Human posture recognition is a challenging task in the medical healthcare industry, when pursuing intelligence, accuracy, security, privacy, and efficiency, etc. Currently, the main posture recognition methods are captured-behaviors-based visual image analysis and wearable devices-based signal analysis. However, these methods suffer from issues such as high misjudgment rate, high-cost and low-efficiency. To address these issues, we propose a collaborative AI-IoT-based solution (namely, WMHPR) that embeds with advanced AI-assisted approach. In WMHPR, we propose the multi-posture recognition (MPR), an offline algorithm is implemented on wearable hardware, to identify posture based on multi-dimensions data. Meanwhile, an AI-based algorithm running on the cloud server (online), named Cascade-AdaBoosting-CART (CACT), is proposed to further enhance the reliability and accuracy of MPR. We recruit 20 volunteers for real-life experiments to evaluate the effectiveness, and the results show our solution is significantly outstanding in terms of accuracy and reliability while comparing with other typical algorithms.

人体姿态识别是医疗保健行业的一项具有挑战性的任务，在追求智能、准确、安全、隐私和效率等方面，目前主要的姿态识别方法是基于捕捉行为的视觉图像分析和基于可穿戴设备的信号分析。然而，这些方法存在误判率高、成本高、效率低等问题。为了解决这些问题，我们提出了一种基于 AI-IoT 的协作解决方案（即 WMHPR），该解决方案嵌入了先进的 AI 辅助方法。在 WMHPR 中，我们提出了多姿势识别 (MPR)，这是一种在可穿戴硬件上实现的离线算法，以基于多维数据识别姿势。同时，在云服务器（在线）上运行的基于人工智能的算法，名为 Cascade-AdaBoosting-CART (CACT)，建议进一步提高 MPR 的可靠性和准确性。我们招募了 20 名志愿者进行实际实验以评估其有效性，结果表明，与其他典型算法相比，我们的解决方案在准确性和可靠性方面非常出色。