Paleari Marco, Luciani Riccardo, Ariano Paolo
IEEE Int Conf Rehabil Robot. 2017 Jul;2017:1506-1511. doi: 10.1109/ICORR.2017.8009461.
This work reports on preliminary results about on hand movement recognition with Near InfraRed Spectroscopy (NIRS) and surface ElectroMyoGraphy (sEMG). Either basing on physical contact (touchscreens, data-gloves, etc.), vision techniques (Microsoft Kinect, Sony PlayStation Move, etc.), or other modalities, hand movement recognition is a pervasive function in today environment and it is at the base of many gaming, social, and medical applications. Albeit, in recent years, the use of muscle information extracted by sEMG has spread out from the medical applications to contaminate the consumer world, this technique still falls short when dealing with movements of the hand. We tested NIRS as a technique to get another point of view on the muscle phenomena and proved that, within a specific movements selection, NIRS can be used to recognize movements and return information regarding muscles at different depths. Furthermore, we propose here three different multimodal movement recognition approaches and compare their performances.
这项工作报告了关于利用近红外光谱（NIRS）和表面肌电图（sEMG）进行手部运动识别的初步结果。无论是基于物理接触（触摸屏、数据手套等）、视觉技术（微软Kinect、索尼PlayStation Move等）还是其他模式，手部运动识别在当今环境中都是一项普遍功能，并且是许多游戏、社交和医疗应用的基础。尽管近年来，通过sEMG提取的肌肉信息的使用已从医疗应用扩展到消费领域，但在处理手部运动时，这项技术仍然存在不足。我们测试了NIRS作为一种获取肌肉现象另一种视角的技术，并证明在特定的运动选择范围内，NIRS可用于识别运动并返回不同深度肌肉的相关信息。此外，我们在此提出三种不同的多模态运动识别方法并比较它们的性能。
