If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
A revolutionary lightweight exosuit that makes walking and running easier has been developed. Scientists say their pioneering design - weighing just five kilos (11 lbs) - could be worn by soldiers, firefighters or rescue workers. They say it could help keep them fresh by lightening the load of their jobs and assist them in negotiating difficult terrain. The portable gear may also improve mobility and quality of life for the elderly and people suffering from neurodegenerative disorders. A revolutionary lightweight exosuit (pictured) that makes walking and running easier has been developed.
Artificial intelligence–connected sensors, tracking wristbands, and data analytics: We've seen this type of tech pop up in smart homes, cars, classrooms, and workplaces. And now, we're seeing these types of networked systems show up in a new frontier--prisons. Specifically, China and Hong Kong have recently announced that their governments are rolling out new artificial intelligence (AI) technology aimed at monitoring inmates in some prisons every minute of every day. In Hong Kong, the government is testing Fitbit-like devices to monitor individuals' locations and activities, including their heart rates, at all times. Some prisons will also start using networked video surveillance systems programmed to identify abnormal behavior, such as self-harm or violence against others.
In chronic pain physical rehabilitation, physiotherapists adapt movement to current performance of patients especially based on the expression of protective behavior, gradually exposing them to feared but harmless and essential everyday movements. As physical rehabilitation moves outside the clinic, physical rehabilitation technology needs to automatically detect such behaviors so as to provide similar personalized support. In this paper, we investigate the use of a Long Short-Term Memory (LSTM) network, which we call Protect-LSTM, to detect events of protective behavior, based on motion capture and electromyography data of healthy people and people with chronic low back pain engaged in five everyday movements. Differently from previous work on the same dataset, we aim to continuously detect protective behavior within a movement rather than overall estimate the presence of such behavior. The Protect-LSTM reaches best average F1 score of 0.815 with leave-one-subject-out (LOSO) validation, using low level features, better than other algorithms. Performances increase for some movements when modelled separately (mean F1 scores: bending=0.77, standing on one leg=0.81, sit-to-stand=0.72, stand-to-sit=0.83, reaching forward=0.67). These results reach excellent level of agreement with the average ratings of physiotherapists. As such, the results show clear potential for in-home technology supported affect-based personalized physical rehabilitation.
As machine-learning algorithms, big data methods and artificial intelligence are increasingly used in the toolkit of U.S. law enforcement agencies, many are worrying that the existing biases of the criminal justice system are simply being automated – and deepened. Police departments are increasingly relying on predictive algorithms to figure out where to deploy their forces by blanketing cities with a mesh of human-based and computerized surveillance technology including, but not limited to, data-mining, facial recognition, and predictive policing programs. This comes despite the flaw in such tools. Facial recognition software have often held a bias toward darker-skinned individuals, including mistaking members of Congress for criminal suspects. In essence, racial profiling has become automated while allowing law enforcement agencies to claim that the computers are race-neutral tools.
Note: This article is based on a transcript of The Dr. Data Show episode, "Five Ways Your Safety Depends on Machine Learning" (click to view). Your safety depends on machine learning. This technology protects you from harm every day by guiding the maintenance of bridges, buildings, and vehicles, and by guiding healthcare providers and law enforcement officers. This puts you in good hands. Hospitals, companies, and the government use machine learning to combat risk, actively protecting you from all sorts of dangers and hazards, including fires, explosions, collapses, crashes, workplace accidents, restaurant E. coli, and crime.
The article proposes a new framework for assessment of physical rehabilitation exercises based on a deep learning approach. The objective of the framework is automated quantification of patient performance in completing prescribed rehabilitation exercises, based on captured whole-body joint trajectories. The main components of the framework are metrics for measuring movement performance, scoring functions for mapping the performance metrics into numerical scores of movement quality, and deep neural network models for regressing quality scores of input movements via supervised learning. Furthermore, an overview of the existing methods for modeling and evaluation of rehabilitation movements is presented, encompassing various distance functions, dimensionality-reduction techniques, and movement models employed for this problem in prior studies. To the best of our knowledge, this is the first work that implements deep neural network for assessment of rehabilitation performance. Multiple deep network architectures are repurposed for the task in hand and are validated on a dataset of rehabilitation exercises.
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. AMBIDEX is a robot arm resulting from collaborative R&D on human-robot coexistence. The arm uses innovative cable-driven mechanisms that make any interaction with humans safe.
Companies that make exoskeleton suits are hoping the devices might soon become as commonly provided as wheelchairs. Private medical insurers and at least three state health providers have agreed to cover the cost of the cyborg-type wearables for people unable to walk. Germany made a landmark decision this year when it registered exoskeletons – normally prohibitively expensive at prices in the tens of thousands of pounds – on its official list of medical aids, providing a degree of obligation on insurers to pay. In Italy, the Israeli-made ReWalk suit was added to a state-run workplace insurance body. The US Department of Veterans Affairs has expanded its policy to include the devices at private rehabilitation clinics for veterans with spinal cord injuries.
This article proposes a method for mathematical modeling of human movements related to patient exercise episodes performed during physical therapy sessions by using artificial neural networks. The generative adversarial network structure is adopted, whereby a discriminative and a generative model are trained concurrently in an adversarial manner. Different network architectures are examined, with the discriminative and generative models structured as deep subnetworks of hidden layers comprised of convolutional or recurrent computational units. The models are validated on a data set of human movements recorded with an optical motion tracker. The results demonstrate an ability of the networks for classification of new instances of motions, and for generation of motion examples that resemble the recorded motion sequences.
We study the fundamental problem of learning an unknown, smooth probability function via point-wise Bernoulli tests. We provide the first scalable algorithm for efficiently solving this problem with rigorous guarantees. In particular, we prove the convergence rate of our posterior update rule to the true probability function in L2-norm. Moreover, we allow the Bernoulli tests to depend on contextual features, and provide a modified inference engine with provable guarantees for this novel setting. Numerical results show that the empirical convergence rates match the theory, and illustrate the superiority of our approach in handling contextual features over the state-of-the-art.