Distinguishing Patients With a Coordination Disorder From Healthy Controls Using Local Features of Movement Trajectories During the Finger-to-Nose Test

Assessment of coordination disorders is valuable for monitoring progression of patients, distinguishing healthy and pathological conditions, and ultimately aiding in clinical decision making, thereby offering the possibility to improve medical care or rehabilitation. A common method to assess movement disorders is by using clinical rating scales. However, rating scales depend on the evaluation and interpretation of an observer, implying that subjective phenotypic assignment precedes the application of the scales. Objective and more accurate methods are under continuous development but gold standards are still scarce. Here, we show how a method we previously developed, originally aimed at assessing dynamic balance by a probabilistic generalized linear model, can be used to assess a broader range of functional movements. In this paper, the method is applied to distinguish patients with coordination disorders from healthy controls. We focused on movements recorded during the finger-to-nose task (FNT), which is commonly used to assess coordination disorders. We also compared clinical FNT scores and model scores. Our method achieved 84% classification accuracy in distinguishing patients and healthy participants, using only two features. Future work could entail testing the reliability of the method by using additional features and other clinical tests such as finger chasing, quiet standing, and/or usage of tracking devices such as depth cameras or force plates

Instrumented classification of patients with early onset ataxia or developmental coordination disorder and healthy control children combining information from three upper limb SARA tests

Background: Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) share several phenotypical characteristics, which can be clinically hard to distinguish. Aim: To combine quantified movement information from three tests obtained from inertial measure-ments units (IMUs), to improve the classification of EOA and DCD patients and healthy controls compared to using a single test. Methods: Using IMUs attached to the upper limbs, we collected data from EOA, DCD and healthy control children while they performed the three upper limb tests (finger to nose, finger chasing and fast alter -nating movements) from the Scale for the Assessment and Rating of Ataxia (SARA) test. The most relevant features for classification were extracted. A random forest classifier with 300 trees was used for classification. The area under the receiver operating curve (ROC-AUC) and precision-recall plots were used for classification performance assessment. Results: The most relevant discerning features concerned smoothness and velocity of movements. Classification accuracy on group level was 85.6% for EOA, 63.5% for DCD and 91.2% for healthy control children. In comparison, using only the finger to nose test for classification 73.7% of EOA and 53.4% of DCD patients and 87.2% of healthy controls were accurately classified. For the ROC/precision recall plots the AUC was 0.96/0.89 for EOA, 0.92/0.81 for DCD and 0.97/0.94 for healthy control children. Discussion: Using quantified movement information from all three SARA-kinetic upper limb tests improved the classification of all diagnostic groups, and in particular of the DCD group compared to using only the finger to nose test. (c) 2021 The Authors. Published by Elsevier Ltd on behalf of European Paediatric Neurology Society. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Visual analysis and quantitative assessment of human movement

Our ability to navigate in our environment depends on the condition of the musculoskeletal and nervous systems. Any deterioration of a component of these two systems can cause instability or disability of body movements. Such deterioration can happen as a consequence of natural age-related changes, injuries and/or diseases. The ability to objectively and quantitatively assess different functional tasks such as postural control, gait or hand movements can be useful for preventing falls, following disease progression, assessing the effectiveness of medical care and interventions, and ultimately improving the accuracy of clinical decisions. The benefits are clear. However, current metrics, algorithms and tools are not enough to analyze and understand the infinite complexity of human movements. In this thesis, I developed visualizations and a novel method to assess human movement in real-time using data collected from tracking devices such as Kinect and inertial measurement units. This method was used to assess balance performance on data from exergames, digital games controlled by body movements, and to classify young and older adults achieving more than 85% accuracy. This kind of assessment can also be used to provide meaningful feedback and to automatically adapt the difficulty of exergames, which in turn could increase motivation to play and improve balance control among older adults. Additionally, the method was used to classify healthy participants and patients with a coordination disorder during a hand movement task achieving 84% accuracy. In conclusion, this thesis presents a promising method that can be used for assessing and understanding human movement

2D Gait Skeleton Data Normalization for Quantitative Assessment of Movement Disorders from Freehand Single Camera Video Recordings

