Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Structure-function relationships at the human spinal disc-vertebra interface.

Damage at the intervertebral disc-vertebra interface associates with back pain and disc herniation. However, the structural and biomechanical properties of the disc-vertebra interface remain underexplored. We sought to measure mechanical properties and failure mechanisms, quantify architectural features, and assess structure-function relationships at this vulnerable location. Vertebra-disc-vertebra specimens from human cadaver thoracic spines were scanned with micro-computed tomography (μCT), surface speckle-coated, and loaded to failure in uniaxial tension. Digital image correlation (DIC) was used to calculate local surface strains. Failure surfaces were scanned using scanning electron microscopy (SEM), and adjacent sagittal slices were analyzed with histology and SEM. Seventy-one percent of specimens failed initially at the cartilage endplate-bone interface of the inner annulus region. Histology and SEM both indicated a lack of structural integration between the cartilage endplate (CEP) and bone. The interface failure strength was increased in samples with higher trabecular bone volume fraction in the vertebral endplates. Furthermore, failure strength decreased with degeneration, and in discs with thicker CEPs. Our findings indicate that poor structural connectivity between the CEP and vertebra may explain the structural weakness at this region, and provide insight into structural features that may contribute to risk for disc-vertebra interface injury. The disc-vertebra interface is the site of failure in the majority of herniation injuries. Here we show new structure-function relationships at this interface that may motivate the development of diagnostics, prevention strategies, and treatments to improve the prognosis for many low back pain patients with disc-vertebra interface injuries. © 2017 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 36:192-201, 2018

Combining quantitative narrative analysis and predictive modeling - an eye tracking study

As a part of a larger interdisciplinary project on Shakespeare sonnets’ reception (Jacobs et al., 2017; Xue et al., 2017), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning- based predictive modeling approach five ‘surface’ features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. The fact that one phonological feature, i.e., sonority score, also played a role is in line with current theorizing on poetry reading. Our approach opens new ways for future eye movement research on reading poetic texts and other complex literary materials (cf. Jacobs, 2015c)

Look at Me: Early Gaze Engagement Enhances Corticospinal Excitability During Action Observation

Direct gaze is a powerful social cue able to capture the onlooker's attention. Beside gaze, head and limb movements as well can provide relevant sources of information for social interaction. This study investigated the joint role of direct gaze and hand gestures on onlookers corticospinal excitability (CE). In two experiments we manipulated the temporal and spatial aspects of observed gaze and hand behavior to assess their role in affecting motor preparation. To do this, transcranial magnetic stimulation (TMS) on the primary motor cortex (M1) coupled with electromyography (EMG) recording was used in two experiments. In the crucial manipulation, we showed to participants four video clips of an actor who initially displayed eye contact while starting a social request gesture, and then completed the action while directing his gaze toward a salient object for the interaction. This way, the observed gaze potentially expressed the intention to interact. Eye tracking data confirmed that gaze manipulation was effective in drawing observers' attention to the actor's hand gesture. In the attempt to reveal possible time-locked modulations, we tracked CE at the onset and offset of the request gesture. Neurophysiological results showed an early CE modulation when the actor was about to start the request gesture looking straight to the participants, compared to when his gaze was averted from the gesture. This effect was time-locked to the kinematics of the actor's arm movement. Overall, data from the two experiments seem to indicate that the joint contribution of direct gaze and precocious kinematic information, gained while a request gesture is on the verge of beginning, increases the subjective experience of involvement and allows observers to prepare for an appropriate social interaction. On the contrary, the separation of gaze cues and body kinematics can have adverse effects on social motor preparation. CE is highly susceptible to biological cues, such as averted gaze, which is able to automatically capture and divert observer's attention. This point to the existence of heuristics based on early action and gaze cues that would allow observers to interact appropriately

The Cat Is On the Mat. Or Is It a Dog? Dynamic Competition in Perceptual Decision Making

