Mining Bodily Cues to Deception.

A significant body of research has investigated potential correlates of deception and bodily behavior. The vast majority of these studies consider discrete, subjectively coded bodily movements such as specific hand or head gestures. Such studies fail to consider quantitative aspects of body movement such as the precise movement direction, magnitude and timing. In this paper, we employ an innovative data mining approach to systematically study bodily correlates of deception. We re-analyze motion capture data from a previously published deception study, and experiment with different data coding options. We report how deception detection rates are affected by variables such as body part, the coding of the pose and movement, the length of the observation, and the amount of measurement noise. Our results demonstrate the feasibility of a data mining approach, with detection rates above 65%, significantly outperforming human judgement (52.80%). Owing to the systematic analysis, our analyses allow for an understanding of the importance of various coding factor. Moreover, we can reconcile seemingly discrepant findings in previous research. Our approach highlights the merits of data-driven research to support the validation and development of deception theory.

Extensive Angular Sampling Enables the Sensitive Localization of Macromolecules in Electron Tomograms.

Cryo-electron tomography provides 3D images of macromolecules in their cellular context. To detect macromolecules in tomograms, template matching (TM) is often used, which uses 3D models that are often reliable for substantial parts of the macromolecules. However, the extent of rotational searches in particle detection has not been investigated due to computational limitations. Here, we provide a GPU implementation of TM as part of the PyTOM software package, which drastically speeds up the orientational search and allows for sampling beyond the Crowther criterion within a feasible timeframe. We quantify the improvements in sensitivity and false-discovery rate for the examples of ribosome identification and detection. Sampling at the Crowther criterion, which was effectively impossible with CPU implementations due to the extensive computation times, allows for automated extraction with high sensitivity. Consequently, we also show that an extensive angular sample renders 3D TM sensitive to the local alignment of tilt series and damage induced by focused ion beam milling. With this new release of PyTOM, we focused on integration with other software packages that support more refined subtomogram-averaging workflows. The automated classification of ribosomes by TM with appropriate angular sampling on locally corrected tomograms has a sufficiently low false-discovery rate, allowing for it to be directly used for high-resolution averaging and adequate sensitivity to reveal polysome organization.

Multi-Dataset, Multitask Learning of Egocentric Vision Tasks.

For egocentric vision tasks such as action recognition, there is a relative scarcity of labeled data. This increases the risk of overfitting during training. In this paper, we address this issue by introducing a multitask learning scheme that employs related tasks as well as related datasets in the training process. Related tasks are indicative of the performed action, such as the presence of objects and the position of the hands. By including related tasks as additional outputs to be optimized, action recognition performance typically increases because the network focuses on relevant aspects in the video. Still, the training data is limited to a single dataset because the set of action labels usually differs across datasets. To mitigate this issue, we extend the multitask paradigm to include datasets with different label sets. During training, we effectively mix batches with samples from multiple datasets. Our experiments on egocentric action recognition in the EPIC-Kitchens, EGTEA Gaze+, ADL and Charades-EGO datasets demonstrate the improvements of our approach over single-dataset baselines. On EGTEA we surpass the current state-of-the-art by 2.47 percent. We further illustrate the cross-dataset task correlations that emerge automatically with our novel training scheme.

Game mechanics in eHealth interventions promoting self-management in young people with chronic diseases: a protocol for a systematic review and meta-analyses from the eHealth Junior Consortium.

INTRODUCTION: Young people (aged 10-25 years) with chronic diseases are vulnerable to have reduced social participation and quality of life. It is important to empower young people to engage in their chronic diseases self-management. In comparison with traditional face-to-face care, interventions delivered through the internet and related technologies (eHealth) are less stigmatising and more accessible. Gamified eHealth self-management interventions may be particularly promising for young people. This systematic review aims at identifying (1) the game mechanics that have been implemented in eHealth interventions to support young people's self-management of their chronic (somatic or psychiatric) diseases, (2) the investigators' rationale for implementing such game mechanics and, if possible, (3) the effects of these interventions. METHODS AND ANALYSIS: The Preferred Reporting Items for Systematic reviews and Meta-Analysis statement guidelines will be followed. A systematic search of the literature will be conducted in Embase, Psycinfo and Web of Science from inception until 30 August 2022. Studies will be eligible if focused on (1) young people (aged 10-25 years) with chronic diseases and (2) describing gamified eHealth self-management interventions. When possible, the effects of the gamified interventions will be compared with non-gamified interventions or care-as-usual. Primary quantitative, qualitative or mixed-method studies written in English will be included. Two independent reviewers will (1) select studies, (2) extract and summarise the implemented game mechanics as well as the characteristics of the intervention and study, (3) evaluate their methodological quality and (4) synthesise the evidence. The reviewers will reach a consensus through discussion, and if required, a third researcher will be consulted. ETHICS AND DISSEMINATION: As systematic reviews use publicly available data, no formal ethical review and approval are needed. Findings will be published in peer-reviewed journals, presented at conferences and communicated to relevant stakeholders including patient organisations via the eHealth Junior Consortium. PROSPERO REGISTRATION NUMBER: CRD42021293037.

