Low and Medium Frequency Portable Intelligent Electrotherapy Device based on Embedded Integrated Circuit and Deep Learning

Recently, intelligent medical devices have drawn much attention in rehabilitation and chronic disease treatment. This paper focuses on the design and implementation of a portable low- and medium-frequency intelligent electrotherapy device based on embedded integrated circuit technology and deep learning technology. The device applied GBDT and CNN models into processing and analyzing the electrotherapy data in order to enhance its intelligence and usability. In the application of experiments, it treated sports injury rehabilitation and chronic diseases with the device. The GBDT model showed a high-precision prediction ability, which may set the parameters precisely, and the CNN model improved generalization ability by effectively extracting features. The results prove that a deep learning-based data processing approach will greatly improve the performance of the device and bring a good user experience. Future work will include model parameter optimization, extension of application scenarios in clinics, incorporation of wireless communication technology for remote control and data monitoring to further expand the use of intelligent electrotherapy devices in medical and rehabilitation settings.