AI 中文总结
本文提出一种基于5×2阵列机械接触开关的二元接触感知系统，无需压力传感器，通过决策树等分类器实现四种坐姿识别，准确率达96%，可低成本监测坐姿。
AI 中文摘要
长时间保持不良坐姿会导致肌肉骨骼损伤。监测坐姿的早期干预与预防方法依赖于摄像头、可穿戴设备和密集压力阵列，尽管有效，但这些方法存在隐私问题、校准需求、成本较高等缺陷。本文探索身体接触的空间模式，将其作为二元姿态特征向量，构建无需模拟信号调理与校准的接触式坐姿识别系统。该感知原理采用10个机械接触开关，以5×2阵列布置于靠背，每个开关将局部身体接触编码为10位二元姿态特征，对应四种姿态：正常坐姿、后仰、左倾、右倾。决策树与逻辑回归分类器达到最高96%的准确率。开关间低相关性表明，每个开关捕获互补信息以实现成功的姿态分类。SHAP分析确定中心触点是姿态区分最具信息性和显著性的区域。本文结果显示，二元接触模式可捕获足够的空间信息用于可靠的姿态识别，该感知策略提供了一种简单低成本的替代方案，无需映射生物力学压力分布即可监测姿态。
英文摘要
Prolonged sitting with poor posture results in musculoskeletal injury. Early intervention and prevention methods to monitor posture rely on cameras, wearable devices, and dense pressure arrays. Although effective, these approaches introduce privacy concerns, calibration needs, higher cost, etc. In this paper, we explore the spatial pattern of body contact as a binary posture feature vector for a distinct contact-based sitting posture recognition system without the need for analog signal conditioning and calibration. Our sensing principle uses 10 mechanical contact switches arranged in a 5 x 2 array on the backrest. Each switch encodes local body contact into 10-bit binary posture signatures for four postures: normal sitting, leaning back, leaning left, and leaning right. Decision tree and logistic regression classifiers achieved highest accuracy of 96%. Low correlation amongst the switches indicates that every switch captures complimentary information for successful posture classification. SHAP analysis identified the central contacts as the most informative and significant region for posture discrimination. Our results show that binary contact patterns can capture sufficient spatial information for reliable posture recognition. This sensing strategy offers a simple & low-cost alternative to pressure-based systems to monitor posture without the need for mapping the biomechanical pressure distribution.
Comments6 pages, 7 figures
