Meta发布“意念操控”腕带，研究登Nature，要抢马斯克生意？

智东西2025-07-25

能隔空写字，还能意念操控。作者 |王涵编辑 |漠影智东西7月25日消息，7月23日，Meta现实实验室（Reality Labs）在Nature上发表最新论文《A generic non-invasive neuromotor interface for human-computer interaction》（用于人机交互的通用非侵入式神经运动接口）。这项创新主要推出了基于表面肌电图（sEMG）的...

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href=http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20250725235742a463a688&s=b><strong>智东西</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>能隔空写字，还能意念操控。作者 |王涵编辑 |漠影智东西7月25日消息，7月23日，Meta现实实验室（Reality Labs）在Nature上发表最新论文《A generic non-invasive neuromotor interface for human-computer interaction》（用于人机交互的通用非侵入式神经运动接口）。这项创新主要推出了基于表面肌电图（sEMG）的...</p>\n\n<a href=\"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20250725235742a463a688&s=b\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"LU1267930730.SGD","symbol_name":"富兰克林美国机遇基金AS Acc SGD 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interaction》（用于人机交互的通用非侵入式神经运动接口）。这项创新主要推出了基于表面肌电图（sEMG）的通用型非侵入性神经运动接口。该接口以腕带形式呈现，无需通过侵入性手术，就能够精准捕捉手腕部位的神经信号，识别包括点击、滑动和捏合在内的多种手势意图，甚至在手部自然下垂的状态下也能实现隐蔽操作。与传统的键盘、鼠标、触摸屏等输入设备不同，它摆脱了对中间设备的依赖。并且，相较于基于摄像头或惯性传感器的手势系统，它不受动作遮挡的影响。而与侵入性的脑机接口相比，它也无需定制解码器，且能在不同人群中实现通用化应用。基于300多名受试者提供的超过100小时肌电数据，研究团队还开发出具有高度适应性的机器学习模型。该模型最显著的特点是无需个人校准即可实现高精度手势识别，而仅需少量个性化数据就能将笔迹识别准确率提升16%。Meta在2023年通过Orion AR眼镜原型完成该成果的早期技术验证。01.高灵敏度腕带+通用解码模型为实现这一突破，研究团队从硬件和模型两方面着手。硬件方面：该团队研发了一款高灵敏度、易佩戴的sEMG腕带（sEMG-RD）。该腕带采用干电极、多通道记录设计，采样率达2kHz，噪音低至2.46μVrms，续航超过4小时，且有四种尺寸以适应不同腕围。其电极布局经过优化，能精准捕捉手腕、手部和前臂肌肉的电信号，甚至可检测到单个运动单位动作电位（MUAPs）。在研发过程中，团队经过多次材料测试与结构优化，才确定了最终的腕带设计，以确保其佩戴舒适性与信号采集稳定性的平衡。数据收集与模型训练方面：该团队构建了可扩展的数据收集基础设施，从数千参与者获取训练数据。基于这些数据，团队开发出通用的sEMG解码模型。模型采用了多种深度学习架构，如用于手腕任务的长短期记忆（LSTM）层、用于离散手势任务的1D卷积层加LSTM层，以及用于手写任务的Conformer架构等，以适应不同交互场景的需求。在模型训练阶段，研究人员还运用了迁移学习等先进技术，加速模型收敛，并通过不断调整超参数，提升模型的泛化能力与准确性。02.0.88次/秒手势识别手写输入达20.9字/分钟在连续导航任务中，sEMG的闭环手势解码中位数性能为0.66次/秒；这意味着用户在进行连续的手势操作以控制光标等对象在屏幕上导航时，平均每秒能够实现0.66次精准的目标获取，大大提升了操作效率。在离散手势任务中，其手势检测速率达0.88次/秒，即用户做出如握拳、伸指等离散的特定手势时，系统能够以每秒0.88次的速度快速识别并做出响应。戴sEMG腕带的测试者，手写输入速度可达20.9字/分钟，且通过个性化调整sEMG解码模型，手写模型的解码性能可进一步提升16%。值得一提的是，这些模型在无需针对个人进行训练或校准的情况下，就能在不同人群中表现良好。其在离线评估中，对未参与训练的参与者，手写和手势检测的分类准确率超过90%，手腕角度速度解码误差小于13°s⁻¹。研究团队在不同年龄、性别、身体状况的志愿者群体中进行了广泛测试，均得到了上述稳定且优异的结果，验证了该神经运动接口的通用性与可靠性。03.未来应用：潜力广泛，前景可观这一技术在多个领域都有着广阔的应用前景。日常交互：可应用于智能手机、智能手表、智能眼镜等移动设备，实现无缝输入，尤其适合在移动场景中使用，解决传统输入方式在该场景下的局限性。比如，用户在行走、乘车时，无需再依赖屏幕触摸或键盘输入，仅通过简单的手部肌肉活动，就能完成文字输入、指令下达等操作，让信息交互更加便捷高效。辅助技术：为行动不便者提供新的交互方式，例如那些因肌肉无力或肢体缺失而难以使用传统设备的人群，通过细微的肌肉活动就能实现与计算机的交互。对于肢体残疾人士，他们可以借助该接口，以自身独特的肌肉运动模式控制轮椅、假肢等辅助设备，获得更高的生活自主性。医疗康复：可用于临床诊断和康复治疗，如监测患者的肌肉活动情况，辅助制定个性化康复方案，或作为闭环神经康复范式的一部分。医生能够通过分析患者使用该接口时的肌肉电信号数据，更精准地了解患者的肌肉恢复状态，及时调整康复训练计划，提高康复效果。新型控制方式探索：有望实现对意图手势力量的直接检测，开发多自由度联合控制，以及低做功的控制方式，甚至可能催生出基于神经运动信号空间的全新交互形式。例如，在工业控制领域，工人可以通过佩戴该接口，以更自然的方式远程操控复杂设备，减少操作失误，提高生产效率。此外，该技术还为脑机接口等相关领域提供了借鉴，其大规模数据收集和模型训练方法可能为解决其他接口的校准问题提供方向。04.结语：下一代人机交互范式或将被重新定义Meta在2023年通过Orion 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