Kappattanavar Arpita Mallikarjuna, Steckhan Nico, Sachs Jan Philipp, Freitas da Cruz Harry, Böttinger Erwin, Arnrich Bert
Hasso-Plattner-Institut, University of Potsdam, Potsdam, Germany.
Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
JMIR Biomed Eng. 2021 Mar 1;6(1):e21105. doi: 10.2196/21105.
A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain.
This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements, using sensors and wearables such as inertial measurement units, pressure or piezoresistive sensors, accelerometers or gyroscopes, combined with machine learning approaches.
A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases in the shortlisted papers.
A total of 14 papers were shortlisted from 952 papers obtained after a systematic search. The majority of the studies used pressure sensors to measure sitting postures, whereas neural networks were the most frequently used approaches for classification tasks in this context. Only 2 studies were performed in a free-living environment. Most studies presented ethical and methodological shortcomings. Moreover, the findings indicate that the strategic placement of sensors can lead to better performance and lower costs.
The included studies differed in various aspects of design and analysis. The majority of studies were rated as medium quality according to our assessment. Our study suggests that future work for posture classification can benefit from using inertial measurement unit sensors, since they make it possible to differentiate among spine movements and similar postures, considering transitional movements between postures, and using three-dimensional cameras to annotate the data for ground truth. Finally, comparing such studies is challenging, as there are no standard definitions of sitting postures that could be used for classification. In addition, this study identifies five basic sitting postures along with different combinations of limb and spine movements to help guide future research efforts.
在工业领域，大多数员工在工作时大部分时间都处于坐姿。监测坐姿有助于深入了解诸如腰痛等职业不适的潜在原因。
本研究聚焦于利用传感器和可穿戴设备（如惯性测量单元、压力或压阻传感器、加速度计或陀螺仪），结合机器学习方法，对椅子上的坐姿进行分类，具体涉及脊柱和肢体运动方面的技术与算法。
共检索了三个电子文献数据库，以确定有关成年人坐姿分类的研究。进行质量评估以提取关键细节并评估入围论文中的偏差。
在系统检索后获得的952篇论文中，共筛选出14篇入围论文。大多数研究使用压力传感器来测量坐姿，而神经网络是在此背景下分类任务中最常用的方法。只有2项研究是在自由生活环境中进行的。大多数研究存在伦理和方法上的缺陷。此外，研究结果表明传感器的合理布局可带来更好的性能和更低的成本。
纳入的研究在设计和分析方面存在差异。根据我们的评估，大多数研究被评为中等质量。我们的研究表明，未来的姿势分类工作可受益于使用惯性测量单元传感器，因为它们能够区分脊柱运动和相似姿势，考虑姿势之间的过渡运动，并使用三维相机为地面真值标注数据。最后，由于没有可用于分类的坐姿标准定义，比较此类研究具有挑战性。此外，本研究确定了五种基本坐姿以及肢体和脊柱运动的不同组合，以帮助指导未来的研究工作。
