Video Camera Research Articles

Predicting Knee Joint Contact Force Peaks During Gait Using a Video Camera or Wearable Sensors.

Estimating loading of the knee joint may be helpful in managing degenerative joint diseases. Contemporary methods to estimate loading involve calculating knee joint contact forces using musculoskeletal modeling and simulation from motion capture (MOCAP) data, which must be collected in a specialized environment and analyzed by a trained expert. To make the estimation of knee joint loading more accessible, simple input predictors should be used for predicting knee joint loading using artificial neural networks. We trained feedforward artificial neural networks (ANNs) to predict knee joint loading peaks from the mass, height, age, sex, walking speed, and knee flexion angle (KFA) of subjects using their existing MOCAP data. We also collected an independent MOCAP dataset while recording walking with a video camera (VC) and inertial measurement units (IMUs). We quantified the prediction accuracy of the ANNs using walking speed and KFA estimates from (1) MOCAP data, (2) VC data, and (3) IMU data separately (i.e., we quantified three sets of prediction accuracy metrics). Using portable modalities, we achieved prediction accuracies between 0.13 and 0.37 root mean square error normalized to the mean of the musculoskeletal analysis-based reference values. The correlation between the predicted and reference loading peaks varied between 0.65 and 0.91. This was comparable to the prediction accuracies obtained when obtaining predictors from motion capture data. The prediction results show that both VCs and IMUs can be used to estimate predictors that can be used in estimating knee joint loading outside the motion laboratory. Future studies should investigate the usability of the methods in an out-of-laboratory setting.

Collapse of the Dharahara Tower During the April 25, 2015 Nepal Earthquake: A New Interpretation Based on Video-Camera Footage

ABSTRACT Among the various important monuments impacted by the April 25, 2015 Mw7.8 Nepal earthquake, the Dharahara Tower located in Kathmandu suffered complete collapse. The collapse of the tower, a slender 60-meter-high old brick masonry structure with an almost cylindrical hollow shape, was captured by a low-quality video camera, which nonetheless helped to identify the critical moments. Initially, the tower descended more than 10 m vertically after a rupture at the base. After a few seconds, it overturned, disintegrating upon impact on the ground. At a distance of 1.3 km, an accelerometric station recorded the strong motion which was used as input of a discrete element model of the structure. Mechanical properties were taken from lab tests made on similar building materials. The Dharahara Tower had previously been analysed by other researchers using various methods. However, since they were apparently not aware of the mechanism observed in the video footage, their collapse models do not agree with this new evidence. For the initial phase of the collapse, the present model explains with great accuracy the observations of the video footage, but the final phase is dependent on various details, and modelling is not so robust.

Continuing Current Seen Above and Below the Cloud: Comparing Observations From GLM and High‐Speed Video Cameras

Continuing Current Seen Above and Below the Cloud: Comparing Observations From GLM and High‐Speed Video Cameras

Transition from bouncing to rolling on a horizontal surface

If a ball is incident obliquely on a horizontal surface and is allowed to bounce more than once, then it is likely to bounce many times before it starts rolling along the surface. The number of bounces before rolling commences depends on the initial vertical speed and the normal coefficient of restitution. The transition from bouncing to rolling is examined using a simple theoretical model and is compared with experimental data obtained by filming the process with a video camera. We find that the final rolling speed is proportional to the initial horizontal speed of the ball and depends on the initial ball spin, but is independent of the tangential coefficient of restitution. Representative videos for different balls are included as supplementary material, including a superball thrown with a backspin that creates a back and forth motion. Instructors could use the experiment and/or analysis for an advanced undergraduate lab or use a simplified observational exercise for non-majors.

Cosmic pears from the Havelland (Germany): Ribbeck, the twelfth recorded aubrite fall in history

AbstractIn 1889 the German poet and novelist Theodor Fontane wrote the popular literary ballad “Herr von Ribbeck auf Ribbeck im Havelland.” The Squire von Ribbeck is described as a gentle and generous person, who often gives away pears from his pear trees to children passing by and continued donating pears after his death. Now, 135 years later the rock called Ribbeck is giving us insight into processes that happened 4.5 billion years ago. The meteorite Ribbeck (official find location: 52°37′15″N, 12°45′40″E) fell January 21, 2024, and has been classified as a brecciated aubrite. This meteoroid actually entered the Earth's atmosphere at 00:32:38 UTC over Brandenburg, west of Berlin, and the corresponding fireball was recorded by professional all sky and video cameras. More than 200 pieces (two proved by radionuclide analysis to belong to this fresh fall) were recovered totaling about 1.8 kg. Long‐lived radionuclide and noble gas data are consistent with long cosmic ray exposure (55–62 Ma) and a preatmospheric radius of Ribbeck between 20 and 30 cm. The heavily brecciated aubrite consists of major (76 ± 3 vol%) coarse‐grained FeO‐free enstatite (En99.1Fs<0.04Wo0.9), with a significant abundance (15.0 ± 2.5 vol%) of albitic plagioclase (Ab95.3 An2.0Or2.7), minor forsterite (5.5 ± 1.5 vol%; Fo99.9) and 3.5 ± 1.0 vol% of opaque phases (mainly sulfides and metals) with traces of nearly FeO‐free diopside (En53.2Wo46.8) and K‐feldspar (Ab4.6Or95.4). The rock has a shock degree of S3 (U‐S3), and terrestrial weathering has affected metals and sulfides, resulting in the brownish appearance of rock pieces and the partial destruction of certain sulfides already within days after the fall. The bulk chemical data confirm the feldspar‐bearing aubritic composition. Ribbeck is closely related to the aubrite Bishopville. Ribbeck does not contain solar wind implanted gases and is a fragmental breccia. Concerning the Ti‐ and O‐isotope compositions, the data are similar to those of other aubrites. They are also similar to E chondrites and fall close to the data point for the bulk silicate Earth (BSE). Before the Ribbeck meteoroid entered Earth's atmosphere, it was observed in space as asteroid 2024 BX1. The aphelion distance of 2024 BX1's orbit lies in the innermost region of the asteroid belt, which is populated by the Hungaria family of minor planets characterized by their E/X‐type taxonomy and considered as the likely source of aubrites. The spectral comparison of an average large‐scale emission spectrum of Mercury converted into reflectance and of the Ribbeck meteorite spectrum does not show any meaningful similarities.

A robust method for multi object tracking in autonomous ship navigation systems

A novel Maritime Multi-Object Tracking method is proposed, combining a deep learning-based object detector with target association algorithms to achieve robust sea-surface multi-object tracking. Specifically, the Multi-Object Tracking employs You Only Look Once version 7 detector for object detection. In the data association part, a module for onboard camera motion compensation is developed, a maritime dynamic spatial information-based intersection-over-union is presented as a similarity metric, and a progressive refinement cascade matching strategy is designed to enhance the tracker's sea-surface multi-object tracking capabilities. The Jari Maritime Tracking Dataset is utilised to validate the effectiveness performance of the proposed method. Experimental results demonstrate that compared to the earlier process, the proposed method exhibits a significant enhancement in multiple object tracking accuracy, with an increase of 27.8% and an achieved score of 81.3. In particular, it reduces the number of identifications switching and missed targets, achieving holistically preferable performance. Meanwhile, the speed of the proposed Multi-Object Tracking fulfils the engineering application requirements for an autonomous ship navigation system.

Designing for Networked Learning in 360VR - A scenographic turn in online learning environments?

Despite the growing popularity of Virtual Reality (VR) in Higher Education (HE), there is a lack of studies dealing with networked and collaborative activities in VR (Radianti et al., 2020). Historically, VR has been promoted as an educational technology that can give access to exotic places or dangerous situations through computer-generated virtual worlds. Another area of application of VR has been therapeutical sessions exposing individuals to unknown situations. Concepts like interactivity, immersion and presence has been shaping the discourse of educational VR (Markowitz & Bailenson, 2019), but that is not necessarily supporting a networked or collaborative learning approach. Recently, 360-degree video cameras has made it possible to record situated practices, which can then be used as the canvas in the virtual world (McIlvenny & Davidsen, 2017). Pirker et al. (2021) argued that 360VR could potentially be a game-changer for distant education, but it is also clear that 360VR pose a new medium for supporting Networked Learning. Basically, 360VR presents a transition from logocentric platforms (e.g. Moodle) emphasising the exchange of text between peers towards platforms that build upon ideas of immersion, inhabitation and multimodality. The aim of the workshop is to discuss how principles of Networked Learning can inform the design of 360VR activities in HE. CAVA360VR is a prototype Unity-based Windows application supporting 20 simultaneous participants to collaboratively analyse, visualise and annotate 360° video in VR. CAVA360VR is developed by the BigSoftVideo team (www.bigvideo.aau.dk) in Aalborg University (McIlvenny, 2020). In CAVA360VR, remote participants can share, view and interact with a 360° video together, draw on the 360° video, use a ‘mirror-cam’ to see what is behind you, use a laser pointer to guide others’ attention, import a 2D image, view a transcript, and view a synced 2D video with the 360° video. Further, participants can talk to each other, and the audio is spatialized in the VR environment. In CAVA360VR, each participant is represented with an avatar that follows the orientation of the Head Mounted Display (HMD) of the individual participant and the controllers are showed as pair of avatar hands. The potential of CAVA360VR is also particularly interesting in the context of Networked Learning as it offers a new platform for designing for learning. CAVA360VR is not only available in VR, but can also run as a standard desktop application, which allows a larger, mixed group to participate in the analysis of the recorded data. Not all of the features available in VR are available in non-VR mode. For two years, CAVA360VR has been used in many video data sessions (Jordan & Henderson, 1995; McIlvenny, 2020) with participants – for example, from Ghana, Finland and Denmark – analysing 360° video data together. The potential of CAVA360VR is also particularly interesting in the context of Networked Learning as it offers a new platform for designing for learning. This includes addressing how to collect 360 video data, how to pedagogically design activities, and how to support students negotiating of meaning in 360VR, etc.

Synchronising a stereoscopic surgical video stream using specular reflection.

A stereoscopic surgical video stream consists of left-right image pairs provided by a stereo endoscope. While the surgical display shows these image pairs synchronised, most capture cards cause de-synchronisation. This means that the paired left and right images may not correspond once used in downstream tasks such as stereo depth computation. The stereo synchronisation problem is to recover the corresponding left-right images. This is particularly challenging in the surgical setting, owing to the moist tissues, rapid camera motion, quasi-staticity and real-time processing requirement. Existing methods exploit image cues from the diffuse reflection component and are defeated by the above challenges. We propose to exploit the specular reflection. Specifically, we propose a powerful left-right comparison score (LRCS) using the specular highlights commonly occurring on moist tissues. We detect the highlights using a neural network, characterise them with invariant descriptors, match them, and use the number of matches to form the proposed LRCS. We perform evaluation against 147 existing LRCS in 44 challenging robotic partial nephrectomy and robotic-assisted hepatic resection video sequences with simulated and real de-synchronisation. The proposed LRCS outperforms, with an average and maximum offsets of 0.055 and 1 frames and 94.1±3.6% successfully synchronised frames. In contrast, the best existing LRCS achieves an average and maximum offsets of 0.3 and 3 frames and 81.2±6.4% successfully synchronised frames. The use of specular reflection brings a tremendous boost to the real-time surgical stereo synchronisation problem.

