Ключевые слова: диагностика риска падения; нарушения ходьбы; нарушения равновесия; биомеханика движений; искусственный интеллект.
- Аннотация
- Список литературы
- Как цитировать в References
Цель исследования — создание модели искусственного интеллекта для прогнозирования вероятности падений на основе анализа видеозаписей паттерна ходьбы.
Материалы и методы. В исследование включено 187 пациентов (медиана возраста — 59 лет) с неврологическими заболеваниями различной этиологии, предъявлявших жалобы на нарушение равновесия и неустойчивость при ходьбе. Всем участникам выполнена видеорегистрация движений с использованием смартфона в условиях теста 10-метровой ходьбы и теста «Встань и иди» (TUG). На основе анализа 2077 шагов, зафиксированных у 122 пациентов, с применением архитектуры YOLO-NAS Pose M и двухуровневой модели машинного обучения разработан алгоритм стратификации риска падений.
Результаты. Итоговая модель при анализе видеоданных достигла точности 76% при прогнозировании риска, средняя абсолютная ошибка предсказания временных параметров составила 1,445 с. В качестве ключевых биомеханических предикторов риска падений идентифицированы ширина базы шага на уровне голеностопных суставов, латеральные колебания туловища, ширина базы шага на уровне коленных суставов, вертикальный клиренс стопы в фазе переноса, а также временные параметры шага.
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