This month we continue a series of reprints from the IEEE Global History Network's STARS articles. STARS is an online compendium of invited, peer-reviewed articles on the history of major developments in electrical and computer science and technology. Some light editing has been done, along with the addition of a few illustrations, to make the article more suitable for a journal publication. This article provides a brief history of the artificial cardiac pacemaker, a truly significant technological achievement of engineers, scientists, and doctors that has benefited humanity.

Medical devices and medical electronics are areas that had little to offer 100 years ago. However, there were three important existing technologies that led to many further developments over the following 100 years. These are the stethoscope, electrocardiography, and X-ray medical imaging. Although these technologies had been described and were available to some extent when the Proceedings of the IEEE pages first appeared, they had yet to achieve the widespread use that they have today. The stethoscope is the oldest of these, and it helped physicians to hear sounds of the body and relate them to functioning and malfunctioning organs. The early use of the stethoscope by physicians was more of an art than a science, but as the Proceedings matured, so did this technology. Engineers were able to make this a more quantitative process by graphically displaying the sounds and ultimately using techniques such as voiceprint analysis to assist the physician in diagnosis and monitoring of treatment. The electrocardiograph had been invented a few years prior to the appearance of the Proceedings, but the apparatus was awkward to use, especially for sick people, and was considered more of an oddity than a viable medical technology 100 years ago. Today, it and devices derived from it such as cardiac patient monitors are important parts of our healthcare system. Similarly, X-rays represented a new technology 100 years ago, but unlike electrocardiography physicians immediately saw the value of this technology and quickly adopted it. Many improvements have been made to the basic technology over the last 100 years culminating in computer tomography and complex image processing. Other devices to create high-quality and 3-D medical images have also been developed in recent years to make medical imaging a very important aspect of clinical care today. Looking to the future is always a difficult task, but it is clear that the electronic health record will play an important role in consolidating the information from various medical devices as well as providing readily available data on patients wherever it might be needed. Future medical devices will need to not only address the problems of diagnostic and therapeutic medicine but also be capable of addressing important societal problems such as worldwide disparities in the availability of medical care, continually rising healthcare costs, and healthcare for travel beyond Earth. The next 100 years promises to be even more exciting than the last from the perspective of medical devices and medical electronics.

We propose the use of hidden Markov models (HMMs) to qualify arm gestures. A HMM is trained based on the reference or correct gesture. Then, samples of the gesture that we want to score are used to train a second HMM. Both HMMs are compared, and a measure of their similarity is used to qualify the gesture. We used 3 different metrics to compare HMMs: Levinson, Kullback-Leibler and Porikli. For this, a visual system was developed to track a person's arm, which serves as input to the models that qualify the gestures. We applied this method to qualify the arm movements of stroke patients under rehabilitation. We analyzed three therapeutic gestures: flexion, circular and abduction. A HMM is trained to represent the movement of a healthy person for each gesture, which is compared with the HMMs obtained for each patient. The results are compared with the scales that are used in therapy. From the analysis of several experiments, the Porikli metric was the best to qualify the three gestures, in terms of the motricity index.