APC: Contactless healthy sitting posture monitoring with microphone array

Q2 Health Professions Smart Health Pub Date : 2024-04-18 DOI:10.1016/j.smhl.2024.100463
Kaiyuan Ma , Shunan Song , Lingling An , Shiwen Mao , Xuyu Wang
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Abstract

The prevalence of poor sitting posture in daily work has become a growing concern among office workers and students due to the associated health problems. To address this issue, we design an acoustic sitting posture care system (termed, APC) based on a circular microphone array. Compared with classic posture recognition technologies such as visual perception and sensors, acoustic sensing naturally possesses advantages such as privacy protection and contactless capabilities. Concretely, our system leverages a customized and inaudible sound signal sent from a speaker to a user’s body, and an echo signal preprocessing method to sense the body posture. Our system comprises three modules: signal generation and collection, signal preprocessing, and posture classification. The signal generation and collection module is designed to create an appropriate signal waveform for transmitting the sound signal. We also develop a unique alignment method for received signals to implement background interference cancellation. In the signal preprocessing module, we propose a body profile extraction method based on the phase difference between received signals. In the posture classification module, we design an attention mechanism based classification network that can map the output of the previous module to different sitting posture categories. The experimental results show that our proposed method achieves an average accuracy of 98.4% for five common sitting postures. Furthermore, case studies conducted under different practical conditions have validated the robustness of our system.

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APC:利用麦克风阵列进行非接触式健康坐姿监测
由于相关的健康问题,日常工作中普遍存在的不良坐姿已成为上班族和学生日益关注的问题。针对这一问题,我们设计了一种基于圆形麦克风阵列的声学坐姿护理系统(简称 APC)。与传统的坐姿识别技术(如视觉感知和传感器)相比,声学传感具有隐私保护和非接触式等天然优势。具体来说,我们的系统利用从扬声器向用户身体发送的定制且不可听的声音信号,以及回声信号预处理方法来感知身体姿态。我们的系统包括三个模块:信号生成与收集、信号预处理和姿势分类。信号生成和收集模块旨在创建合适的信号波形以传输声音信号。我们还开发了一种独特的接收信号对齐方法,以实现背景干扰消除。在信号预处理模块,我们提出了一种基于接收信号相位差的人体轮廓提取方法。在坐姿分类模块中,我们设计了一种基于注意力机制的分类网络,可将前一模块的输出映射到不同的坐姿类别。实验结果表明,我们提出的方法对五种常见坐姿的平均准确率达到 98.4%。此外,在不同实际条件下进行的案例研究也验证了我们系统的鲁棒性。
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来源期刊
Smart Health
Smart Health Computer Science-Computer Science Applications
CiteScore
6.50
自引率
0.00%
发文量
81
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