Impact of woody biomass in the composition of coal-water suspensions on their transportation and combustion

Relevance. A promising fuel from an environmental point of view is coal-water suspensions. Multiple studies have shown that when they are burned, emissions of anthropogenic gases into the Earth's atmosphere are significantly reduced compared to emissions from coal burning. But the large-scale introduction of coal-water suspensions into the overall balance of energy production is difficult due to a significant delay in their ignition, which can reach several tens of seconds at relatively low oxidizer temperatures (Tg≤1073 K). One of the possible ways to solve this problem is the use of new technologies for preparation for combustion, for example, the use of additional microwave heating of fuel and the introduction of special additives into the composition of coal-water suspensions, which accelerate their ignition. The introduction of such additives into the fuel composition can lead to a significant change in the rheological characteristics. Aim. To determine the degree of impact of the concentration and type of wood additive in the composition of the coal-water suspension on the rheological characteristics of droplets of coal-water fuel, as well as the possibility of reducing the duration of thermal preparation of the latter when exposed to microwave heating. Object. Water-coal suspension based on long-flame thermal coal and on long-flame thermal coal with the addition of sawdust and pine needles (Bio-water-coal suspension). Methods. Experimental studies to determine the time characteristics of water-coal suspension ignition were carried out using a high-speed video camera FASTCAM. Fuel combustion was carried out in a flow-through combustion chamber equipped with magnetrons for additional microwave heating of the fuel. Dynamic viscosity was determined using a Brookfield RVDV-II + Pro viscometer. Results. It has been established that the addition of microwave heating can significantly (up to 15%) reduce the ignition delay of water-coal suspension and Bio-water-coal suspension at relatively low oxidizer temperatures (673 K). Experimental studies shown that the type of biomass affects the time characteristics of fuel ignition at ambient temperatures up to 753 K; with a further increase in the temperature of the oxidizer, the type of additive does not have a significant effect. It is shown that when studying the rheological characteristics of the water-coal suspension, the dynamic viscosity of Bio-water-coal suspension does not go beyond the characteristic value of 1200 Pa s at a shear rate of 100 rpm when no more than 2% of pine sawdust and no more than 6% of pine needles are introduced into the water-coal suspension.

Foothold selection during locomotion in uneven terrain: Results from the integration of eye tracking, motion capture, and photogrammetry.

Relatively little is known about the way vision is used to guide locomotion in the natural world. What visual features are used to choose paths in natural complex terrain? To answer this question, we measured eye and body movements while participants walked in natural outdoor environments. We incorporated measurements of the 3D terrain structure into our analyses and reconstructed the terrain along the walker's path, applying photogrammetry techniques to the eyetracker's scene camera videos. Combining these reconstructions with the walker's body movements, we demonstrate that walkers take terrain structure into account when selecting paths through an environment. We find that they change direction to avoid taking steeper steps that involve large height changes, instead of choosing more circuitous, relatively flat paths. Our data suggest walkers plan the location of individual footholds and plan ahead to select flatter paths. These results provide evidence that locomotor behavior in natural environments is controlled by decision mechanisms that account for multiple factors, including sensory and motor information, costs, and path planning.

Trunk kinematics and limb movement of horses walking backwards and forwards in hand and lifting a single limb

Equine physiotherapy commonly includes basic exercises such as walking backward (BW) and voluntary lifting of single limbs (SLL), but trunk movements during these have not been studied. In order to compare the trunk kinematics during BW and SLL with forward walking (FW), nine horses were measured in FW, BW and during SLL triggered by tactile cue. Kinematics were obtained from skin markers captured by ten high-speed video cameras. Trunk angles were calculated in sagittal and horizontal planes from withers, dorsal to spinous processes of the 16th thoracic vertebra (T16), 2nd and 4th sacral vertebrae (S2, S4), WT16S2 and T16S2S4 respectively. From the hooves, maximum hoof height during swing phase and horizontal distance between hoof and median body plane during swing and stance phases were determined.Dorsoventral range of motion (ROM) and maximum flexion of WT16S2 was significantly larger in BW than in FW, while laterolateral ROM was significantly smaller during hindlimb swing phase in BW and SLL than in FW. In contrast, dorsoventral ROM of T16S2S4 was significantly smaller during stance and swing phases of hindlimbs in BW compared to FW, and throughout the movement. During forelimb swing phase, T16S2S4 ROM was significantly larger in BW than SLL. Hindhoof height in SLL was significantly higher than in FW. Distance between median body plane and hooves was significantly larger in BW than in FW, and significantly larger in BW than in SLL for hindlimb swing phase. In BW, increased lumbosacral stabilisation and the larger area of support created by fore- and hindlimbs may represent a strategy to enhance body stabilisation, as BW entails some insecurity.

