Development and validation of the baby eating behaviour coding system (BEBECS) to assess eating behaviour during complementary feeding

Eating behaviour in children is a matter of study for which diverse tools have been designed. Coding systems for videotaped meals allow the extraction of detailed in vivo information; however, there is no tool available for infants following a Baby-Led Weaning (BLW) method. This study aimed to create and validate a new tool to assess eating behaviour in infants during weaning, applicable regardless of the complementary feeding method. The Baby Eating Behaviour Coding System (BEBECS) was developed comprising time variables, behaviours, feeder-led actions, and other meal-related variables. Sixty videos of infants aged 6-18 months following spoon-feeding (SF) or BLW methods were coded by two trained coders. These scores were analysed together with intake and maternal ratings of liking and calmness. Additionally, combined analysis and internal comparison assessed the possible differences in BEBECS variables between SF and BLW. Inter-rater and test-retest reliability had good to excellent agreement: Cohen's Kappa >0.75, Lin's CCC >0.70, and Intraclass Correlation Coefficient >0.75, for almost all variables. Infants' liking and intake of the offered food correlated positively with meal duration and total count of mouth approaches but negatively with having leftovers and time between mouth approaches. Infants' calmness and tiredness were negatively correlated. More food than initially offered was available during the meal in BLW but not in SF. There was a tendency towards more autonomous behaviour in BLW infants regarding changes observed in the time the food was in the mouth at each stage (6, 12, and 18 months). In conclusion, BEBECS has the potential to be a valid tool for application in the research of infant eating behaviour during weaning by trained coders.

GesturalOrigins: A bottom-up framework for establishing systematic gesture data across ape species

Current methodologies present significant hurdles to understanding patterns in the gestural communication of individuals, populations, and species. To address this issue, we present a bottom-up data collection framework for the study of gesture: GesturalOrigins. By "bottom-up", we mean that we minimise a priori structural choices, allowing researchers to define larger concepts (such as 'gesture types', 'response latencies', or 'gesture sequences') flexibly once coding is complete. Data can easily be re-organised to provide replication of, and comparison with, a wide range of datasets in published and planned analyses. We present packages, templates, and instructions for the complete data collection and coding process. We illustrate the flexibility that our methodological tool offers with worked examples of (great ape) gestural communication, demonstrating differences in the duration of action phases across distinct gesture action types and showing how species variation in the latency to respond to gestural requests may be revealed or masked by methodological choices. While GesturalOrigins is built from an ape-centred perspective, the basic framework can be adapted across a range of species and potentially to other communication systems. By making our gesture coding methods transparent and open access, we hope to enable a more direct comparison of findings across research groups, improve collaborations, and advance the field to tackle some of the long-standing questions in comparative gesture research.

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Motion Estimation is one of the main tasks behind any video encoder. It is a computationally costly task; therefore, it is usually delegated to specific or reconfigurable hardware, such as FPGAs. Over the years, multiple FPGA implementations have been developed, mainly using hardware description languages such as Verilog or VHDL. Since programming using hardware description languages is a complex task, it is desirable to use higher-level languages to develop FPGA applications.The aim of this work is to evaluate OpenCL, in terms of expressiveness, as a tool for developing this kind of FPGA applications. To do so, we present and evaluate a parallel implementation of the Block Matching Motion Estimation process using OpenCL for Intel FPGAs, usable and tested on an Intel Stratix 10 FPGA. The implementation efficiently processes Full HD frames completely inside the FPGA. In this work, we show the resource utilization when synthesizing the code on an Intel Stratix 10 FPGA, as well as a performance comparison with multiple CPU implementations with varying levels of optimization and vectorization capabilities. We also compare the proposed OpenCL implementation, in terms of resource utilization and performance, with estimations obtained from an equivalent VHDL implementation.

Low-complexity three-dimensional discrete Hartley transform approximations for medical image compression

The discrete Hartley transform (DHT) is a useful tool for medical image coding. The three-dimensional DHT (3D DHT) can be employed to compress medical image data, such as magnetic resonance and X-ray angiography. However, the computation of the 3D DHT involves several multiplications by irrational quantities, which require floating-point arithmetic and inherent truncation errors. In recent years, a significant progress in wireless and implantable biomedical devices has been achieved. Such devices present critical power and hardware limitations. The multiplication operation demands higher hardware, power, and time consumption than other arithmetic operations, such as addition and bit-shifts. In this work, we present a set of multiplierless DHT approximations, which can be implemented with fixed-point arithmetic. We derive 3D DHT approximations by employing tensor formalism. Such proposed methods present prominent computational savings compared to the usual 3D DHT approach, being appropriate for devices with limited resources. The proposed transforms are applied in a lossy 3D DHT-based medical image compression algorithm, presenting practically the same level of visual quality (>98% in terms of SSIM) at a considerable reduction in computational effort (100% multiplicative complexity reduction). Furthermore, we implemented the proposed 3D transforms in an ARM Cortex-M0+ processor employing the low-cost Raspberry Pi Pico board. The execution time was reduced by ∼70% compared to the usual 3D DHT and ∼90% compared to 3D DCT.

