1 research outputs found
Predictive Analysis for Detection of Human Neck Postures using a robust integration of kinetics and kinematics
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.Comment: 12 pages, 15 figures, To appear in the Journal of Computer Methods in
Biomechanics and Biomedical Engineerin