Predictive Analysis for Detection of Human Neck Postures using a robust integration of kinetics and kinematics
This work addresses the need for a notification mechanism for improper neck usage in healthcare applications, representing an incremental improvement by combining existing data types.
The paper tackled the problem of accurately classifying human neck postures to prevent musculoskeletal disorders by integrating kinematic and kinetic data, achieving 100% accuracy in predictive analysis using machine learning algorithms.
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.