ArticleAnnals of the New York Academy of Sciences2022
The kinectome: A comprehensive kinematic map of human motion in health and disease.
Article in Annals of the New York Academy of Sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Whole body analysis of functional communities and topological features of gait with different speeds in Parkinson's disease.Journal of neurology · 2026Article
- Inter-segmental coordination patterns in Parkinson's disease are particularly disturbed during preferred walking speed: a data-driven network approach.Journal of neuroengineering and rehabilitation · 2025Article
- An Accessible AI-Assisted Rehabilitation System for Guided Upper Limb Therapy.Sensors (Basel, Switzerland) · 2025Article
- Video-Based Biomechanical Analysis Captures Disease-Specific Movement Signatures of Different Neuromuscular Diseases.NEJM AI · 2025Article
- Mapping the complexity of motor variability: From individual space of variability to motor fingerprints.Behavior research methods · 2025Article
- Deep learning-based detection of affected body parts in Parkinson's disease and freezing of gait using time-series imaging.Scientific reports · 2024Article
- Kinematic network of joint motion provides insight on gait coordination: An observational study on Parkinson's disease.Heliyon · 2024Article
- The effect of dopaminergic treatment on whole body kinematics explored through network theory.Scientific reports · 2024Article
- Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics.PLoS computational biology · 2023Article
- The kinectome: A comprehensive kinematic map of human motion in health and disease.Annals of the New York Academy of Sciences · 2022Article
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9 authors.
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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.
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