Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba 305-8577, Japan.
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
Sensors (Basel). 2022 Jul 17;22(14):5337. doi: 10.3390/s22145337.
We present a solution for intelligent posture training based on accurate, real-time sitting posture monitoring using the LifeChair IoT cushion and supervised machine learning from pressure sensing and user body data. We demonstrate our system's performance in sitting posture and seated stretch recognition tasks with over 98.82% accuracy in recognizing 15 different sitting postures and 97.94% in recognizing six seated stretches. We also show that user BMI divergence significantly affects posture recognition accuracy using machine learning. We validate our method's performance in five different real-world workplace environments and discuss training strategies for the machine learning models. Finally, we propose the first smart posture data-driven stretch recommendation system in alignment with physiotherapy standards.
我们提出了一种基于 LifeChair IoT 坐垫的精确、实时坐姿监测的智能坐姿训练解决方案，以及基于压力感应和用户身体数据的监督机器学习。我们展示了我们的系统在坐姿和坐姿伸展识别任务中的性能，对于 15 种不同的坐姿，识别准确率超过 98.82%，对于 6 种坐姿伸展，识别准确率超过 97.94%。我们还表明，用户 BMI 差异会显著影响基于机器学习的坐姿识别准确性。我们在五个不同的现实工作环境中验证了我们方法的性能，并讨论了机器学习模型的训练策略。最后，我们提出了第一个符合物理治疗标准的智能基于数据驱动的坐姿伸展推荐系统。
