Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

3D technologies for intangible cultural heritage preservation—literature review for selected databases

Conservation of cultural heritage is nowadays a very important aspect of our lives. Thanks to such legacy we gain knowledge about our ancestors, methods of production and ways of their life. The rapid development of 3D technology allows for more and more faithful reflection of this area of life. The rich cultural heritage, both tangible and intangible, can be preserved for future generations due to the use of advanced 3d technologies. They provide the means of documenting, recovering and presenting items of cultural heritage. Not only buildings or monuments are taken into account. An important aspect of our culture is intangible cultural heritage (ICH), including acting, crafting or storytelling, passed down from generation to generation. Due to the rapid development of civilisation and the migration of people, this type of culture is often forgotten. That is why the preservation of ICH is an important element of today world. The main aim of this study, on the basis of the gathered papers, is to identify: (1) the general state of use of 3D digital technologies in ICH; (2) the topics and themes discussed; (3) the technologies used in the study; (4) locations of research centres conducting such studies; and (5) the types of research carried out. The methodology consists of the following main steps: defining study questions, searching query development, selection of publications in Scopus, Web of Knowledge and IEEE Xplore, finally the study execution and the analysis of the obtained results. The results show that for ICH the most often used technologies are: 3D visualisation, 3D modelling, Augmented Reality, Virtual Reality and motion capture systems.

Compensating for Soft-Tissue Artifact Using the Orientation of Distal Limb Segments During Electromagnetic Motion Capture of the Upper Limb

Most motion capture measurements suffer from soft-tissue artifacts (STA). Especially affected are rotations about the long axis of a limb segment, such as humeral internal-external rotation (HIER) and forearm pronation-supination (FPS). Unfortunately, most existing methods to compensate for STA were designed for optoelectronic motion capture systems. We present and evaluate a STA compensation method that 1) compensates for STA in HIER and/or FPS, 2) is developed specifically for electromagnetic motion capture systems, and 3) does not require additional calibration or data. To compensate for STA, calculation of HIER angles rely on forearm orientation, and calculation of FPS angles rely on hand orientation. To test this approach, we recorded whole-arm movement data from eight subjects and compared their joint angle trajectories calculated according to progressive levels of STA compensation. Compensated HIER and FPS angles were significantly larger than uncompensated angles. Although the effect of STA compensation on other joint angles (besides HIER and FPS) was usually modest, significant effects were seen in certain DOF under some conditions. Overall, the method functioned as intended during most of the range of motion of the upper limb, but it becomes unstable in extreme elbow extension and extreme wrist flexion-extension. Specifically, this method is not recommended for movements within 20° of full elbow extension, full wrist flexion, or full wrist extension. Since this method does not require additional calibration of data, it can be applied retroactively to data collected without the intent to compensate for STA.

Use of the Azure Kinect to measure foot clearance during obstacle crossing

Obstacle crossing is a typical adaptive locomotion known to be related to the risk of falls. Previous conventional studies have used elaborate and costly optical motion capture systems, which not only represent a considerable expense but also require participants to visit a laboratory. To overcome these shortcomings, we aimed to develop a practical and inexpensive solution for measuring obstacle-crossing behavior by using the Microsoft Azure Kinect, one of the most promising markerless motion capture systems. We validated the Azure Kinect as a tool to measure foot clearance and compared its performance to that of an optical motion capture system (Qualisys). We also determined the effect of the Kinect sensor placement on measurement performance. Sixteen healthy young men crossed obstacles of different heights (50, 150, and 250 mm). Kinect sensors were placed in front of and beside the obstacle as well as diagonally between those positions. As indices of measurement quality, we counted the number of measurement failures and calculated the systematic and random errors between the foot clearance measured by the Kinect and Qualisys. We also calculated the Pearson correlation coefficients between the Kinect and Qualisys measurements. The number of measurement failures and the systematic and random error were minimized when the Kinect was placed diagonally in front of the obstacle on the same side as the trail limb. The high correlation coefficient (r > 0.890) observed between the Kinect and Qualisys measurements suggests that the Azure Kinect has excellent potential for measuring foot clearance during obstacle-crossing tasks.