RobotP: A Benchmark Dataset for 6D Object Pose Estimation.

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

Healthy play, better coping: The importance of play for the development of children in health and disease.

Play is of vital importance for the healthy development of children. From a developmental perspective, play offers ample physical, emotional, cognitive, and social benefits. It allows children and adolescents to develop motor skills, experiment with their (social) behavioural repertoire, simulate alternative scenarios, and address the various positive and negative consequences of their behaviour in a safe and engaging context. Children with a chronic or life-threatening disease may face obstacles that negatively impact play and play development, possibly impeding developmental milestones, beyond the actual illness itself. Currently, there is limited understanding of the impact of (1) aberrant or suppressed play and (2) play-related interventions on the development of chronic diseased children. We argue that stimulating play behaviour enhances the adaptability of a child to a (chronic) stressful condition and promotes cognitive, social, emotional and psychomotor functioning, thereby strengthening the basis for their future health. Systematic play research will help to develop interventions for young patients, to better cope with the negative consequences of their illness and stimulate healthy development.

Learning to recognize rat social behavior: Novel dataset and cross-dataset application.

BACKGROUND: Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. NEW METHOD: To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. RESULTS: We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. COMPARISON WITH EXISTING METHODS: Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. CONCLUSIONS: With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings.

Usability Test of Exercise Games Designed for Rehabilitation of Elderly Patients After Hip Replacement Surgery: Pilot Study.

BACKGROUND: Patients who receive rehabilitation after hip replacement surgery are shown to have increased muscle strength and better functional performance. However, traditional physiotherapy is often tedious and leads to poor adherence. Exercise games, provide ways for increasing the engagement of elderly patients and increase the uptake of rehabilitation exercises. OBJECTIVE: The objective of this study was to evaluate Fietsgame (Dutch for cycling game), which translates existing rehabilitation exercises into fun exercise games. The system connects exercise games with a patient's personal record and a therapist interface by an Internet of Things server. Thus, both the patient and physiotherapist can monitor the patient's medical status. METHODS: This paper describes a pilot study that evaluates the usability of the Fietsgame. The study was conducted in a rehabilitation center with 9 participants, including 2 physiotherapists and 7 patients. The patients were asked to play 6 exercise games, each lasting about 5 min, under the guidance of a physiotherapist. The mean age of the patients was 74.57 years (standard deviation [SD] 8.28); all the patients were in the recovery process after hip surgery. Surveys were developed to quantitatively measure the usability factors, including presence, enjoyment, pain, exertion, and technology acceptance. Comments on advantages and suggested improvements of our game system provided by the physiotherapists and patients were summarized and their implications were discussed. RESULTS: The results showed that after successfully playing the games, 75% to 100% of the patients experienced high levels of enjoyment in all the games except the squats game. Patients reported the highest level of exertion in squats when compared with other exercise games. Lunges resulted in the highest dropout rate (43%) due to interference with the Kinect v2 from support chairs. All the patients (100%) found the game system useful and easy to use, felt that it would be a useful tool in their further rehabilitation, and expressed that they would like to use the game in the future. The therapists indicated that the exercise games highly meet the criteria of motor rehabilitation, and they intend to continue using the game as part of their rehabilitation treatment of patients. Comments from the patients and physiotherapists suggest that real-time corrective feedback when patients perform the exercises wrongly and a more personalized user interface with options for increasing or decreasing cognitive load are needed. CONCLUSIONS: The results suggest that Fietsgame can be used as an alternative tool to traditional motor rehabilitation for patients with hip surgery. Lunges and squats are found to be more beneficial for patients who have relatively better balance skills. A follow-up randomized controlled study will be conducted to test the effectiveness of the Fietsgame to investigate how motivating it is over a longer period of time.

Training Behavioral Control in Adolescents Using a Serious Game.

OBJECTIVE: Risk taking, such as heavy alcohol use, is commonplace among adolescents. Nevertheless, prolonged alcohol use at this age can lead to severe health problems. The goal of this study was to develop and evaluate a serious game training ("The Fling"), aimed at increasing behavioral control in adolescents and thereby helping them to improve control over their alcohol use. The game training was compared to a game placebo and a nongame training version in a randomized controlled trial. MATERIALS AND METHODS: A sample of 185 adolescents (mean age 14.9 years) in secondary education participated in the study. They performed four sessions of training, as well as a set of questionnaires and cognitive assessment tasks before and after the training. The basis for the training was the stop-signal paradigm, aimed at increasing behavioral control. RESULTS: The game variants were shown to motivate adolescents beyond the level of the nongame version. Behavioral control improved significantly over time, but this effect was also present in the game placebo, suggesting that the game activities alone may have had a beneficial effect on our measures of behavioral control. As baseline drinking levels were low, no significant training effects on drinking behavior were found. CONCLUSIONS: Although the current results are not yet conclusive as to whether "The Fling" is effective as a cognitive training, they do warrant further research in this direction. This study also shows that serious games may be uniquely suitable to bridge the gap between an evidence-based training paradigm and an attractive, motivating training environment.