Deep Learning Based Human Locomotion Recognition in Video Sequences

Bejinariu, Silviu-Ioan; Costin, Hariton; Rotaru, F.; Luca, Ramona; Lazăr, Camelia

doi:10.1109/ehb50910.2020.9280168

Cited by 2 publications

(1 citation statement)

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“…They considered sensors-, video-, and markers-based datasets for posture prediction. They achieved strong and accurate results using these methods [ 50 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ]. In this research, we opted for Machine Learning algorithms for the prediction of neck postures/movements.…”

Section: Resultsmentioning

confidence: 99%

Real-Time Tracking of Human Neck Postures and Movements

Kumar

Elias

2021

Healthcare

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Improper neck postures and movements are the major causes of human neck-related musculoskeletal disorders. To monitor, quantify, analyze, and detect the movements, remote and non-invasive based methods are being developed for prevention and rehabilitation. The purpose of this research is to provide a digital platform for analyzing the impact of human neck movements on the neck musculoskeletal system. The secondary objective is to design a rehabilitation monitoring system that brings accountability in the treatment prescribed, which is shown in the use-case model. To record neck movements effectively, a Smart Neckband integrated with the Inertial Measurement Unit (IMU) was designed. The initial task was to find a suitable position to locate the sensors embedded in the Smart Neckband. IMU-based real-world kinematic data were captured from eight research subjects and were used to extract kinetic data from the OpenSim simulation platform. A Random Forest algorithm was trained using the kinetic data to predict the neck movements. The results obtained correlated with the novel idea proposed in this paper of using the hyoid muscles to accurately detect neck postures and movements. The innovative approach of integrating kinematic data and kinetic data for analyzing neck postures and movements has been successfully demonstrated through the efficient application in a rehabilitation use case with about 95% accuracy. This research study presents a robust digital platform for the integration of kinematic and kinetic data that has enabled the design of a context-aware neckband for the support in the treatment of neck musculoskeletal disorders.

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