Over the past few years, the division of gait phases has emerged as a complex area of research that carries significant importance for various applications in the field of gait technologies. The accurate partitioning of gait phases plays a crucial role in advancing these applications. Researchers have been exploring a range of sensors that can be employed to provide data for algorithms involved in gait phase partitioning. These sensors can be broadly categorized into two types: wearable and non-wearable, each offering unique advantages and capabilities. In our study aimed at examining the current approaches to gait analysis and detection specifically designed for implementation in ambulatory rehabilitation systems, we conducted a comprehensive meta-analysis of existing research studies. Our analysis revealed a diverse range of sensors and sensor combinations that demonstrate the ability to analyze gait patterns in ambulatory settings. These sensor options vary from basic force-based binary switches to more intricate setups incorporating multiple inertial sensors and sophisticated algorithms. The findings highlight the wide spectrum of available technologies and methodologies used in gait analysis for ambulatory applications. To conduct an extensive review, we systematically examined two prominent databases, IEEE and Scopus, with the aim of identifying relevant studies pertaining to gait analysis. The search criteria were limited to 189 papers published between 1999 and 2023. From this pool, we identified and included five papers that specifically focused on various techniques including Thresholding, Quasi-static method, adaptive classifier, and SVM-based approaches. These selected papers provided valuable insights for our review.

The paper presents an algorithm, called Self-Morphing Adaptive Replanning Tree (SMART), that facilitates fast replanning in dynamic environments. SMART performs risk based tree-pruning if the current path is obstructed by nearby moving obstacle(s), resulting in multiple disjoint subtrees. Then, for speedy recovery, it exploits these subtrees and performs informed tree-repair at hot-spots that lie at the intersection of subtrees to find a new path. The performance of SMART is comparatively evaluated with eight existing algorithms through extensive simulations. Two scenarios are considered with: 1) dynamic obstacles and 2) both static and dynamic obstacles. The results show that SMART yields significant improvements in replanning time, success rate and travel time. Finally, the performance of SMART is validated by a real laboratory experiment.

This article compares artificial intelligence with the invention of electricity while talking about its transformative impact because when discovered electricity and when it was widely adopted, it had a radical impact on the global economy and as well as on our day-to-day existence. The widespread adoption of electricity radically transformed every sector of the economy, including agriculture, manufacturing, and services, and it also radically altered virtually every aspect of our existence. In this regard, artificial intelligence is somewhat similar to electricity as far as its transformative impact is concerned. How will A.I. Boost the Country's Growth and Economy? Artificial intelligence is not just a futuristic technology, but it has already embedded itself into our daily lives. Be it the customized recommendations that we get on our phones and websites, or be it GPS-based navigation, or be it a predictive text that completes the sentences when we try to send an e-mail or a message, they're all driven by artificial intelligence-based smart algorithms.

It is not that smart algorithms will one day become too smart, as some fear; not that smart machines will one day overshadow human intellect. Rather the danger is that artificial intelligence (AI) machines are viewed by people as more impartial than they are; that their decisions are more objective than those of people. I have heard wise people speak of AI with reverence, almost as if it were superhuman. They are wrong to do so. This is not to say that AI should be trivialized.

Education systems face a multitude of challenges in today's fast-moving world. Teacher workload is ever-increasing, while delivering personalised lessons to students and fostering their critical thinking skills are crucial but elusive goals. Many people lack access to high-quality learning materials and qualified professors. Fortunately, technologies such as artificial intelligence (AI) can provide schools with much needed assistance, and companies have developed smart algorithms that refine educational experiences in many different ways. Whether through personalised learning and smart content or through transcribing words and improving cognitive performance, AI-driven tools are transforming the way children learn and develop new skills.

Pragmatism from cybersecurity to enterprise imaging was in vogue at the 2019 meeting of the Society of Imaging Informatics in Medicine (SIIM). Not unexpectedly, artificial intelligence accounted for much discussion amid telltale cracks in its hype. Exerting pressure was an undercurrent of practicality, bubbling up in session talks by key opinion leaders (KOLs) and from the exhibit floor, where some company representatives spoke of the continuing need for artificial intelligence (AI) to demonstrate an ROI (return on investment). This doff of the hat to practicality could be seen in the format of scientific sessions, which were kicked off by luminary speakers providing the context in which to understand research data presented in follow-on talks. Other sessions featured faculty, as in the case of one about cybersecurity hosted by J. Anthony Seibert, Ph.D., an imaging physicist on the radiology faculty of the University of California in Davis.

AI has taken its space in all aspects of this digital world. Users become accustomed to experience the benefits of AI. Users get helped well and they are able to get their usual chores like alarm setting, reminders, playing music, etc, done by AI, getting things done simpler and saving the time to a great extent. AI creates expert systems and implements the intelligence of humans into machines. When it comes to AI in web development, it makes the web applications to analyse, observe and learn from the habits and the preferences of the users.

To tackle all of this, Barron's sat down with New Street Research technology infrastructure chief Pierre Ferragu at the investment research boutique's office in Manhattan's Flatiron District. Its open floor plan has a European-design sensibility, yet resembles a small trading operation--a book of Richard Avedon photos makes way for whiteboards covered with remnants of S-curves and math equations as oversize Bloomberg screens flash green and red. Ferragu, 44, joined New Street last year looking for "freedom of thought." He began his career at the Boston Consulting Group, where he advised management teams at media and technology companies; he joined Bernstein in 2008 to make the leap to investment research. But technology is not just a sector in the S&P 500 index--it's also a part of every sector, and Ferragu wanted more freedom to make connections and draw insights from disparate industry groups that can sometimes cause turf wars among analysts.

Ask someone to list life's essentials without which they can't survive, and chances are that their phone will feature near the top. With the increasing use of smartphones, the telecommunication & media industry has become an increasingly critical player in our lives, arguably representing the backbone of digital disruption. Yet service providers still have much to learn. Imagine driving to work and your navigation app prompts you to change routes to avoid a traffic buildup. The source of this information is a combination of artificial intelligence (AI) and machine learning (ML).

Hype and fears surround artificial intelligence taking jobs in healthcare. Will it render physicians obsolete? Will it replace the majority of medical professionals? The Medical Futurist decided to set things straight. Here are five fundamental reasons why A.I. won't replace doctors and never will.