Physically demanding tasks are one of the leading causes of fatigue among workers in labor-intensive industries such as construction. Despite the recent development in physical demand assessment, there is a lack of a practical solution to reduce injuries and illnesses resulted from high-intensity tasks. Consequently, this study proposes a framework to assess the workers' physical demand level using an off-the-shelf respiration sensor. In the proposed framework, the extracted features from respiratory signals in the time and frequency domain are used to train the random forest classifier. Then, the trained model is used to classify the physical demand of the worker in new observations. To evaluate the performance of the proposed framework, an experiment including a masonry wall construction task was designed where the respiratory signals of the 15 participants were recorded. Then, the collected signals were labeled using the NASA-TLX questionnaire. The results showed that the proposed framework increases the accuracy of physical demand classification up to 93.4% while being less sensitive to body and sensor movement artifacts. Moreover, physical demand assessment was performed using a single bio-signal while eliminating the need for monitoring multiple bio-signals simultaneously. The findings of this study should make an important contribution to workers' safety, well-being through the detection of high physical load on workers.

体力劳动是建筑等劳动密集型行业工人疲劳的主要原因之一。尽管最近在体力需求评估方面取得了进展，但仍缺乏切实可行的解决方案来减少高强度任务造成的伤害和疾病。因此，本研究提出了一个框架，使用现成的呼吸传感器评估工人的身体需求水平。在提出的框架中，从时域和频域的呼吸信号中提取的特征用于训练随机森林分类器。然后，训练后的模型用于对新观察中工人的体力需求进行分类。为了评估拟议框架的性能，设计了一项包括砖石墙施工任务的实验，其中记录了 15 名参与者的呼吸信号。然后，使用 NASA-TLX 问卷对收集到的信号进行标记。结果表明，所提出的框架将物理需求分类的准确度提高了 93.4%，同时对身体和传感器运动伪影的敏感度较低。此外，物理需求评估是使用单个生物信号进行的，同时消除了同时监测多个生物信号的需要。这项研究的结果应该通过检测工人的高身体负荷对工人的安全和福祉做出重要贡献。结果表明，所提出的框架将物理需求分类的准确度提高了 93.4%，同时对身体和传感器运动伪影的敏感度较低。此外，物理需求评估是使用单个生物信号进行的，同时消除了同时监测多个生物信号的需要。这项研究的结果应该通过检测工人的高身体负荷对工人的安全和福祉做出重要贡献。结果表明，所提出的框架将物理需求分类的准确度提高了 93.4%，同时对身体和传感器运动伪影的敏感度较低。此外，物理需求评估是使用单个生物信号进行的，同时消除了同时监测多个生物信号的需要。这项研究的结果应该通过检测工人的高身体负荷对工人的安全和福祉做出重要贡献。