Although biofeedback is an effective treatment for various physiological problems and can be used to optimize physiological functioning in many ways, the benefits can only be attained through a number of training sessions, and such gains can only be maintained over time through regular practice. Adherence to regular training has been a problem that has plagued the field of physiological self-regulation, limiting its utility. As with any exercise, incorporating biofeedback training with another activity encourages participation and enhances its usefulness.

Brain-machine interfaces (BMIs) are rarely found outside of medical clinics, where the disabled receive hours or days of training in order to operate wheelchairs with their minds. Now the largest-ever BMI experiment Mental Work, conducted as an experimental artwork at EPFL's Artlab, has provided preliminary evidence that training time can be shortened, the use of dry electrodes are a robust solution for public BMI and that user performance tends to improve within a relatively short period of time.

Cuts, scrapes, blisters, burns, splinters, and punctures — there are a number of ways our skin can be broken. Most treatments for skin wounds involve simply placing a barrier over them (usually an adhesive gauze bandage) to keep it moist, limit pain, and reduce exposure to infectious microbes, but do not actively assist in the healing process. A new, scalable approach to speeding up wound healing has been developed based on heat-responsive hydrogels that are mechanically active, stretchy, tough, highly adhesive, and antimicrobial: active adhesive dressings (AADs). Created by researchers at the Wyss Institute for Biologically Inspired Engineering at Harvard University, Harvard's John A. Paulson School for Engineering and Applied Sciences (SEAS), and McGill University, AADs can close wounds significantly faster than other methods and prevent bacterial growth without the need for any additional apparatus or stimuli. The research is reported in Science Advances.

The use of artificial intelligence (AI) based machine learning technologies in autonomous vehicles is on the rise. Helping to drive this trend is the availability of a new class of embedded AI processors. A good example is NVIDIA’s Jetson family, which includes small form factor system on modules (SoMs) that provide GPU-accelerated parallel processing. These high-performance, low-power devices are designed to support the deep learning and computer vision capabilities needed to build software-defined autonomous machines. They derive massive computing capabilities from the use of a parallel processing GPU device with many cores, enabling next-gen computing devices to take on many of the tasks that were historically handled by humans or multiple, traditional computers.