Abstract: A person's fall risk may be determined based on machine learning algorithms. The fall risk information can be used to notify the person and/or a third party monitoring person (e.g. doctor, physical therapist, personal trainer, etc.) of the person's fall risk. This information may be used to monitor and track changes in fall risk that may be impacted by changes in health status, lifestyle behaviors or medical treatment. Furthermore, the fall risk classification may help individuals be more careful on the days they are more at risk for falling. The fall risk may be estimated using machine learning algorithms that process data from load sensors by computing basic and advanced punctuated equilibrium model (PEM) stability metrics.

Abstract: This disclosure describes, generally, methods and systems for monitoring and reporting an entity's carbon footprint. The method may include gathering information about the entity. The information may include utility information. The method may further include equipping the entity with a global positioning system (GPS) device, an accelerometer, and a pressure sensor, and gathering utility usage data for the entity. Further, the method may include capturing travel data for the entity, and calculating the entity's carbon footprint based at least in part on the utility usage data and the travel data. Furthermore, the method may include generating a report of the entity's carbon footprint.

Abstract: Systems and methods for monitoring equilibrium of a user are presented. A stability monitoring device in a shoe insole area utilizes pressure sensors to measure pressure information in real-time. The pressure information is transmitted over an RF network to a device for analyzing the pressure information and calculating postural state information including the current postural state, next postural state, and/or a range of postural stability. The person or a third party may be notified if the postural state information indicates an unstable state. Additionally, the postural state information may be analyzed to determine activity level of the user, diagnostic information, or performance information. Metrics may be displayed by the system for assisting physical therapy or training regimens.