Wearable robotics have the capacity to assist stroke survivors in assisting and rehabilitating hand function. Many devices that use surface electromyographic (sEMG) for control rely on extrinsic muscle signals, since sEMG sensors are relatively easy to place on the forearm without interfering with hand activity. In this work, we target the intrinsic muscles of the thumb, which are superficial to the skin and thus potentially more accessible via sEMG sensing. However, traditional, rigid electrodes can not be placed on the hand without adding bulk and affecting hand functionality. We thus present a novel sensing sleeve that uses textile electrodes to measure sEMG activity of intrinsic thumb muscles. We evaluate the sleeve's performance on detecting thumb movements and muscle activity during both isolated and isometric muscle contractions of the thumb and fingers. This work highlights the potential of textile-based sensors as a low-cost, lightweight, and non-obtrusive alternative to conventional sEMG sensors for wearable robotics.

Teleoperation can transfer human perception and cognition to a slave robot to cope with some complex tasks, in which the agility and flexibility of the interface play an important role in mapping human intention to the robot. In this paper, we developed an agile large-workspace teleoperation interface by estimating human arm behavior. Using the wearable sensor, namely the inertial measurement unit and surface electromyography armband, we can capture the human arm motion and force information, thereby intuitively controlling the manipulation of the robot. The control principle of our wearable interface includes two parts: (1) the arm incremental kinematics and (2) the grasping recognition. Moreover, we developed a teleoperation framework with a time synchronization mechanism for the real-time application. We conducted experimental comparisons with a versatile haptic device (Omega 7) to verify the effectiveness of our interface and framework. Seven subjects are invited to complete three different tasks: free motion, handover, and pick-and-place action (each task ten times), and the total number of tests is 420. Objectively, we used the task completion time and success rate to compare the performance of the two interfaces quantitatively. In addition, to quantify the operator experience, we used the NASA Task Load Index to assess their subjective feelings. The results showed that the proposed interface achieved a competitive performance with a better operating experience.

The FIFA World Cup in Qatar was discussed extensively in the news and on social media. Due to news reports with allegations of human rights violations, there were calls to boycott it. Wearing a OneLove armband was part of a planned protest activity. Controversy around the armband arose when FIFA threatened to sanction captains who wear it. To understand what topics Twitter users Tweeted about and what the opinion of German Twitter users was towards the OneLove armband, we performed an analysis of German Tweets published during the World Cup using in-context learning with LLMs. We validated the labels on human annotations. We found that Twitter users initially discussed the armband's impact, LGBT rights, and politics; after the ban, the conversation shifted towards politics in sports in general, accompanied by a subtle shift in sentiment towards neutrality. Our evaluation serves as a framework for future research to explore the impact of sports activism and evolving public sentiment. This is especially useful in settings where labeling datasets for specific opinions is unfeasible, such as when events are unfolding.

Hands are used for communicating with the surrounding environment and have a complex structure that enables them to perform various tasks with their multiple degrees of freedom. Hand amputation can prevent a person from performing their daily activities. In that event, finding a suitable, fast, and reliable alternative for the missing limb can affect the lives of people who suffer from such conditions. As the most important use of the hands is to grasp objects, the purpose of this study is to accurately predict gripping force from surface electromyography (sEMG) signals during a pinch-type grip. In that regard, gripping force and sEMG signals are derived from 10 healthy subjects. Results show that for this task, recurrent networks outperform nonrecurrent ones, such as a fully connected multilayer perceptron (MLP) network. Gated recurrent unit (GRU) and long short-term memory (LSTM) networks can predict the gripping force with R-squared values of 0.994 and 0.992, respectively, and a prediction rate of over 1300 predictions per second. The predominant advantage of using such frameworks is that the gripping force can be predicted straight from preprocessed sEMG signals without any form of feature extraction, not to mention the ability to predict future force values using larger prediction horizons adequately. The methods presented in this study can be used in the myoelectric control of prosthetic hands or robotic grippers.

