发表机构
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出认知感知式可穿戴人体工学助手ErgoAssist，结合IMU与EEG技术实现姿势分类和认知负荷估算，可降低提醒频率并提升可用性、任务表现及姿势纠正率。
AI 中文摘要
长期使用数字设备导致不良姿势和肌肉骨骼不适在知识工作者中普遍存在。现有人体工学可穿戴设备仅依赖姿势阈值，常在用户高度专注时发出提醒，引发提醒疲劳和弃用。而姿势与认知负荷密切相关，多数系统缺乏认知感知能力。我们提出ErgoAssist，一款头戴式人体工学助手，通过基于IMU的头部追踪检测不良姿势，利用消费级EEG头带估算任务诱发的认知负荷，适用于日常持续使用。在受控实验室研究中，采用留一被试交叉评估，ErgoAssist的姿势分类准确率达81%，任务诱发认知负荷估算准确率达90.2%。在初步实时部署中，认知感知式提醒使提醒频率降低81%，感知可用性提升43%，任务表现提升25%，姿势纠正率提升38%，提供更少但时机更恰当的干预，而非仅抑制提醒。
英文摘要
Prolonged digital device use has made poor posture and musculoskeletal discomfort pervasive among knowl- edge workers. Existing ergonomic wearables rely solely on posture thresholds, frequently interrupting users during high-focus moments and leading to alert fatigue and abandonment. Yet posture and cognitive load are closely coupled, and most systems remain cognitively unaware. We present ErgoAssist, a head-worn ergonomic assistant that detects poor posture using IMU-based head tracking and estimates task-induced cognitive load using a consumer-grade EEG headband for continuous everyday use. In a controlled lab study, ErgoAssist achieves 81% posture classification and 90.2% task induced cognitive load estimation accuracy under leave-one-subject-out evaluation. In a preliminary real-time deployment, cognition-aware alerting reduces alert frequency by 81%, improves perceived usability by 43%, task performance by 25%, and improves posture correction rate by 38%, delivering fewer but better-timed interventions rather than merely suppressing alerts.
