected using an underwater camera during a standard hydrotherapy session conducted by a trained canine hydrotherapist. Range of motion, primarily due to an increase in flexion, was significantly greater (P<0.005) during swimming than trotting. Stride length (P<0.001) and frequency (P<0.005) were both significantly reduced in swimming compared to trot. Swimming kinematics recorded in this study are consistent with previously published data on canine aquatic locomotion but differ from those previously reported for water treadmill exercise. This study provides an insight into aquatic locomotion in healthy dogs indicating that range of motion exceeds that of terrestrial gaits. It is unclear whether these changes are beneficial for healthy animals and therefore further research is required to develop evidence-based protocols for industry practice.

Localisation of Unmanned Underwater Vehicles (UUVs) in Complex and Confined Environments: A Review

The inspection of aquatic environments is a challenging activity, which is made more difficult if the environment is complex or confined, such as those that are found in nuclear storage facilities and accident sites, marinas and boatyards, liquid storage tanks, or flooded tunnels and sewers. Human inspections of these environments are often dangerous or infeasible, so remote inspection using unmanned underwater vehicles (UUVs) is used. Due to access restrictions and environmental limitations, such as low illumination levels, turbidity, and a lack of salient features, traditional localisation systems that have been developed for use in large bodies of water cannot be used. This means that UUV capabilities are severely restricted to manually controlled low-quality visual inspections, generating non-geospatially located data. The localisation of UUVs in these environments would enable the autonomous behaviour and the development of accurate maps. This article presents a review of the state-of-the-art in localisation technologies for these environments and identifies areas of future research to overcome the challenges posed.

Underwater Network Management System in Internet of Underwater Things: Open Challenges, Benefits, and Feasible Solution

As oceans cover the majority of the earth’s surface, it becomes inevitable in extending the concepts of Internet of Things (IoT) to ocean bodies, thereby tiling the way for a new drift in the digital world, the Internet of Underwater Things (IoUT). The primary objective of IoUT is the creation of a network of several smart interconnected undersea things, to digitally link water bodies by using devices such as autonomous underwater vehicles. Since the traditional ideas of IoT cannot be merely expanded to underwater, due to the difference in environmental characteristics, this puts forward a variety of challenges for scientists to work with IoUT, and one such challenge is the network management with IoUT. This paper gives an overview on (1) underwater network management systems (U-NMS) using acoustic communication in IoUT; (2) the challenges and benefits and use cases of U-NMS; (3) fault, configuration, accounting, performance, security and constrained management (FCAPSC) functionalities of U-NMS and (4) a comparison between network management system in IoT and U-NMS system in IoUT. Additionally, this paper shows the prototype design and implementation setup of U-NMS in a laboratory environment, using lightweight machine to machine (LWM2M) and acoustic communication technology for IoUT. This paper will contribute much to the profit of researchers and industry players in uncovering the critical areas of the Internet of Underwater Things.

Moving system with action sport cameras: 3D kinematics of the walking and running in a large volume

Traditionally, motion analysis in clinical laboratories using optoelectronic systems (MOCAP) is performed in acquisition volumes of limited size. Given the complexity and cost of MOCAP in larger volumes, action sports cameras (ASC) represent an alternative approach in which the cameras move along with the subject during the movement task. Thus, this study aims to compare ASC against a traditional MOCAP in the perspective of reconstructing walking and running movements in large spatial volumes, which extend over the common laboratory setup. The two systems, consisting of four cameras each, were closely mounted on a custom carrying structure endowed with wheels. Two different acquisition setups, namely steady and moving conditions, were taken into account. A devoted calibration procedure, using the same protocol for the two systems, enabled the reconstruction of surface markers, placed on voluntary subjects, during the two acquisition setups. The comparison was quantitatively expressed in terms of three-dimensional (3D) marker reconstruction and kinematic computation quality. The quality of the marker reconstruction quality was quantified by means of the mean absolute error (MAE) of inter-marker distance and two-stick angle. The kinematic computation quality was quantified by means of the measure of the knee angle reconstruction during walking and running trials. In order to evaluate the camera system and moving camera effects, we used a Wilcoxon rank sum test and a Kruskal Wallis test (post-hoc Tukey), respectively. The Spearman correlation coefficient (ρ) and the Wilcoxon rank sum test were applied to compare the kinematic data obtained by the two camera systems. We found small ASC MAE values (< 2.6mm and 1.3°), but they were significantly bigger than the MOCAP (< 0.7mm and 0.6°). However, for the human movement no significant differences were found between kinematic variables in walking and running acquisitions (p>0.05), and the motion patterns of the right-left knee angles between both systems were very similar (ρ>0.90, p<0.05). These results highlighted the promising results of a system that uses ASC based on the procedure of mobile cameras to follow the movement of the subject, allowing a less constrained movement in the direction in which the structure moves, compared to the traditional laboratory setup.