Asymmetric multi-task learning for interpretable gaze-driven grasping action forecasting.

This work tackles the problem of automatically predicting the grasping intention of humans observing their environment, with eye-tracker glasses and video cameras recording the scene view. Our target application is the assistance to people with motor disabilities and potential cognitive impairments, using assistive robotics. Our proposal leverages the analysis of human attention captured in the form of gaze fixations recorded by an eye-tracker on the first person video, as the anticipation of prehension actions is a well studied and well known phenomenon. We propose a multi-task system that simultaneously addresses the prediction of human attention in the near future, and the anticipation of grasping actions. In our model, visual attention is modeled as a competitive process between a discrete set of states, each one associated to a well-known gaze movement pattern from visual psychology. We additionally consider an asymmetric multitask problem, where attention modeling is an auxiliary task that helps to regularize the learning process of the main action prediction task, and propose a constrained multi-task loss that naturally deals with this asymmetry. Our model shows superior performance than other losses for dynamic multi-task learning, current dominant deep architectures for general action forecasting and particularly-tailored models for predicting grasping intention. In particular, it provides state-of-the-art performance in three datasets for egocentric action anticipation, with an average precision of 0.569 and 0.524 in GITW and Sharon datasets, respectively, and an accuracy of 89.2% and a success rate of 51.7% in Invisible dataset.

Insights into two-phase flow dynamics in closed-loop pulsating heat pipes utilizing Fe3O4/water: experimental visualization study

This article discusses a focused study on visualizing the flow patterns in a two-phase pulsating heat pipe (PHP) using Fe3O4/water as the working fluid at 3 V/V% concentration. The research also aims to meticulously examine phase change phenomena in the heating section, particularly focusing on bubble formation and expansion processes. A high-speed video camera was utilized to capture dynamic insights into the behavior of the Fe3O4/water mixture. Based on the findings, a straightforward model was developed to explain bubble generation and growth in the mixture, serving as a useful reference for future PHP designs and optimizations. Visual observations also noted the stable nature of the Fe3O4/water nanofluid over a 4-day period, confirming its consistency throughout the experiments. Moreover, the impact of heat load variation on the evaporator section was assessed using controlled heat inputs ranging from 10 to 80 W. Observations on the arrangement of slugs and plugs at a 50% filling ratio revealed interesting self-adjusting flow patterns in response to increasing heat inputs, providing valuable insights into PHP operational dynamics. Notably, the oscillatory flow behavior of Fe3O4/water, the chosen working fluid, exhibited greater activity in comparison to water. This distinctive flow behavior contributed to achieving heightened thermal performance efficiency for the Fe3O4/water system, attributed to its faster attainment of the annular flow condition.

Are airports like cities? Affordances and people’s micro embodied interactions during the arrival experience

Airports are designed to work efficiently. Specifically, they seek to efficiently process and ‘control’ people’s movements across landside and airside spaces, as well as to entice consumption through the architectural engineering of atmospheres and passenger experiences using architecture (Adey 2008; Fuller and Harley 2004; Hubregtse 2020). However, studies of the design and human sensorial perception of spaces of mobilities have shown that the way people move through and inhabit such spaces transcends the intended functionality of their design (Jensen and Lanng 2017). This includes work that considers airports as not only machines of movement, control and profit, but also city-like spaces (Hernandez-Bueno 2021; Nikolaeva 2013). Based on the conceptualisation of the airport as an ambiguous and city-like space, this paper uses thermal camera video recordings, ethnography, and design mappings to analyse the passenger movements, practices, embodied interactions, and mobile affordances of Copenhagen Airport’s meet and greet area. Focusing on a micro scale, the analysis shows the comparability of people’s practices and ways of inhabiting the airport with practices common to urban public spaces. This facilitates broader reflection on how to speculatively re-design and re-imagine the airport like an urban ‘public’ space.