Overlapping phenotypic features between Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) can complicate the clinical distinction of these disorders. Clinical rating scales are a common way to quantify movement disorders but in children these scales also rely on the observer's assessment and interpretation. Despite the introduction of inertial measurement units for objective and more precise evaluation, special hardware is still required, restricting their widespread application. Gait video recordings of movement disorder patients are frequently captured in routine clinical settings, but there is presently no suitable quantitative analysis method for these recordings. Owing to advancements in computer vision technology, deep learning pose estimation techniques may soon be ready for convenient and low-cost clinical usage. This study presents a framework based on 2D video recording in the coronal plane and pose estimation for the quantitative assessment of gait in movement disorders. To allow the calculation of distance-based features, seven different methods to normalize 2D skeleton keypoint data derived from pose estimation using deep neural networks applied to freehand video recording of gait were evaluated. In our experiments, 15 children (five EOA, five DCD and five healthy controls) were asked to walk naturally while being videotaped by a single camera in 1280 × 720 resolution at 25 frames per second. The high likelihood of the prediction of keypoint locations (mean = 0.889, standard deviation = 0.02) demonstrates the potential for distance-based features derived from routine video recordings to assist in the clinical evaluation of movement in EOA and DCD. By comparison of mean absolute angle error and mean variance of distance, the normalization methods using the Euclidean (2D) distance of left shoulder and right hip, or the average distance from left shoulder to right hip and from right shoulder to left hip were found to better perform for deriving distance-based features and further quantitative assessment of movement disorders

Cognitive Robots for Social Interactions

One of my goals is to work towards developing Cognitive Robots, especially with regard to improving the functionalities that facilitate the interaction with human beings and their surrounding objects. Any cognitive system designated for serving human beings must be capable of processing the social signals and eventually enable efficient prediction and planning of appropriate responses. My main focus during my PhD study is to bridge the gap between the motoric space and the visual space. The discovery of the mirror neurons ([RC04]) shows that the visual perception of human motion (visual space) is directly associated to the motor control of the human body (motor space). This discovery poses a large number of challenges in different fields such as computer vision, robotics and neuroscience. One of the fundamental challenges is the understanding of the mapping between 2D visual space and 3D motoric control, and further developing building blocks (primitives) of human motion in the visual space as well as in the motor space. First, I present my study on the visual-motoric mapping of human actions. This study aims at mapping human actions in 2D videos to 3D skeletal representation. Second, I present an automatic algorithm to decompose motion capture (MoCap) sequences into synergies along with the times at which they are executed (or "activated") for each joint. Third, I proposed to use the Granger Causality as a tool to study the coordinated actions performed by at least two units. Recent scientific studies suggest that the above "action mirroring circuit" might be tuned to action coordination rather than single action mirroring. Fourth, I present the extraction of key poses in visual space. These key poses facilitate the further study of the "action mirroring circuit". I conclude the dissertation by describing the future of cognitive robotics study

Visual and non-visual control of movement: the role of proprioception in upper limb function after stroke

This dissertation presents a series of studies into human reach and grasp, focusing on the neural systems and behaviors of upper-limb action that underly performance under varied sensory conditions: specifically, acting with and without visual feedback of the limb and under typical or impaired proprioceptive sensation (proprioceptive decline with aging and proprioceptive deficit following stroke). Under typical conditions, a combination of visual and non-visual (e.g., proprioception) sources of information are used to guide action. In the instance of stroke survivors or elderly individuals with proprioceptive deficits/decline, there may be a necessary reliance on visual information to perform. The studies are conducted in healthy adults (across the lifespan) and stroke survivors, who often suffer from somatosensory deficits. The overall goal of each study is: 1) the identification of neural systems involved in reaching and grasping without online visual feedback of the limb, 2) the development and validation of a novel approach to measuring upper-limb proprioceptive function, and 3) a pilot study using head-mounted VR to assess the relationship between proprioceptive capacity/deficit (healthy individuals and stroke survivors) and performance with or without online visual feedback of the limb during varied reaching tasks (ballistic reach vs slow/controlled reach)

Typical and atypical development of the brain’s functional network architecture

The human brain is a complex organ that gives rise to many behaviors. Specialized neural regions cooperate as functional networks that form an intricate functional architecture. Development provides a unique window into how brain functioning and human thinking are affected if the necessary neural features and connections are not fully formed. Similarly, developmental disorders can shed light on atypical trajectories of neural systems that may lead to or be a consequence of symptomatic behavior. A description of the typical and atypical development of functional networks is essential to identify the features of brain organization critical for mature human thinking and to provide better diagnosis, treatment, and prognosis in neurodevelopmental disorders. Recently, resting state functional MRI has been found to illuminate functionally related regions, giving access to functional networks and the organization of brain’s functional architecture. This thesis aims to harness resting-state functional connectivity to explore how functional networks coordinate over the course of development. First, I present our work investigating the organizing principles of typical developmental patterns in functional networks (Chapter 2). Then, I apply these approaches to the atypical development of functional networks in Tourette syndrome (TS), a developmental disorder characterized by motor and vocal tics. In this work, we tested whether the patterns in functional networks that distinguish individuals with TS from controls differ between children and adults and alter the typical developmental pattern of functional networks (Chapter 3). Lastly, I present our work to identify and describe the coordination of specific functional networks that develop atypically in TS (Chapter 4)