Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this paper we extend our previous work by extracting the normalised pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for use in new deep learning architectures. We explore the viability of using these pose-based feature sets for automated classification within a deep learning framework by carrying out extensive experiments on five new deep learning architectures. Experimental results show that the proposed fully connected neural network FCNet performed robustly across different feature sets. Furthermore, the proposed convolutional neural network architectures demonstrated excellent performance in handling features in higher dimensionality. We make the code, extracted features and associated GMA labels publicly available

Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping

The young infant explores its body, its sensorimotor system, and the immediately accessible parts of its environment, over the course of a few months creating a model of peripersonal space useful for reaching and grasping objects around it. Drawing on constraints from the empirical literature on infant behavior, we present a preliminary computational model of this learning process, implemented and evaluated on a physical robot. The learning agent explores the relationship between the configuration space of the arm, sensing joint angles through proprioception, and its visual perceptions of the hand and grippers. The resulting knowledge is represented as the peripersonal space (PPS) graph, where nodes represent states of the arm, edges represent safe movements, and paths represent safe trajectories from one pose to another. In our model, the learning process is driven by intrinsic motivation. When repeatedly performing an action, the agent learns the typical result, but also detects unusual outcomes, and is motivated to learn how to make those unusual results reliable. Arm motions typically leave the static background unchanged, but occasionally bump an object, changing its static position. The reach action is learned as a reliable way to bump and move an object in the environment. Similarly, once a reliable reach action is learned, it typically makes a quasi-static change in the environment, moving an object from one static position to another. The unusual outcome is that the object is accidentally grasped (thanks to the innate Palmar reflex), and thereafter moves dynamically with the hand. Learning to make grasps reliable is more complex than for reaches, but we demonstrate significant progress. Our current results are steps toward autonomous sensorimotor learning of motion, reaching, and grasping in peripersonal space, based on unguided exploration and intrinsic motivation.Comment: 35 pages, 13 figure

Continuous sensing and quantification of body motion in infants:A systematic review

Abnormal body motion in infants may be associated with neurodevelopmental delay or critical illness. In contrast to continuous patient monitoring of the basic vitals, the body motion of infants is only determined by discrete periodic clinical observations of caregivers, leaving the infants unattended for observation for a longer time. One step to fill this gap is to introduce and compare different sensing technologies that are suitable for continuous infant body motion quantification. Therefore, we conducted this systematic review for infant body motion quantification based on the PRISMA method (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this systematic review, we introduce and compare several sensing technologies with motion quantification in different clinical applications. We discuss the pros and cons of each sensing technology for motion quantification. Additionally, we highlight the clinical value and prospects of infant motion monitoring. Finally, we provide suggestions with specific needs in clinical practice, which can be referred by clinical users for their implementation. Our findings suggest that motion quantification can improve the performance of vital sign monitoring, and can provide clinical value to the diagnosis of complications in infants.</p

Markerless human pose estimation for biomedical applications: a survey

Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-based Motion Capture systems due to the ease of setup, portability, and affordable cost of the technology. However, the exploitation of HPE in biomedical applications is still under investigation. This review aims to provide an overview of current biomedical applications of HPE. In this paper, we examine the main features of HPE approaches and discuss whether or not those features are of interest to biomedical applications. We also identify those areas where HPE is already in use and present peculiarities and trends followed by researchers and practitioners. We include here 25 approaches to HPE and more than 40 studies of HPE applied to motor development assessment, neuromuscolar rehabilitation, and gait & posture analysis. We conclude that markerless HPE offers great potential for extending diagnosis and rehabilitation outside hospitals and clinics, toward the paradigm of remote medical care

Markerless Human Motion Analysis

Measuring and understanding human motion is crucial in several domains, ranging from neuroscience, to rehabilitation and sports biomechanics. Quantitative information about human motion is fundamental to study how our Central Nervous System controls and organizes movements to functionally evaluate motor performance and deficits. In the last decades, the research in this field has made considerable progress. State-of-the-art technologies that provide useful and accurate quantitative measures rely on marker-based systems. Unfortunately, markers are intrusive and their number and location must be determined a priori. Also, marker-based systems require expensive laboratory settings with several infrared cameras. This could modify the naturalness of a subject\u2019s movements and induce discomfort. Last, but not less important, they are computationally expensive in time and space. Recent advances on markerless pose estimation based on computer vision and deep neural networks are opening the possibility of adopting efficient video-based methods for extracting movement information from RGB video data. In this contest, this thesis presents original contributions to the following objectives: (i) the implementation of a video-based markerless pipeline to quantitatively characterize human motion; (ii) the assessment of its accuracy if compared with a gold standard marker-based system; (iii) the application of the pipeline to different domains in order to verify its versatility, with a special focus on the characterization of the motion of preterm infants and on gait analysis. With the proposed approach we highlight that, starting only from RGB videos and leveraging computer vision and machine learning techniques, it is possible to extract reliable information characterizing human motion comparable to that obtained with gold standard marker-based systems