Deep learning-based video quality enhancement for the new versatile video coding

Multimedia IoT (M-IoT) is an emerging type of Internet of things (IoT) relaying multimedia data (images, videos, audio and speech, etc.). The rapid growth of M-IoT devices enables the creation of a massive volume of multimedia data with different characteristics and requirements. With the development of artificial intelligence (AI), AI-based multimedia IoT systems have been recently designed and deployed for various video-based services for contemporary daily life, like video surveillance with high definition (HD) and ultra-high definition (UHD) and mobile multimedia streaming. These new services need higher video quality in order to meet the quality of experience (QoE) required by the users. Versatile video coding (VVC) is the new video coding standard that achieves significant coding efficiency over its predecessor high-efficiency video coding (HEVC). Moreover, VVC can achieve up to 30% BD rate savings compared to HEVC. Inspired by the rapid advancements in deep learning, we propose in this paper a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Therefore, we replace the conventional in-loop filtering in VVC by the proposed WSE-DCNN model that eliminates the compression artifacts in order to improve visual quality and hence increase the end user QoE. The obtained results prove that the proposed in-loop filtering technique achieves - 2.85 %, - 8.89 %, and - 10.05 % BD rate reduction for luma and both chroma components under random access configuration. Compared to the traditional CNN-based filtering approaches, the proposed WSE-DCNN-based in-loop filtering framework achieves efficient performance in terms of RD cost.

Low-energy motion estimation memory system with dynamic management

The digital video coding process imposes severe pressure on memory traffic, leading to considerable power consumption related to frequent DRAM accesses. External off-chip memory demand needs to be minimized by clever architecture/algorithm co-design, thus saving energy and extending battery lifetime during video encoding. To exploit temporal redundancies among neighboring frames, the motion estimation (ME) algorithm searches for good matching between the current block and blocks within reference frames stored in external memory. To save energy during ME, this work performs memory accesses distribution analysis of the test zone search (TZS) ME algorithm and, based on this analysis, proposes both a multi-sector scratchpad memory design and dynamic management for the TZS memory access. Our dynamic memory management, called neighbor management, reduces both static consumption-by employing sector-level power gating-and dynamic consumption-by reducing the number of accesses for ME execution. Additionally, our dynamic management was integrated with two previously proposed solutions: a hardware reference frame compressor and the Level C data reuse scheme (using a scratchpad memory). This system achieves a memory energy consumption savings of 99.8 % and, when compared to the baseline solution composed of a reference frame compressor and data reuse scheme, the memory energy consumption was reduced by 44.1 % at a cost of just 0.35 % loss in coding efficiency, on average. When compared with related works, our system presents better memory bandwidth/energy savings and coding efficiency results.

A dataset of labelled objects on raw video sequences

We present an object labelled dataset called SFU-HW-Objects-v1, which contains object labels for a set of raw video sequences. The dataset can be useful for the cases where both object detection accuracy and video coding efficiency need to be evaluated on the same dataset. Object ground-truths for 18 of the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences have been labelled. The object categories used for the labeling are based on the Common Objects in Context (COCO) labels. A total of 21 object classes are found in test sequences, out of the 80 original COCO label classes. Brief descriptions of the labeling process and the structure of the dataset are presented.

Sleep disorders in children with Angelman and Smith-Magenis syndromes: The assessment of potential causes of disrupted settling and night time waking

BACKGROUND

Sleep problems are common in Smith-Magenis (SMS) and Angelman syndromes (AS). Effectiveness of interventions depends on appropriate assessment, complicated by compromised self-report and health and behaviour difficulties. Studying settling and waking in these syndromes could inform assessment.

AIMS

To describe settling and waking behaviours in children at high-risk of sleep and health problems, using direct observation.

METHODS AND PROCEDURES

Video and actigraphy data were collected for 12 participants with AS (Mean age = 8.02, SD = 2.81) and 11 with SMS (Mean age = 8.80, SD = 2.18). Settling (30 min prior to sleep onset) and night waking were coded for nineteen behaviours relating to pain, challenging behaviour and caregiver interaction. Lag sequential analyses were conducted for pain-related behaviours.

OUTCOMES AND RESULTS

Percentage of time spent in behaviours was calculated. Parent-child interactions (0.00-9.93 %) and challenging behaviours (0 %) were rare at settling and waking in both groups. In the AS group, pain-related behaviours were more likely to occur before waking than by chance (p < 0.001).

CONCLUSIONS AND IMPLICATIONS

Findings highlight the importance of considering pain as a cause of sleep problems in AS. The principle and methodology could be extended to individuals with ID experiencing sleep problems.

