用手机传感器实现动作识别，打造沉浸式健身游戏。
Mobile Exergames: Activity Recognition Based on Smartphone Sensors
- 融合加速度计、陀螺仪和磁力计数据进行动作识别。
- 动作识别准确率高，支持真实与虚假动作区分。
- 结合语音识别提升游戏互动性，适合健康类应用。
智能手机传感器在提供个人活动与行为信息方面具有巨大潜力。人体活动识别正被广泛应用于游戏、医疗和监控等领域。本文提出一款概念验证的2D无尽游戏Duck Catch & Fit，其内置详细的动作识别系统，利用手机的加速度计、陀螺仪和磁力计传感器，通过特征提取与学习机制，识别静止、侧向移动及虚假侧移等动作。此外，系统还集成语音识别模块，可识别关键词“fire”以增加游戏难度。实验结果表明，基于机器学习的方法可在高准确率下实现人类动作识别，且运动与语音的融合显著提升了游戏沉浸感。
原文摘要 · Abstract (English)
Smartphone sensors can be extremely useful in providing information on the activities and behaviors of persons. Human activity recognition is increasingly used for games, medical, or surveillance. In this paper, we propose a proof-of-concept 2D endless game called Duck Catch & Fit, which implements a detailed activity recognition system that uses a smartphone accelerometer, gyroscope, and magnetometer sensors. The system applies feature extraction and learning mechanism to detect human activities like staying, side movements, and fake side movements. In addition, a voice recognition system is combined to recognize the word "fire" and raise the game's complexity. The results show that it is possible to use machine learning techniques to recognize human activity with high recognition levels. Also, the combination of movement-based and voice-based integrations contributes to a more immersive gameplay.
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