IEEE Signal Processing Magazine

Smart home technologies, designed to make users happier, healthier, and wealthier, are rapidly becoming a mainstay of everyday life. In most cases, signal processing is essential to the devices' operation and performance. These days, a variety of intelligent automated devices can be found in nearly every home. The trend is accelerating so rapidly that it now appears inevitable that smart technology will soon be integrated into virtually every facet of daily life.

It’s been a while since I last wrote a column for IEEE Signal Processing Magazine. I will try to address here some of the many questions and changes that arose since the beginning of the year. But before I do so, I would like to invite you to watch a short documentary by Ben Proudfoot with the exact title of this column: “She Changed Astronomy Forever. He Won the Nobel Prize for It.”

This issue of IEEE Signal Processing Magazine is mainly focused on neurorehabilitation and assistive technologies. For a few decades, microelectronics, signal processing, robotics, and computer science have been the driver of many scientific and technological advances, with applications in many domains, including health.

This article reviews technologies and algorithms for decoding volitional movement intent using bioelectrical signals recorded from the human body. Such signals include electromyograms, electroencephalograms, electrocorticograms, intracortical recordings, and electroneurograms. After reviewing signal features commonly used for interpreting movement intent, this article describes traditional movement decoders based on Kalman filters (KFs) and machine learning (ML). 

Prostheses provide a means for individuals with amputations to regain some of the lost functions of their amputated limb. Human-machine interfaces (HMIs), used for controlling prosthetic devices, play a critical role in users' experiences with prostheses. This review article provides an overview of the HMIs commonly adopted for upper-limb prosthesis control and inspects collected signals and their processing methods.

During the last few decades, the number of seniors over the age of 60 has increased significantly. A recent study from the United Nations has shown that the number of people aged 65 years or over will increase from 727 million in 2020 to 1.5 billion by 2050. Consequently, the proportion of the global population aged 65 years or over will increase from 9.3% in 2020 to 16% in 2050. 

Signal processing (SP) is at the very heart of our digital lives, owing to its role as the pivotal enabling technology for advancement across multiple disciplines. Its prominence in modern data science has created a necessity to supply industry, government labs, and academia with graduates who possess relevant SP expertise and are well equipped to deal with the manifold challenges in current and future applications.

In this article, we describe and discuss the design-based approach for signal processing education at the undergraduate level at the University of New South Wales (UNSW) Sydney. The electrical engineering (EE) undergraduate curriculum at UNSW Sydney includes three dedicated signal processing courses as well as a design course that involves a major signal processing task.

Signal processing is an engineering discipline known to involve abstract and complex concepts. Curriculum development should be informed by an understanding of the most critical and challenging learning in the field. Threshold concept theory and threshold capability theory provide a framework describing the features of the most critical and challenging learning in any discipline.