SBIR Phase I: Biosensor device for recordation of handwriting

这个小型企业创新研究（SBIR）第一阶段项目解决了手写活动的瞬时数字化问题，目的是验证手写可以从手部肌肉记录的EMG信号中重建。这项研究将在几个任务领域进行，即在人类受试者的肌电图记录在手写，分析肌电图记录使用模式识别算法提取的手写模式，重建的笔迹，并在计算机上显示的笔迹。期望EMG信号和手写之间将存在一致的相关性，这将允许手写模式的解码和重建的手写的显示。预计还将开发出最有效的模式识别算法，以提供准确的笔迹重建。本研究将主要研究线性回归法与贝氏法两种模式辨识演算法，以解决手写体即时数位化的问题，此方法将克服目前技术所面临的一些限制，并提供一个更耐用、灵活、准确且使用者友善的产品，可以轻易地适应不同的使用者。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职业病。基于EMG的无指手套也可以被不能说话或有听力问题的残疾人用作替代通信设备。由此产生的产品在教育、医学、远程机器人技术方面有许多应用，并且可以由移动的工人使用。作为一种可穿戴的计算机设备，该产品将有助于提高用户的形象和自尊。该研究项目将有助于更好地了解肌肉的相互作用。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测试平台，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中有许多应用。

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.