SPM Article

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.

The effectiveness of teaching digital signal processing (DSP) can be enhanced by reducing lecture time devoted to theory and increasing emphasis on applications, programming aspects, visualization, and intuitive understanding. An integrated approach to teaching requires instructors to simultaneously teach theory and its applications in storage and processing of audio, speech, and biomedical signals.

Despite the impressive technological strides made over the years, human lives still depend very much on the natural environment. Fortunately, technology can now be used to help address critical environmental concerns in air quality, soil condition, and weather events.

Ten years ago, the world marveled at the ability of social media technology to assist an entire region in its pursuit of democracy. As I write this column days after the U.S. Presidential Inauguration, the world this time is overwhelmingly appalled by the role that same technology played in a violent attempt to overturn democracy. Those who decried the shutdown of access to social media desperately implemented by authoritarian regimes applauded similar restrictions implemented by tech companies in a quest to forestall additional violence.

This past summer, Prof. Robert Heath Jr. IEEE Signal Processing Magazine’s (SPM’s) former editor-in-chief, stressed to me how important it is to include a strong team of scientists on the magazine’s editorial board. It is especially important for area editors and members of the senior editorial board, but also associate editors for columns and forums as well as the e-Newsletter.