Sensing and 3D printing technologies in personalized healthcare for the management of health crises including the COVID-19 outbreak.

A major threat that has surrounded human civilization since the beginning of the year 2020 is the outbreak of coronavirus disease 2019 (COVID-19). It has been declared a pandemic by the World Health Organization and significantly affected populations globally, causing medical and economic despair. Healthcare chains across the globe have been under grave stress owing to shortages of medical equipments necessary to address a pandemic. Furthermore, personal protective equipment supplies, mandatory for healthcare staff for treating severely ill patients, have been in short supply. To address the necessary requisites during the pandemic, several researchers, hospitals, and industries collaborated to meet the demand for these medical equipments in an economically viable manner. In this context, 3D printing technologies have provided enormous potential in creating personalized healthcare equipment, including face masks, face shields, rapid detection kits, testing swabs, biosensors, and various ventilator components. This has been made possible by capitalizing on centralized large-scale manufacturing using 3D printing and local distribution of verified and tested computer-aided design files. The primary focus of this study is, "How 3D printing is helpful in developing these equipments, and how it can be helpful in the development and deployment of various sensing and point-of-care-testing (POCTs) devices for the commercialization?" Further, the present study also takes care of patient safety by implementing novel 3D printed health equipment used for COVID-19 patients. Moreover, the study helps identify and highlight the efforts made by various organizations toward the usage of 3D printing technologies, which are helpful in combating the ongoing pandemic.

Integrating Signal Processing Modules of Hearing Aids into a Real-Time Smartphone App.

This paper presents the integration of three major modules of the signal processing pipeline that go into a typical digital hearing aid as a real-time smartphone app. These modules include voice activity detection, noise reduction, and compression. The steps taken to allow the real-time implementation of this integration or signal processing pipeline are discussed. These steps can be utilized to create similar signal processing pipelines or integrated apps to evaluate hearing improvement algorithms. The real-time characteristics of the developed integrated app are reported as well as an objective evaluation of its noise reduction.

A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection.

This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated with convolutional neural networks is addressed. The developed smartphone app is meant to act as a switch for noise reduction in the signal processing pipelines of hearing devices, enabling noise estimation or classification to be conducted in noise-only parts of noisy speech signals. The developed smartphone app is compared with a previously developed voice activity detection app as well as with two highly cited voice activity detection algorithms. The experimental results indicate that the developed app using convolutional neural network outperforms the previously developed smartphone app.