Recent neurobiological findings suggest that the brain solves simple perceptual decision-making tasks by means of a dynamic competition in which evidence is accumulated in favor of the alternatives. However, it is unclear if and how the same process applies in more complex, real-world tasks, such as the categorization of ambiguous visual scenes and what elements are considered as evidence in this case. Furthermore, dynamic decision models typically consider evidence accumulation as a passive process disregarding the role of active perception strategies. In this paper, we adopt the principles of dynamic competition and active vision for the realization of a biologically- motivated computational model, which we test in a visual catego- rization task. Moreover, our system uses predictive power of the features as the main dimension for both evidence accumulation and the guidance of active vision. Comparison of human and synthetic data in a common experimental setup suggests that the proposed model captures essential aspects of how the brain solves perceptual ambiguities in time. Our results point to the importance of the proposed principles of dynamic competi- tion, parallel specification, and selection of multiple alternatives through prediction, as well as active guidance of perceptual strategies for perceptual decision-making and the resolution of perceptual ambiguities. These principles could apply to both the simple perceptual decision problems studied in neuroscience and the more complex ones addressed by vision research.Peer reviewe

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery

The electrocardiogram or ECG has been in use for over 100 years and remains the most widely performed diagnostic test to characterize cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, innovations in machine learning algorithms, and availability of large-scale digitized ECG data would enable extending the utility of the ECG beyond its current limitations, while at the same time preserving interpretability, which is fundamental to medical decision-making. We identified 36,186 ECGs from the UCSF database that were 1) in normal sinus rhythm and 2) would enable training of specific models for estimation of cardiac structure or function or detection of disease. We derived a novel model for ECG segmentation using convolutional neural networks (CNN) and Hidden Markov Models (HMM) and evaluated its output by comparing electrical interval estimates to 141,864 measurements from the clinical workflow. We built a 725-element patient-level ECG profile using downsampled segmentation data and trained machine learning models to estimate left ventricular mass, left atrial volume, mitral annulus e' and to detect and track four diseases: pulmonary arterial hypertension (PAH), hypertrophic cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP). CNN-HMM derived ECG segmentation agreed with clinical estimates, with median absolute deviations (MAD) as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Patient-level ECG profiles enabled quantitative estimates of left ventricular and mitral annulus e' velocity with good discrimination in binary classification models of left ventricular hypertrophy and diastolic function. Models for disease detection ranged from AUROC of 0.94 to 0.77 for MVP. Top-ranked variables for all models included known ECG characteristics along with novel predictors of these traits/diseases.Comment: 13 pages, 6 figures, 1 Table + Supplemen

Tracking of toddler fruit and vegetable preferences to intake and adiposity later in childhood

This study examined whether toddlers' liking for fruit and vegetables (FV) predicts intake of FV later in childhood, how both relate to childhood adiposity and how these were moderated by factors in infancy. Children in the Gateshead Millennium Study were recruited at birth in 1999–2000. Feeding data collected in the first year were linked to data from a parental questionnaire completed for 456 children at age 2.5 years (30 m) and to anthropometry, skinfolds and bioelectrical impedance and 4‐day food diary data collected for 293 of these children at age 7 years. Aged 30 months, 50% of children were reported to like eight different vegetables and three fruits, but at 7 years, children ate a median of only 1.3 (range 0–7) portions of vegetables and 1.0 portion of fruit (0–4). Early appetite, feeding problems and food neophobia showed significant univariate associations with liking for FV aged 30 m, but the number of vegetables toddlers liked was the only independent predictor of vegetable consumption at age 7 years (odds ratio (OR) 1.28 p < 0.001). Liking for fruit aged 30 m also independently predicted fruit intake (OR = 1.31, p = 0.016), but these were also related to deprivation (OR = 2.69, p = 0.001) maternal education (OR = 1.28, p = 0.039) and female gender (OR = 1.8, p = 0.024). Children eating more FV at age 7 years had slightly lower body mass index and skinfolds. An early liking for FV predicted increased later intake, so increasing early exposure to FV could have long term beneficial consequences