The kinectome: A comprehensive kinematic map of human motion in health and disease,Annals of the New York Academy of Sciences

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The kinectome: A comprehensive kinematic map of human motion in health and disease
Annals of the New York Academy of Sciences ( IF 4.1 ) Pub Date : 2022-07-15 , DOI: 10.1111/nyas.14860
Emahnuel Troisi Lopez ₁ , Pierpaolo Sorrentino ₂ , Marianna Liparoti ₃ , Roberta Minino ₁ , Arianna Polverino ₄ , Antonella Romano ₁ , Anna Carotenuto ₅ , Enrico Amico _{6,

7} , Giuseppe Sorrentino _{1,

4,

8}

Affiliation

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.

中文翻译：

运动组：健康和疾病状态下人体运动的综合运动学图

人类随意运动源于许多肌肉骨骼部分在空间和时间上的协调激活。然而，目前研究人体运动的方法仍然仅限于评估几个身体元素之间的协同作用。网络科学可以作为一种有用的方法来描述整个运动，并提取与理解其复杂的生理学和运动障碍的病理生理学相关的特征。在这里，我们建议将人体运动表示为一个网络（我们将其命名为运动组），其中节点代表身体点，边缘定义为每对之间的加速度的相关性。我们将该框架应用于健康个体和帕金森病患者，观察到与健康对照相比，患者的运动组表现出不太对称的模式。此外，我们使用运动组通过短步态记录成功识别健康和患病受试者。最后，我们强调了预测患者个体临床损伤的拓扑特征。我们的结果定义了一种研究人类运动的新方法。虽然看似简单，但这种方法有充分的基础，并且代表了一种可以应用于广泛框架的强大工具。

更新日期：2022-07-15

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