Traffic Volume Study and Analysis at Major Road Network in an Urban Area

Traffic volume is a significant method to study the time and movement of vehicles on the road. The study was carried out at six different sampling sites in Greater Noida. The current study effectively analyzed the peak hours of several roadways using traffic flow per hour. To perform this study, the traffic flow per hour in the research region was calculated using both manual and video camera (photographic) approaches. Additionally, PCU (Passenger Car Unit) data was utilized to examine how urban roadways were used. According to PCU data, there are a lot of two-wheelers in Greater Noida, with 206 PCUs in total. This indicates that a lot of two-wheelers use the city's roadways. The results of the PCUs show that the 2-wheelers are the major contributor to urban air pollution. As 2-wheelers use petrol as their fuel, the composition of their exhaust emissions is like that of gasoline- operated vehicle emissions. Keywords: - Traffic Volume, Vehicular Emission, Roadways, Passenger Car Unit,

An Image Processing-Based Method to Analyze Driver Visual Behavior Using Eye-Tracker Data

This paper presents a practical method for analyzing drivers’ eye movements, providing a valuable tool for understanding their behavior during driving simulations. The method, which utilizes an image processing technique, addresses the challenges when the driver’s attention is on points without information about the image depth. The screen image changes or moves with the simulation. It allows us to identify the gaze position relative to the road, determining whether the glance is inside or outside. This is achieved by transforming RGB images (frames) collected by the eye-tracker video camera into a b/w image using the Canny filter. This filter can identify objects’ contours by evaluating the change in color of their surfaces. A window is then applied to these new images to extract information about the gaze position in the real world. Four drivers were used as a sample for the method’s testing. The findings demonstrate various driver variations and a disparity between driving in curved and rectilinear segments. The gaze is typically inside the road in curved sections, whereas in rectilinear sections, the gaze is frequently outside.

Reliability of the Test of Gross Motor Development Third Edition Among Children with Developmental Coordination Disorder

Aim The Test of Gross Motor Development Third Edition (TGMD-3) is used to assess the development of fundamental movement skills in children from 3 to 10 years old. This study aimed to evaluate the intra-rater, inter-rater, and test-retest reliability and to determine the minimal detectable change (MDC) value of the TGMD-3 in children with developmental coordination disorder (DCD). Methods The TGMD-3 was administered to 20 children with DCD. The child’s fundamental movement skills were recorded using a digital video camera. Reliability was assessed at two occasions by three raters using the generalizability theory. Results The TGMD-3 demonstrates good inter-rater reliability for the locomotor skills subscale, the ball skills subscale, and the total score (φ = 0.77 − 0.91), while the intra-rater reliability was even higher (φ = 0.94 − 0.97). Test-retest reliability was also shown to be good (φ = 0.79-0.93). The MDC95 was determined to be 10 points. Conclusion This study provides evidence that the TGMD-3 is a reliable test when used to evaluate fundamental movement skills in children with DCD and suggests that an increase of 10 points represents a significant change in the motor function of a child with DCD.

Measurement properties of activity monitoring for a rehabilitation (AMoR) platform in post-stroke individuals in a simulated home environment

