![]() ![]() ![]() Saez, Y., Baldominos, A., Isasi, P.: A comparison study of classifier algorithms for cross-person physical activity recognition. Lynn, M., Corlett, N.: RULA: a survey method for the investigation of work-related upper limb disorders. Papafotis, K., Sotiriadis, P.P.: Accelerometer and magnetometer joint calibration and axes alignment. Xue, Y., Ju, Z., Xiang, K., Chen, J., Liu, H.: Multimodal human hand motion sensing and analysis-a review. Lara, D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. Nweke, H.F., Teh, Y.W., Al-garadi, M.A., Alo, U.R.: Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: state of the art and research challenges. Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Marty, J., Porcher, B., Autissier, R.: Traumatismes de la main et accident du travail, Statistiques et prevention. This is the editor, where you can edit your recordings and export it to gif, apng, video, project, images and psd. After starting the extension, you only need to move the mouse on the page and click to accurately select the element you want to record, or you can select any area by dragging. Īrmstrong, T.J., Foulke, J.A., Joseph, B.S., Goldstein, S.A.: Investigation of cumulative trauma disorders in a poultry processing plant. It is a little tool which can help you to record animations, videos and everything else on your browser page to an animated gif picture. Lee, K.S., Jung, M.C.: Ergonomic evaluation of biomechanical hand function. It was found that the lectures from the six inertial sensors and the six force resistive sensors showed a pattern that facilitates the recognition of basic and more complex movements (flexion-extension and spheric handgrip) through visual analysis of the plotted data, even at different ranges of motion. A more complex movement involving a 3-finger spherical grip was also recorded. Data were classified by traditional methods. ![]() Two common hand movements were analyzed using wrist flexion-extension with a small (−15° to 15°) and medium (15°) range of motion and flexion-extension movement with the hand pronated-supinated. Data were collected from a hand-motion capture system conformed by six inertial measurement units and six resistive force sensors from hand and fingers movements. We highlight the importance of studying hand movements executed at work, and how they affect workers’ health and productivity. One of the most frequently-used body regions in daily activities is the upper limbs, and many of the work-related musculoskeletal disorders occur in this area, mainly the hands. ![]()
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