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Human neck postures and movements need to be monitored, measured, quantifiedand analyzed, as a preventive measure in healthcare applications. Improper neckpostures are an increasing source of neck musculoskeletal disorders, requiringtherapy and rehabilitation. The motivation for the research presented in thispaper was the need to develop a notification mechanism for improper neck usage.Kinematic data captured by sensors have limitations in accurately classifyingthe neck postures. Hence, we propose an integrated use of kinematic and kineticdata to efficiently classify neck postures. Using machine learning algorithmswe obtained 100% accuracy in the predictive analysis of this data. The researchanalysis and discussions show that the kinetic data of the Hyoid muscles canaccurately detect the neck posture given the corresponding kinematic datacaptured by the neck-band. The proposed robust platform for the integration ofkinematic and kinetic data has enabled the design of a smart neck-band for theprevention of neck musculoskeletal disorders.

Predictive Analysis for Detection of Human Neck Postures using a robust integration of kinetics and kinematics