Time-optimal planning for quadrotor waypoint flight

Quadrotors are among the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential because of their inherent smoothness. Recent works resorted to numerical optimization but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time allocation is a priori unknown and renders previous works incapable of producing truly time-optimal trajectories. To generate truly time-optimal trajectories, we propose a solution to the time allocation problem while exploiting the full quadrotor’s actuator potential. We achieve this by introducing a formulation of progress along the trajectory, which enables the simultaneous optimization of the time allocation and the trajectory itself. We compare our method against related approaches and validate it in real-world flights in one of the world’s largest motion-capture systems, where we outperform human expert drone pilots in a drone-racing task.

Using tripartite group area as a measure of social interactions in pre-school children: A pilot study

This study aimed to measure tripartite group area using motion capture systems and investigated whether group area could be used as a measure of pre-school children’s social interactions. In Experiment 1, two typically developing girls and an adult staff member engaged in free play. In Experiment 2, two typically developing boys and two adult staff members played balloon volleyball. Both experiments had three types of measures: subjective evaluation of whether participants played together, social behaviours (e.g. eye contact for Experiment 1 and balloon tosses for Experiment 2) and group area. Results showed that group area was significantly and negatively related to subjective evaluation in Experiment 2, whereas we observed no relationship between subjective evaluation and group area in Experiment 1. Overall, however, only a low correlation was observed between subjective evaluation and group area in Experiment 2. Furthermore, there were strong sequential associations between subjective evaluation and social behaviour, rather than between subjective evaluation and group area. Although group area as an index of social interactions is less accurate than behavioural data directly observed by humans, it may be worth using as a low-cost preliminary measure, since it can be automatically calculated using motion capture systems.

A Hand-Worn Inertial Measurement Unit for Detection of Bat–Ball Impact during Baseball Hitting

Swinging a baseball bat at a pitched ball takes less than half of a second. A hitter uses his lower extremities to generate power, and coordination of the swing motion gradually transfers power through the trunk to the upper extremities during bat–ball impact. The most important instant of the baseball swing is at the bat–ball impact, after which the direction, speed, height, and distance of the hit ball determines whether runs can be scored. Thus, analyzing the biomechanical parameters at the bat–ball impact is useful for evaluating player performance. Different motion-capture systems use different methods to identify bat–ball impact. However, the level of accuracy to detect bat–ball impact is not well documented. The study aim was to examine the required accuracy to detect bat–ball impact timing. The results revealed that ±2 ms accuracy is required to report trunk and hand kinematics, especially for higher-order time-derivatives. Here, we propose a new method using a hand-worn inertial measurement unit to accurately detect bat–ball impact timing. The results of this study will be beneficial for analyzing the kinematics of baseball hitting under real-game conditions.

Marker-Less Motion Capture of Insect Locomotion With Deep Neural Networks Pre-trained on Synthetic Videos

Motion capture of unrestrained moving animals is a major analytic tool in neuroethology and behavioral physiology. At present, several motion capture methodologies have been developed, all of which have particular limitations regarding experimental application. Whereas marker-based motion capture systems are very robust and easily adjusted to suit different setups, tracked species, or body parts, they cannot be applied in experimental situations where markers obstruct the natural behavior (e.g., when tracking delicate, elastic, and/or sensitive body structures). On the other hand, marker-less motion capture systems typically require setup- and animal-specific adjustments, for example by means of tailored image processing, decision heuristics, and/or machine learning of specific sample data. Among the latter, deep-learning approaches have become very popular because of their applicability to virtually any sample of video data. Nevertheless, concise evaluation of their training requirements has rarely been done, particularly with regard to the transfer of trained networks from one application to another. To address this issue, the present study uses insect locomotion as a showcase example for systematic evaluation of variation and augmentation of the training data. For that, we use artificially generated video sequences with known combinations of observed, real animal postures and randomized body position, orientation, and size. Moreover, we evaluate the generalization ability of networks that have been pre-trained on synthetic videos to video recordings of real walking insects, and estimate the benefit in terms of reduced requirement for manual annotation. We show that tracking performance is affected only little by scaling factors ranging from 0.5 to 1.5. As expected from convolutional networks, the translation of the animal has no effect. On the other hand, we show that sufficient variation of rotation in the training data is essential for performance, and make concise suggestions about how much variation is required. Our results on transfer from synthetic to real videos show that pre-training reduces the amount of necessary manual annotation by about 50%.