Are Action Sport Cameras Accurate Enough for 3D Motion Analysis? A Comparison With a Commercial Motion Capture System

The aim of this study was to assess the precision and accuracy of an Action Sport Camera (ASC) system (4 GoPro Hero3+ Black) by comparison with a commercial motion capture (MOCAP) system (4 ViconMX40). Both systems were calibrated using the MOCAP protocol and the 3D markers coordinates of a T-shaped tool were reconstructed, concurrently. The 3D precision was evaluated by the differences in the reconstructed position using a Bland-Altman test, while accuracy was assessed by a rigid bar test (Wilcoxon rank sum). To examine the accuracy of the ASC in respect to the knee flexion angles, a jump and gait task were also examined using one subject (Wilcoxon rank sum). The ASC system provided a maximum error of 2.47 mm, about 10 times higher than the MOCAP (0.21 mm). The reconstructed knee flexion angles were highly correlated (r²>0.99) and showed no significant differences between systems (<2.5°; p>0.05). As expected, the MOCAP obtained better 3D precision and accuracy. However, we show such differences have little practical effect on reconstructed 3D kinematics.

3D reconstruction of human movement in a single projection by dynamic marker scaling

The three dimensional (3D) reconstruction of movement from videos is widely utilized as a method for spatial analysis of movement. Several approaches exist for a 3D reconstruction of movement using 2D video projection, most of them require the use of at least two cameras as well as the application of relatively complex algorithms. While a few approaches also exist for 3D reconstruction of movement with a single camera, they are not widely implemented due to tedious and complicated methods of calibration. Here we propose a simple method that allows for a 3D reconstruction of movement by using a single projection and three calibration markers. Such approach is made possible by tracking the change in diameter of a moving spherical marker within a 2D projection. In order to test our model, we compared kinematic results obtained with this model to those with the commonly used approach of two cameras and Direct Linear Transformation (DLT). Our results show that such approach appears to be in line with the DLT method for 3D reconstruction and kinematic analysis. The simplicity of this method may render it approachable for both clinical use as well as in uncontrolled environments.

In-air versus underwater comparison of 3D reconstruction accuracy using action sport cameras

Action sport cameras (ASC) have achieved a large consensus for recreational purposes due to ongoing cost decrease, image resolution and frame rate increase, along with plug-and-play usability. Consequently, they have been recently considered for sport gesture studies and quantitative athletic performance evaluation. In this paper, we evaluated the potential of two ASCs (GoPro Hero3+) for in-air (laboratory) and underwater (swimming pool) three-dimensional (3D) motion analysis as a function of different camera setups involving the acquisition frequency, image resolution and field of view. This is motivated by the fact that in swimming, movement cycles are characterized by underwater and in-air phases what imposes the technical challenge of having a split volume configuration: an underwater measurement volume observed by underwater cameras and an in-air measurement volume observed by in-air cameras. The reconstruction of whole swimming cycles requires thus merging of simultaneous measurements acquired in both volumes. Characterizing and optimizing the instrumental errors of such a configuration makes mandatory the assessment of the instrumental errors of both volumes. In order to calibrate the camera stereo pair, black spherical markers placed on two calibration tools, used both in-air and underwater, and a two-step nonlinear optimization were exploited. The 3D reconstruction accuracy of testing markers and the repeatability of the estimated camera parameters accounted for system performance. For both environments, statistical tests were focused on the comparison of the different camera configurations. Then, each camera configuration was compared across the two environments. In all assessed resolutions, and in both environments, the reconstruction error (true distance between the two testing markers) was less than 3mm and the error related to the working volume diagonal was in the range of 1:2000 (3×1.3×1.5m³) to 1:7000 (4.5×2.2×1.5m³) in agreement with the literature. Statistically, the 3D accuracy obtained in the in-air environment was poorer (p<10^-5) than the one in the underwater environment, across all the tested camera configurations. Related to the repeatability of the camera parameters, we found a very low variability in both environments (1.7% and 2.9%, in-air and underwater). This result encourage the use of ASC technology to perform quantitative reconstruction both in-air and underwater environments.