ABSTRACT Aim The aim of this study was to evaluate the measurement properties of activity monitoring for a rehabilitation (AMoR) platform for step counting, time spent in sedentary behavior, and postural changes during activities of daily living (ADLs) in a simulated home environment. Methods Twenty-one individuals in the post-stroke chronic phase used the AMoR platform during an ADL protocol and were monitored by a video camera. Spearman’s correlation coefficient, mean absolute percent error (MAPE), intraclass correlation coefficient (ICC), and Bland–Altman plot analyses were used to estimate the validity and reliability between the AMoR platform and the video for step counting, time spent sitting/lying, and postural changes from sit-to-stand (SI-ST) and sit-to-stand (ST-SI). Results Validity of the platform was observed with very high correlation values for step counting (rs = 0.998) and time spent sitting/lying (rs = 0.992) and high correlation for postural change of SI-ST (rs = 0.850) and ST-SI (rs = 0.851) when compared to the video. An error percentage above 5% was observed only for the SI-ST postural change (7.13%). The ICC values show excellent agreement for step counting (ICC3, k = 0.999) and time spent sitting/lying (ICC3, k = 0.992), and good agreement for SI-ST (ICC3, k = 0.859) and ST-SI (ICC3, k = 0.936) postural change. Values of the differences for step counting, sitting/lying time, and postural change were within the limits of agreement according to the analysis of the Bland–Altman graph. Conclusion The AMoR platform presented validity and reliability for step counting, time spent sitting/lying, and identification of SI-ST and ST-SI postural changes during tests in a simulated environment in post-stroke individuals.

A novel concurrent learning-based fixed-time convergent visual depth observer for weakly persistently exciting perspective dynamical systems

The problem of synthesizing a fixed-time depth observer based on monocular image feedback is considered in this study. The key challenge lies in the fact that the perspective dynamical system used to model visual motion is typically characterized as being weakly persistently exciting, which complicates observer synthesis. Furthermore, the key to achieving the task objective (safe obstacle avoidance, for instance) lies in the synthesis of a depth observer that achieves rapid convergence of the (static) obstacle depth estimate to the ground truth in a known fixed-time. To address these challenges, and in contrast with prior schemes that rely on a motion-restrictive persistency of excitation (PE) condition for ensuring exponential convergence, a novel adaptive observer framework is considered in this study that incorporates a concurrent learning (CL) term for ensuring fixed-time observer convergence. In particular, the use of concurrent learning allows for the synthesis of a relaxed finite-time excitation condition that relies on historical data recorded over a dynamic sliding window in the recent past that the proposed observer relies on to ensure fixed-time convergence. Thus, a continuous-time reduced-order observer formulation is presented that relies on camera motion data to achieve fixed-time convergence to a uniform ultimate bound for a suitably large choice of the observer gains. Experimental results are used to demonstrate the efficacy of the proposed scheme in the presence of significant measurement noise. A performance comparison study is also undertaken to demonstrate superior performance of the proposed scheme compared to leading alternative designs. Finally, the practical applicability of the proposed scheme is verified by incorporating the proposed scheme within a reactive navigation scheme to accomplish obstacle avoidance. By incorporating a suitably informative CL term within the observer framework, the proposed scheme eliminates the need to rely on a difficult-to-verify PE condition, thus rendering it more suitable for practical applications like visual target-tracking and visual servo control.

Accelerometer Thresholds for Estimating Physical Activity Intensity Levels in Infants: A Preliminary Study

Lack of physical activity (PA) at a young age can result in health issues. Thus, monitoring PA is important. Wearable accelerometers are the preferred tool to monitor PA in children. Validated thresholds are used to classify activity intensity levels, e.g., sedentary, light, and moderate-to-vigorous, in ambulatory children. No previous work has developed accelerometer thresholds for infancy (pre-ambulatory children). Therefore, this work aims to develop accelerometer thresholds for PA intensity levels in pre-ambulatory infants. Infants (n = 10) were placed in a supine position and allowed free movement. Their movements were synchronously captured using video cameras and accelerometers worn on each ankle. The video data were labeled by activity intensity level (sedentary, light, and moderate-to-vigorous) in two-second epochs using observational rating (gold standard). Accelerometer thresholds were developed for acceleration and jerk using two optimization approaches. Four sets of thresholds were developed for dual (two ankles) and for single-worn (one ankle) accelerometers. Of these, for a typical use case, we recommend using acceleration-based thresholds of 1.00 m/s to distinguish sedentary and light activity and 2.60 m/s to distinguish light and moderate-to-vigorous activity. Acceleration and jerk are both suitable for measuring PA.