SBIR Phase I: Biosensor device for recordation of handwriting

这个小型企业创新研究(SBIR)第一阶段项目致力于手写活动的即时数字化问题，目的是验证手写可以从手部肌肉记录的肌电信号中重建笔迹。这项研究将在几个任务领域进行，即人体笔迹过程中的肌电记录，使用模式识别算法分析肌电记录以提取笔迹模式，笔迹重建，以及在计算机上显示笔迹。期望在EMG信号和笔迹之间有一致的相关性，这将允许对笔迹模式进行解码并显示重建的笔迹。还预计将开发最有效的模式识别算法来提供准确的笔迹重建。该研究将主要研究两种模式识别算法：线性回归方法和贝叶斯方法来解决手写活动的瞬时数字化问题，该方法将消除现有技术面临的一些限制，并应提供更耐用、更灵活、更准确、更用户友好的产品，可以方便地适应不同的用户。这项技术将对腕管综合症的病情产生重大影响，据报道，腕管综合症是打字员中的一种常见职业病。基于肌电的无指手套也可以作为不能说话或有听力问题的残疾人的替代通信设备。由此产生的产品在教育、医疗、远程机器人等领域有许多应用，可供移动工作人员使用。作为一款可穿戴电脑设备，这款产品将有助于提升用户形象和自尊。这一研究项目将有助于更好地了解肌肉相互作用。最后，将要开发的手写应用程序可以成为分析和比较各种模式识别算法的试验台，包括传统的统计算法和神经网络，例如时滞递归网络(Tlrn)这些算法已经在各个领域有很多应用。

This Small Business Innovation Research (SBIR) Phase I project addresses the problem of instantaneous digitization of handwriting activity with an objective to verify that the handwriting can be reconstructed from EMG signals recorded from hand muscles. This research will be conducted in several task areas, namely EMG recording in human subjects during handwriting, analysis of EMG records using pattern recognition algorithms to extract the handwriting patterns, reconstruction of handwriting, and displaying the handwriting on a computer. The expectation is that there will be consistent correlation between EMG signals and the handwriting, which will allow the decoding of handwriting patterns and the display of the reconstructed handwriting. It is also expected that the most efficient pattern recognition algorithm to provide accurate handwriting reconstruction will be developed. The proposed research will primarily study two pattern recognition algorithms: linear regression method and Bayesian approach for solving the problem of instantaneous digitizing of handwriting activity.The proposed approach will remove several limitations faced by current technology and should provide a more durable, flexible, accurate, and user friendly product that can be easily adapted to different users. The technology will significantly impact the condition of Carpal Tunnel Syndrome, a common occupational illness being reported among typists. EMG-based fingerless glove can also be used as alternative communication device by disabled people who are not able to talk, or who have hearing problems. The resulting product has many applications in education, medicine, tele-robotics, and can be used by mobile workers. As a wearable computer device, this product will help to improve users image and self esteem. This research project will contribute to the better understanding of muscle interactions. Finally, the handwriting application that will be developed, can become a test bed for analyzing and comparing various pattern recognition algorithms, including traditional statistical algorithms and neural networks, for example Time Lagged Recurrent Networks (TLRN) these algorithms already have numerous applications in various fields.