用深度相机实时识别坐姿，帮上班族预防腰椎病。
SitPose: Real-Time Detection of Sitting Posture and Sedentary Behavior Using Ensemble Learning With Depth Sensor
- 融合多模型的集成学习，通过软投票提升识别准确率
- 在33,409条数据上实现98.1%的F1分数
- 适合办公健康监测、人机交互场景使用
不良坐姿可引发多种职业性肌肉骨骼疾病（WMSDs）。办公人员约81.8%的工作时间处于久坐状态，久坐可能诱发颈椎病、心血管疾病等慢性病。为应对这些健康问题，我们提出SitPose系统，利用最新Kinect深度相机实现坐姿与久坐行为的实时检测。系统实时追踪骨骼关键点的三维坐标，并计算相关关节角度。通过招募36名参与者，构建了包含六种坐姿和一种站姿的数据库，共33,409个数据点。我们对多种先进机器学习算法进行测试并比较其坐姿识别性能。结果表明，基于软投票机制的集成学习模型达到最高F1分数98.1%。最终，我们基于该集成模型部署了SitPose系统，旨在改善坐姿习惯，减少久坐行为。
原文摘要 · Abstract (English)
Poor sitting posture can lead to various work-related musculoskeletal disorders (WMSDs). Office employees spend approximately 81.8% of their working time seated, and sedentary behavior can result in chronic diseases such as cervical spondylosis and cardiovascular diseases. To address these health concerns, we present SitPose, a sitting posture and sedentary detection system utilizing the latest Kinect depth camera. The system tracks 3D coordinates of bone joint points in real-time and calculates the angle values of related joints. We established a dataset containing six different sitting postures and one standing posture, totaling 33,409 data points, by recruiting 36 participants. We applied several state-of-the-art machine learning algorithms to the dataset and compared their performance in recognizing the sitting poses. Our results show that the ensemble learning model based on the soft voting mechanism achieves the highest F1 score of 98.1%. Finally, we deployed the SitPose system based on this ensemble model to encourage better sitting posture and to reduce sedentary habits.
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