SBIR Phase I: Biosensor device for recordation of handwriting

这个小企业创新研究（SBIR）第一阶段项目解决了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。本研究将在几个任务领域进行，即人类受试者在手写过程中的肌电记录，使用模式识别算法分析肌电记录以提取手写模式，重建手写，并在计算机上显示手写。期望肌电图信号与笔迹之间存在一致的相关性，这将允许对笔迹模式进行解码并显示重建的笔迹。我们也期望能开发出最有效的模式识别算法，以提供准确的手写重建。本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，以解决手写活动的即时数字化问题。所提出的方法将消除当前技术面临的几个限制，并应提供更耐用、灵活、准确和用户友好的产品，可以很容易地适应不同的用户。这项技术将显著影响腕管综合症的病情，这是打字员中常见的一种职业病。基于肌电图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交流设备。由此产生的产品在教育、医学、远程机器人方面有许多应用，并且可以被移动工作者使用。作为一种可穿戴的电脑设备，该产品将有助于提高用户的形象和自尊。这个研究项目将有助于更好地理解肌肉的相互作用。最后，将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测试平台，包括传统的统计算法和神经网络，例如时间滞后递归网络（TLRN），这些算法已经在各个领域有许多应用。

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.