论文标题
预测分析使用动力学和运动学的强大整合来检测人颈姿势
Predictive Analysis for Detection of Human Neck Postures using a robust integration of kinetics and kinematics
论文作者
论文摘要
需要监测，测量，量化和分析人类的颈部姿势和运动，作为医疗保健应用中的一种预防措施。颈部姿势不当是需要治疗和康复的颈部肌肉骨骼疾病的越来越多的来源。本文提出的研究的动机是需要开发一种通知机制，以使颈部使用不当。传感器捕获的运动学数据在准确地对颈部姿势进行分类方面存在局限性。因此，我们提出了对运动和动力学数据的综合使用，以有效地对颈部姿势进行分类。使用机器学习算法，我们在对该数据的预测分析中获得了100％的精度。研究分析和讨论表明，考虑到颈带捕获的相应运动学数据，舌骨肌肉的动力学数据可以准确检测到颈部姿势。提出的用于整合运动学和动力学数据的强大平台使智能颈部带来了预防颈部肌肉骨骼疾病的设计。
Human neck postures and movements need to be monitored, measured, quantified and analyzed, as a preventive measure in healthcare applications. Improper neck postures are an increasing source of neck musculoskeletal disorders, requiring therapy and rehabilitation. The motivation for the research presented in this paper was the need to develop a notification mechanism for improper neck usage. Kinematic data captured by sensors have limitations in accurately classifying the neck postures. Hence, we propose an integrated use of kinematic and kinetic data to efficiently classify neck postures. Using machine learning algorithms we obtained 100% accuracy in the predictive analysis of this data. The research analysis and discussions show that the kinetic data of the Hyoid muscles can accurately detect the neck posture given the corresponding kinematic data captured by the neck-band. The proposed robust platform for the integration of kinematic and kinetic data has enabled the design of a smart neck-band for the prevention of neck musculoskeletal disorders.
