Accurate and rapid detection of gait phases is of utmost importance in achieving optimal performance of powered lower-limb prostheses and exoskeletons. With the increasing versatility and complexity of these robotic systems, there is a growing need to enhance the performance of gait detection algorithms. The development of reliable and functional gait detection algorithms holds the potential to enhance precision, stability, and safety in prosthetic devices and other rehabilitation technologies. In this systematic review, we delve into the extensive body of research and development in the domain of gait event detection methods, with a specific focus on their application to prosthetic devices. Our review critically assesses various proposed methods, aiming to identify the most effective approaches for gait phase classification in lower limb robotic systems. Through a comprehensive comparative analysis, we highlight the strengths and weaknesses of different algorithms, shedding light on their performance characteristics, applicability, and potential for further improvements. This comprehensive review was conducted by screening two databases, namely IEEE and Scopus. The search was limited to 204 papers published from 2010 to 2023. A total of 6 papers that focused on Heuristic, Thresholding, and Amplitude Zero Crossing involved techniques were identified and included in the review. 33.3% of implemented Algorithms used kinematic parameters such as joint angles, joint linear and angular velocity, and joint angular acceleration. This study purely focuses on threshold-based algorithms and thus paper focusing on other gait phase detection methods were excluded.

Abstract: Motivated by recent developments in optical switching and reconfigurable network design, we study dynamic binary search trees (BSTs) in the matching model. In the classical dynamic BST model, the cost of both link traversal and basic reconfiguration (rotation) is O(1). However, in the matching model, the BST is defined by two optical switches (that represent two matchings in an abstract way), and each switch (or matching) reconfiguration cost is α while a link traversal cost is still O(1). In this work, we propose Arithmetic BST (A-BST), a simple dynamic BST algorithm that is based on dynamic Shannon-Fano-Elias coding, and show that A-BST is statically optimal for sequences of length Ω(nαlogα) where n is the number of nodes (keys) in the tree. Abstract: The dynamic optimality conjecture, postulating the existence of an O(1)-competitive online algorithm for binary search trees (BSTs), is among the most fundamental open problems in dynamic data structures.

The term "Artificial Intelligence" is often being misused or misunderstood, but the technology is doing more good than bad. The earlier developments in the field of AI might not be relevant today, but the process has gone through significant changes over the years. Although, AI is considered one of the newest fields of intellectual research, its foundation was set thousands of years ago. But today, AI is on everyone lips and there's a not a single day goes by without hearing about AI. Today, AI is everywhere, from automation to augmentation to beyond, it's already revolutionizing everything.

Despite the proliferation of open source tools like Databricks' AutoML Toolkit, Salesforce's TransfogrifAI, and IBM's Watson Studio AutoAI, tuning machine learning algorithms at scale remains a challenge. Finding the right hyperparameters -- variables in the algorithms that help control the overall model's performance -- often involves time-consuming ancillary tasks like job-scheduling and tracking parameters and their effects. That's why scientists at LG's Advanced AI division developed Auptimizer, an open source hyperparameter optimization framework intended to help with AI model tweaking and bookkeeping. As the team explains in a paper describing their work, Auptimizer simplifies the process of configuring a volume of models with a variety of configurations -- with reproducibility. Like all hyperparameter algorithms, it initializes a search space and configuration before proposing values for hyperparameters, after which it trains the target model and updates the results.

Proponents of artificial intelligence (AI) hail the advances in the ability of machines to make independent decisions based on an analysis on the environment as the next step in machine intelligence – and claim that it will revolutionize complex problem solving across a wide spectrum of human endeavor. The simplest definition of AI is that of an'intelligent' machine that exhibits all the attributes of a flexible, rational agent that perceives its environment and makes decisions – and in many instances takes actions that maximize the chances of success when engaged in a particular task. If one looks at a popular definition, Artificial Intelligence machines mimic human cognitive function. They can learn and solve problems. One of the oldest and most well accepted tests on whether a machine exhibits true AI is the Turing Test. Machine AI can pass the 65-year-old Turing Test if the computer is mistaken for a human more than 30% of the time during a series of five-minute keyboard conversations.

While artificial intelligence promises to create actionable insights for clinicians to make better care decisions for patients, the regulations and standards for evaluating AI-based algorithms are lacking. Writing in the February 22 issue of the journal Science, they make the case that evaluations of AI-based algorithms are not held to traditional clinical trial standards--and, as a result, there has been little prospective evidence that predictive analytics improve patient care. "Several commercial algorithms have received regulatory approval for broad clinical use. But the barrier for entry of new advanced algorithms has been low," charge the authors. "To unlock the potential of advanced analytics while protecting patient safety, regulatory and professional bodies should ensure that advanced algorithms meet accepted standards of clinical benefit, just as they do for clinical therapeutics and predictive biomarkers."

Proponents of artificial intelligence (AI) hail the advances in the ability of machines to make independent decisions based on an analysis on the environment as the next step in machine intelligence – and claim that it will revolutionize complex problem solving across a wide spectrum of human endeavor. The simplest definition of AI is that of an'intelligent' machine that exhibits all the attributes of a flexible, rational agent that perceives its environment and makes decisions – and in many instances takes actions that maximize the chances of success when engaged in a particular task. If one looks at a popular definition, Artificial Intelligence machines mimic human cognitive function. They can learn and solve problems. One of the oldest and most well accepted tests on whether a machine exhibits true AI is the Turing Test. Machine AI can pass the 65-year-old Turing Test if the computer is mistaken for a human more than 30% of the time during a series of five-minute keyboard conversations.

The past years have witnessed strong emergence for different datasets and algorithms repositories. Some inquiries accompanied this emergence. An increasing amount of market research started to investigate which is more important for the development of Artificial Intelligence (AI) sciences, which segments are of highest demand and can have greater market share in the future. By reviewing the artificial intelligence (AI) breakthroughs timeline over 30 years, Wissner-Gross found that the availability of high-quality datasets was the key limiting factor for AI advances and not algorithms. He also found that high-quality dataset availability can cause a breakthrough in the field of AI six times faster than Algorithms.

Having herself held senior roles in IT at Wall Street companies including Deutsche Bank and Morgan Stanley Smith Barney, Oksana Sokolovsky is quite familiar with the challenge of Data Management and data discovery. As co-founder and CEO of ROKITT, her goal was "to build a product that solves that challenge," she says. The challenge exists across large enterprises in multiple industries, but is often especially acute in those dealing with regulatory pressures and compliance requirements – healthcare, for instance, and of course, the financial sector. Basel Committee on Banking Supervision (BCBS) 239 compliance for effective risk data aggregation and reporting, for example, is a big driver of improved Data Management for global systemically important banks. In fact, a McKinsey & Company and Institute of International Finance survey showed that more than half of the world's biggest banks faced significant challenges meeting the January 1, 2016 deadline for compliance, with the Global Association of Risk Professionals commenting that "many institutions continue to struggle to fully implement the requirements across the business under the most demanding interpretation of those requirements."

Today we have some very good AI/ML posts. First, Pete Warden tells us how to break into machine learning. It is some very practical advice on how to get started. David Lettier shows us how to develop K-Means from scratch. Lastly, on An Uncommon Lab we get to see how Kalman Filters work.