Human Movement Disorders Analysis with Graph Neural Networks

Human movement disorders encompass a group of neurological conditions that cause abnormal movements. These disorders, even when subtle, may be symptomatic of a broad spectrum of medical issues, from neurological to musculoskeletal. Clinicians and researchers still encounter challenges in understanding the underlying pathologies. In light of this, medical professionals and associated researchers are increasingly looking towards the fast-evolving domain of computer vision in pursuit of precise and dependable automated diagnostic tools to support clinical diagnosis. To this end, this thesis explores the feasibility of the interpretable and accurate human movement disorders analysis system using graph neural networks. Cerebral Palsy (CP) and Parkinson’s Disease (PD) are two common neurological diseases associated with movement disorders that seriously affect patients’ quality of life. Specifically, CP is estimated to affect 2 in 1000 babies born in the UK each year, while PD affects an estimated 10 million people globally. Considering their clinical significance and properties, we develop and examine the state-of-the-art attention-informed Graph Neural Networks (GNN) for robust and interpretable CP prediction and PD diagnosis. We highlight the significant differences between the human body movement frequency of CP infants and healthy groups, and propose frequency attention-informed convolutional networks (GCNs) and spatial frequency attention based GCNs to predict CP with strong interpretability. To support the early diagnosis of PD, we propose novel video-based deep learning system, SPA-PTA, with a spatial pyramidal attention design based on clinical observations and mathematical theories. Our systems provide undiagnosed PD patients with low-cost, non-intrusive PT classification and tremor severity rating results as a PD warning sign with interpretable attention visualizations

Dynamic Gaussian Splatting from Markerless Motion Capture can Reconstruct Infants Movements

Easy access to precise 3D tracking of movement could benefit many aspects of rehabilitation. A challenge to achieving this goal is that while there are many datasets and pretrained algorithms for able-bodied adults, algorithms trained on these datasets often fail to generalize to clinical populations including people with disabilities, infants, and neonates. Reliable movement analysis of infants and neonates is important as spontaneous movement behavior is an important indicator of neurological function and neurodevelopmental disability, which can help guide early interventions. We explored the application of dynamic Gaussian splatting to sparse markerless motion capture (MMC) data. Our approach leverages semantic segmentation masks to focus on the infant, significantly improving the initialization of the scene. Our results demonstrate the potential of this method in rendering novel views of scenes and tracking infant movements. This work paves the way for advanced movement analysis tools that can be applied to diverse clinical populations, with a particular emphasis on early detection in infants

Concurrent Validity of a Custom Method for Markerless 3D Full-Body Motion Tracking of Children and Young Adults Based on a Single RGB-D Camera

Low-cost, portable RGB-D cameras with integrated body tracking functionality enable easy-to-use 3D motion analysis without requiring expensive facilities and specialized personnel. However, the accuracy of existing systems is insufficient for most clinical applications. In this study, we investigated the concurrent validity of our custom tracking method based on RGB-D images with respect to a gold-standard marker-based system. Additionally, we analyzed the validity of the publicly available Microsoft Azure Kinect Body Tracking (K4ABT). We recorded 23 typically developing children and healthy young adults (aged 5 to 29 years) performing five different movement tasks using a Microsoft Azure Kinect RGB-D camera and a marker-based multi-camera Vicon system simultaneously. Our method achieved a mean per joint position error over all joints of 11.7 mm compared to the Vicon system, and 98.4% of the estimated joint positions had an error of less than 50 mm. Pearson's correlation coefficients r ranged from strong ( r =0.64) to almost perfect ( 0.99). K4ABT demonstrated satisfactory accuracy most of the time but showed short periods of tracking failures in nearly two-thirds of all sequences limiting its use for clinical motion analysis. In conclusion, our tracking method highly agrees with the gold standard system. It paves the way towards a low-cost, easy-to-use, portable 3D motion analysis system for children and young adults

Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies

A recent experiment probed how purposeful action emerges in early life by manipulating infants’ functional connection to an object in the environment (i.e., tethering an infant’s foot to a colorful mobile). Vicon motion capture data from multiple infant joints were used here to create Histograms of Joint Displacements (HJDs) to generate pose-based descriptors for 3D infant spatial trajectories. Using HJDs as inputs, machine and deep learning systems were tasked with classifying the experimental state from which snippets of movement data were sampled. The architectures tested included k-Nearest Neighbour (kNN), Linear Discriminant Analysis (LDA), Fully connected network (FCNet), 1D-Convolutional Neural Network (1D-Conv), 1D-Capsule Network (1D-CapsNet), 2D-Conv and 2D-CapsNet. Sliding window scenarios were used for temporal analysis to search for topological changes in infant movement related to functional context. kNN and LDA achieved higher classification accuracy with single joint features, while deep learning approaches, particularly 2D-CapsNet, achieved higher accuracy on full-body features. For each AI architecture tested, measures of foot activity displayed the most distinct and coherent pattern alterations across different experimental stages (reflected in the highest classification accuracy rate), indicating that interaction with the world impacts the infant behaviour most at the site of organism~world connection