Dubai's transport authority is trialling the use of artificial intelligence to monitor tram drivers. The Roads and Transport Authority on Sunday said the data collected could be used to cut accidents, prevent unsafe driving, show incident hot spots and enhance passenger safety. The system includes a smart device and an armband that tracks drivers' heart rates, speech patterns and reaction times to assess driving style, unsafe patterns and gestures based on profiles. The system includes a smart device and an armband that tracks driver heart rate, speech patterns and reaction times. The RTA said the data collected is then "processed from both incidents and routine operations to provide a comprehensive understanding of the individuals". "Transportation networks and their assets are widely known as critical infrastructure that require attention to detail and special protection," said Hassan Al Mutawa, director of rail operations at the RTA.

A new breakthrough could prove a game changer for users of prosthetic hands by enabling new levels of dexterity. With current myoelectric prosthetic hands, users can only control one grasp function at a time even though modern artificial hands are mechanically capable of individual control of all five digits. A first-of-its-kind study has used haptic/touch sensation feedback, electromyogram (EMG) control and an innovative wearable soft robotic armband. Researchers from Florida Atlantic University's College of Engineering and Computer Science in collaboration with FAU's Charles E. Schmidt College of Science investigated whether people could precisely control the grip forces applied to two different objects grasped simultaneously with a dexterous artificial hand. They also explored the role that visual feedback played in this complex multitasking model by systematically blocking visual and haptic feedback in the experimental design.

A first-of-its-kind study using haptic/touch sensation feedback, electromyogram (EMG) control and an innovative wearable soft robotic armband could just be a game changer for users of prosthetic hands who have long awaited advances in dexterity. Findings from the study could catalyze a paradigm shift in the way current and future artificial hands are controlled by limb-absent people. Researchers from Florida Atlantic University's College of Engineering and Computer Science in collaboration with FAU's Charles E. Schmidt College of Science investigated whether people could precisely control the grip forces applied to two different objects grasped simultaneously with a dexterous artificial hand. For the study, they also explored the role that visual feedback played in this complex multitasking model by systematically blocking visual and haptic feedback in the experimental design. In addition, they studied the potential for time saving in a simultaneous object transportation experiment compared to a one-at-a-time approach.

Alongside the rapid development of vaccines, the FDA has cleared a number of COVID-19 breakthroughs for emergency use as part of the ongoing fight against the devastating virus. So far, we've seen the agency approve medical advances including lab-made monoclonal antibodies for moderate infections that risk turning more severe, a rapid test that uses CRISPR gene-editing tech and Fitbit's Flow ventilator. The latest tool to gain clearance is the first AI-based screening device designed to pinpoint lurking signs of COVID-19 in asymptomatic people. Dubbed the Tiger Tech COVID Plus Monitor, the apparatus is an armband that uses light sensors and a small computer processor to check for biomarkers of the virus, such as hypercoagulation -- a common COVID-19 abnormality that causes the blood to clot more easily. Once strapped to a person's arm, the monitor's onboard sensors start collecting pulse signals from blood flow over a period of three to five minutes.

Force myography has recently gained increasing attention for hand gesture recognition tasks. However, there is a lack of publicly available benchmark data, with most existing studies collecting their own data often with custom hardware and for varying sets of gestures. This limits the ability to compare various algorithms, as well as the possibility for research to be done without first needing to collect data oneself. We contribute to the advancement of this field by making accessible a benchmark dataset collected using a commercially available sensor setup from 20 persons covering 18 unique gestures, in the hope of allowing further comparison of results as well as easier entry into this field of research. We illustrate one use-case for such data, showing how we can improve gesture recognition accuracy by utilising transfer learning to incorporate data from multiple other persons. This also illustrates that the dataset can serve as a benchmark dataset to facilitate research on transfer learning algorithms.

In the latest episode of Future You, check out an armband that lets you control tech devices with your mind. This is not a brain implant or even a headset. It's an armband that reads neuron activity to let you move objects in digital space. Then it goes further, giving you mental control of physical robots too. Think "the Force" from Star Wars.