SitPose: Real-Time Detection of Sitting Posture and Sedentary Behavior Using Ensemble Learning With Depth Sensor

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-02-24 DOI:10.1109/JSEN.2025.3541821

Hang Jin;Xin He;Lingyun Wang;Yujun Zhu;Weiwei Jiang;Xiaobo Zhou

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引用次数: 0

Abstract

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 3-D 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 33409 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

$F_{1}$

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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SitPose：使用深度传感器集成学习实时检测坐姿和久坐行为

不良坐姿会导致各种与工作相关的肌肉骨骼疾病（WMSDs）。办公室员工大约81.8%的工作时间是坐着的，久坐行为会导致慢性疾病，如颈椎病和心血管疾病。为了解决这些健康问题，我们提出了SitPose，一种利用最新Kinect深度摄像头的坐姿和久坐检测系统。该系统实时跟踪骨关节点的三维坐标，并计算相关关节的角度值。我们招募了36名参与者，建立了包含6种不同坐姿和1种站立姿势的数据集，总计33409个数据点。我们将几种最先进的机器学习算法应用于数据集，并比较了它们在识别坐姿方面的表现。结果表明，基于软投票机制的集成学习模型达到了最高的98.1%。最后，我们基于这个集成模型部署了SitPose系统，以鼓励更好的坐姿，减少久坐的习惯。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice