DOI: 10.17586/1023-5086-2018-85-09-49-58

Определение физической нагрузки с использованием мимической активности

Полный текст на elibrary.ru

Публикация в Journal of Optical Technology

Ссылка для цитирования:

Xuqiang Li, Kan Hong, Guodong Liu Detection of physical stress using facial muscle activity (Определение физической нагрузки с использованием мимической активности) [на англ. яз.] // Оптический журнал. 2018. Т. 85. № 9. С. 49–58. http://doi.org/10.17586/1023-5086-2018-85-09-49-58

Xuqiang Li, Kan Hong, Guodong Liu Detection of physical stress using facial muscle activity (Определение физической нагрузки с использованием мимической активности) [in English] // Opticheskii Zhurnal. 2018. V. 85. № 9. P. 49–58. http://doi.org/10.17586/1023-5086-2018-85-09-49-58

Ссылка на англоязычную версию:

Xuqiang Li, Kan Hong, and Guodong Liu, "Detection of physical stress using facial muscle activity," Journal of Optical Technology. 85(9), 562-569 (2018). https://doi.org/10.1364/JOT.85.000562

Аннотация:

Исследована возможность использования мультиспектральных изображений лиц для определения степени физической нагрузки человека. Разработанный алгоритм обработки мультиспектральных изображений был применен для анализа мимической активности лиц добровольцев без информирования последних. Алгоритмическая модель проходила верификацию для классификации исходных показателей и степени физических нагрузок. При применении алгоритма наилучшие результаты составляли 75%, что позволяет продолжить работу по его дальнейшему внедрению. Результаты опытов продемонстрировали потенциал использования мультиспектральных изображений для неинвазивного определения степени физических нагрузок человека.

Ключевые слова:

мультиспектральное изображение, физические нагрузки

Благодарность:

Работа выполнена при финансовой поддержке Национального фонда естественных наук Китая (61741507), Научного фонда для молодых ученых провинции Цзянси (грант № 20171BAB212019) и Проекта научно-технологического фонда департамента образования провинции Цзянси (грант № GJJ150798).

Коды OCIS: 100.0100

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