Quantification of discrete behavioral components of the MDS-UPDRS

INTRODUCTION

The Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the current gold standard means of assessing disease state in Parkinson's disease (PD). Objective measures in the form of wearable sensors have the potential to improve our ability to monitor symptomology in PD, but numerous methodological challenges remain, including integration into the MDS-UPDRS. We applied a structured video coding scheme to temporally quantify clinical, scripted, motor tasks in the MDS-UPDRS for the alignment and integration of objective measures collected in parallel.

METHODS

25 PD subjects completed two video-recorded MDS-UPDRS administrations. Visual cues of task performance reliably identifiable in video recordings were used to construct a structured video coding scheme. Postural transitions were also defined and coded. Videos were independently coded by two trained non-expert coders and a third expert coder to derive indices of inter-rater agreement.

RESULTS

50 videos of MDS-UPDRS performance were fully coded. Non-expert coders achieved a high level of agreement (Cohen's κ > 0.8) on all postural transitions and scripted motor tasks except for Postural Stability (κ = 0.617); this level of agreement was largely maintained even when more stringent thresholds for agreement were applied. Durations coded by non-expert coders and expert coders were significantly different (p < 0.05) for only Postural Stability and Rigidity, Left Upper Limb.

CONCLUSIONS

Non-expert coders consistently and accurately quantified discrete behavioral components of the MDS-UPDRS using a structured video coding scheme; this represents a novel, promising approach for integrating objective and clinical measures into unified, longitudinal datasets.

Zooming in on children's behavior during delay of gratification: Disentangling impulsigenic and volitional processes underlying self-regulation

When delaying gratification, both motivational and regulatory processes are likely to be at play; however, the relative contributions of motivational and regulatory influences on delay behavior are unclear. By examining behavioral responses during a delay task, this study sought to examine the motivational (anticipatory behavior) and regulatory mechanisms (executive function and self-control strategies) underlying children's self-regulation. The participants, 65 5- to 9-year-old children (M_age=7.19years, SD=0.89), were video-recorded during a delay procedure and later coded for anticipatory behaviors (e.g., gazing intensely at the tablet) and self-control strategies. Children also completed two executive function (EF) tasks. We found that anticipatory behavior was curvilinearly related to delay time. Children showing either very low or very high levels of anticipatory behavior were not able to wait the entire time. Furthermore, our results indicated that anticipatory behavior interacted with EF to predict delay time. Specifically, anticipatory behavior was negatively related to delay time only if EF abilities were low. Finally, self-control strategies also interacted with EF to predict children's ability to delay. Spontaneous engagement in self-control strategies such as fidgeting and engagement in alternative activities were beneficial for children with low EF but were unrelated to delay time for children with high EF. Results indicate the value of examining motivational and regulatory influences on delay behavior. Lapses in self-regulation may be due to the combination of powerful impulsigenic (i.e., anticipatory behavior) and weak volitional processes (i.e., EF, self-control strategies).

Multiview video plus depth transmission via virtual-view-assisted complementary down/upsampling

Multiview video plus depth is a popular 3D video format which can provide viewers a vivid 3D feeling. However, its requirements in terms of computational complexity and transmission bandwidth are more than that of conventional 2D video. To mitigate these limitations, some works have proposed to reduce the amount of transmitted data by adopting different resolutions for different views, and consequently, the transmitted video is called mixed resolution video. In order to further reduce the transmitted data and maintain good quality at the decoder side; in this paper, we propose a down/upsampling algorithm for 3D multiview video which systematically takes into account the video encoder and decoder. At the encoder side, the rows of the two adjacent views are downsampled following an interlacing and complementary fashion, whereas, at the decoder side, the discarded pixels are recovered by fusing the virtual view pixels with the directional interpolated pixels from the complementary downsampled views. Moreover, the patterns of the texture surrounding the discarded pixels are used to aid the data fusion, so as to enhance edges recovery. Meanwhile, with the assistance of virtual views, at the decoder side, the proposed approach can effectively recover the discarded high-frequency details. The experimental results demonstrate the superior performance of the proposed framework.

A parallel algorithm for motion estimation in video coding using the bilinear transformation

Accurate motion estimation between frames is important for drastically reducing data redundancy in video coding. However, advanced motion estimation methods are computationally intensive and their execution in real time usually requires a parallel implementation. In this paper, we investigate the parallel implementation of such a motion estimation technique. Specifically, we present a parallel algorithm for motion estimation based on the bilinear transformation on the well-known parallel model of the hypercube network and formally prove the time and the space complexity of the proposed algorithm. We also show that the parallel algorithm can also run on other hypercubic networks, such as butterfly, cube-connected-cycles, shuffle-exchange or de Bruijn network with only constant slowdown.