SBIR Phase II: Biosensor Device for Recordation of Handwriting

这个小企业创新研究（SBIR）第二阶段项目提出了一个生物传感器设备原型的建设，将产生从手部肌肉记录的肌电图（EMG）信号的文本。该生物传感器装置将使用户能够将文本输入到计算机或移动终端中，而不需要特殊的纸、笔或其他装置来跟踪笔。各种技术的最新进展使得开发适合于字符识别的肌电信号检测和分析技术成为现实。利用电生理学、模式识别、信号处理和计算机工程的进步，本项目提出开发一个实用的系统来破译安装在数字手套中的生物传感器产生的EMG信号。该项目将使用在第一阶段项目期间开发的测试台系统，并帮助证明这一概念。在第一阶段所获得的各种特征的手部肌电模式的知识将用于硬件设备的开发。开发将在数据收集，数据表示（准备）和数据分析领域进行。由于使用了更先进的电极、数据处理滤波器和神经网络算法，预计在所有三个领域都将有所改进。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，可以轻松地适应不同的用户，用于记笔记或为手机写短信。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。作为一种可穿戴计算机设备，该产品将有助于改善用户的形象和自尊。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，例如自组织映射（SOM）、状态向量机（SVM）或时滞递归网络（TLRN）。这些算法已经在各个领域有了大量的应用。

This Small Business Innovation Research (SBIR) Phase II Project proposes the construction of a biosensor device prototype that will produce text from electromyographic (EMG) signals recorded from hand muscles. This biosensor device will enable the user to enter text into a computer or a mobile device without the need of special paper, pen, or other devices to track the pen. Recent advances in various technologies have made it practical to develop the EMG detection and analysis techniques suitable for character recognition. Taking advantage of advances in electrophysiology, pattern recognition, signal processing, and computer engineering, this project proposes to develop a practical system to decipher the EMG signals generated by biosensors mounted in the digital glove. The project will use the test bed system that was developed during Phase I project and helped to prove the concept. The knowledge of hand EMG patterns of various characters that were gained during Phase I will be used in the development of hardware device. The development will be conducted in the areas of Data Collection, Data Representation (preparation), and Data Analysis. The improvements are expected in all three areas, due to the use of more advanced electrodes, data processing filters, and the application of Neural Networks algorithms.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 for taking notes, or writing SMS messages for cell phones. 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 Self Organizing Maps (SOM), State Vector Machine (SVM), or Time Lagged Recurrent Networks (TLRN). These algorithms already have numerous applications in various fields.