In this work, we propose the use of Multimodal Human-Computer Interfaces (MHCI) through body poses to command a drone in an easy and intuitive way. First, the human-user pose is recovered from a video stream, with the help of the open source library Open Pose. Then, a Support Vector Classifier (SVC), trained to distinguish between different body poses, is used to interpret eleven different human poses as the most important high level commands to the drone. The proposed strategy was successfully implemented to remotely control a drone through a web interface, where the user can interact with a drone in a remote location using only a web interface. Real-time experiments were carried out with fourteen different volunteers, selected to represent different segments of the population, with different ages, gender, experience with technology, socioeconomic class, etc., in order to evaluate the user experience with the help of a User Experience Questionnaire (UEQ), demonstrating satisfactory results. The study suggests that the use of the proposed MHCI received good acceptance between the participants, even in users without previous experience with drones, and received excellent scores in terms of attractiveness, stimulation and novelty from most of the volunteers.

在这项工作中，我们建议通过身体姿势使用多模式人机界面（MHCI）以简单直观的方式命令无人机。首先，在开源库 Open Pose 的帮助下，从视频流中恢复人类用户姿势。然后，经过训练以区分不同身体姿势的支持向量分类器 (SVC) 用于将 11 种不同的人体姿势解释为无人机最重要的高级命令。所提出的策略已成功实施，可通过网络界面远程控制无人机，用户只需使用网络界面即可在远程位置与无人机进行交互。我们对 14 名不同的志愿者进行了实时实验，这些志愿者代表了不同年龄、性别、技术经验、社会经济阶层等不同群体的志愿者，以便在用户的帮助下评估用户体验经验问卷（UEQ），结果令人满意。研究表明，所提出的 MHCI 的使用得到了参与者的良好接受，甚至在没有无人机使用经验的用户中也是如此，并且大多数志愿者在吸引力、刺激性和新颖性方面获得了优异的分数。