The kinectome: A comprehensive kinematic map of human motion in health and disease

Abstract

Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body elements. Network science can be a useful approach to describe movement as a whole and to extract features that are relevant to understanding both its complex physiology and the pathophysiology of movement disorders. Here, we propose to represent human movement as a network (that we named the kinectome), where nodes represent body points, and edges are defined as the correlations of the accelerations between each pair of them. We applied this framework to healthy individuals and patients with Parkinson's disease, observing that the patients' kinectomes display less symmetrical patterns as compared to healthy controls. Furthermore, we used the kinectomes to successfully identify both healthy and diseased subjects using short gait recordings. Finally, we highlighted topological features that predict the individual clinical impairment in patients. Our results define a novel approach to study human movement. While deceptively simple, this approach is well-grounded, and represents a powerful tool that may be applied to a wide spectrum of frameworks.

Keywords: Parkinson's disease; gait analysis; movement pattern; network.

FIGURE 1

Scheme of kinectome analysis. (A) Marker positions of the bone landmarks. Acceleration and jerk time series are computed based on the positions of the markers during the gait cycle, as recorded by a stereophotogrammetric system. (B) Kinectome: the covariance matrix is computed correlating each pair of the bone markers acceleration or jerk time series; different kinectomes were built, based on the mediolateral and anteroposterior axis, and separately taking into account the accelerations and the jerks. (C) Functional network modularity was investigated using the Louvain method, an algorithm customarily employed for community detection. (D) Schematic illustration of the fingerprint analysis. Two kinectomes (named test and retest) have been computed for each subject. The identifiability matrix is obtained by correlating the test and retest kinectomes of each subject. The main diagonal displays self‐identifiability. (E) Graphical representation of the bone markers network used for the topological analysis. Note that the bone markers positioned on the back of the body are not visible.

FIGURE 2

Schematic description of the network analysis. The flowchart describes the methodological approaches applied to the kinectomes. Three main network frameworks were explored: modularity, fingerprint,, and topology. For each of them, the methodological approach and the aim of the analysis have been highlighted.

FIGURE 3

From bones to kinectomes. (A) Illustration of the bone markers position on the kinectome. Each kinematic information derived from each bone marker is used as entry data for both rows and columns. The edges of the kinectome stem from the pairwise interaction between bone markers. (B) Acceleration and jerk kinectomes averaged among healthy subjects (HS) in the mediolateral (ML) and the anteroposterior (AP) axes. The interactions between body elements vary according to both the specific axis and measurement (acceleration or jerk).

FIGURE 4

Within group variability of the kinectomes. Standard deviations of the anteroposterior acceleration kinectomes of healthy controls (HC) and Parkinson's patients (PD). Higher values (i.e., yellow entries in the matrices) indicate greater heterogeneity (i.e., higher standard deviation).

FIGURE 5

Kinematic modular organization of the kinectomes. Allegiance matrices for cluster analysis, based on the Louvain method and consensus‐clustered through 100 iterations. The algorithm automatically defines which body parts belong to the same community, suggesting a functional relationship among those elements. Each matrix includes clustering information from both accelerations and jerks. Healthy subjects (HS) and healthy controls (HC) share the same communities in both mediolateral (ML) and anteroposterior (AP) axes. Parkinson's disease (PD) patients’ matrices show different structural organizations. Body parts depicted with the same color belong to the same functional community.

FIGURE 6

Motion fingerprinting: identifiability based on kinectomes. Identifiability matrices of healthy controls (HC) and Parkinson's disease (PD) patients, based on jerk and acceleration kinectomes in mediolateral (ML) and anteroposterior (AP) axes. The highest values within the main diagonal (I‐self) convey great self‐similarity. Off diagonal elements (I‐others) are representative of the similarity between different subjects. IR, identification rate; I‐diff is the differential identifiability scores of the dataset and is defined as I‐self–I‐others.

FIGURE 7

Identifiability comparison between healthy controls and patients. Box plot for the comparison of I‐diff and I‐others between healthy controls (HC) and patients with Parkinson's disease (PD). High I‐diff values imply that individuals are more similar to themselves than they are to the other subjects of the same group. High I‐others values indicate high within‐group similarity among the subjects of a group. The box represents data from the 25th to the 75th percentiles; the horizontal line shows the median; error lines indicate the 10th and 90th percentiles, and values falling beyond them are represented by colored dots. *, represents significant Bonferroni‐corrected p‐values.

FIGURE 8

Edge‐based identification rate. Identification rate (IR) for healthy controls (HC) and patients with Parkinson's disease (PD) kinectomes, for acceleration and jerk parameters in mediolateral (ML) and anteroposterior (AP) axes. The IR is computed in an iterative fashion: starting from three edges, at each iteration, one edge is added and the IR is computed. The edges were included following an order based on their contribution to the identifiability (from the most to the least contributing), as measured by the intraclass correlation analysis. The HC group exceeded the 99% identification threshold with a smaller number of edges (roughly 12), as compared to the PD patients (about 30).

FIGURE 9

Clinical relevance of kinectome features. (A) The left panel highlights the position of the 10th thoracic vertebra, whose degree in the kinectome derived from the mediolateral accelerations (MLA‐T10) has been analyzed within a clinical framework. The middle panel shows that patients with Parkinson's disease (PD) have significantly higher MLA‐T10 degree with respect to the healthy controls (HC); the right panel shows the positive significant correlation between the MLA‐T10 degree and the clinical motor impairment assessed through the Unified Parkinson's Disease Rating Scale (UPDRS). (B) Multilinear regression model for the prediction of the UPDRS from the MLA‐T10 degree. The left panel shows the explained variance (R ²) of the UPDRS, while sequentially adding the predictors (i.e., age, education, gender, and MLA‐T10 degree) to the model; MLA‐T10 degree was a significant predictor with positive beta coefficient; the middle panel displays the relationship between empirical and predicted UPDRS scores, with k‐fold cross validation (k = 5); and the right panel illustrates the distribution of the residuals with k‐fold cross validation (k = 5).