Gait assessment plays a critical role in the diagnosis and management of neurological disorders. Although AI-based models offer the potential to enhance the objectivity and accessibility of clinical gait analysis, most existing models are limited by their dependence on specific patient populations and sensor configurations, largely due to the scarcity of diverse clinical datasets. In a recent study, researchers from IBM Research, Cleveland Clinic, and University of Tsukuba addressed this limitation by developing a novel framework that integrates synthetic gait data generated by generative AI with physics-based musculoskeletal simulation. This approach supports the development of scalable and generalizable gait analysis models applicable across heterogeneous patient groups and sensor environments.

Water is an essential requirement for life on Earth — it supports everything from cellular processes to ecosystems. Five papers published in ACS journals provide new insights to help protect natural water sources and ensure that more people have access to safe drinking water.

Physicists used a machine-learning method to identify surprising new twists on the non-reciprocal forces governing a many-body system.

The way plastic materials used in electronics are formed may create hidden flaws in their structure that could be limiting their performance.