Abstract: A system, method, and apparatus is provided for estimating a risk of falls from pressure sensor data and inertial sensor data. A classifier function may be generated to relate the inertial sensor and the pressure sensor data with the falls risk (e.g., falls risk or prospective falls data) of the person who generated the pressure sensor data and inertial sensor data. The classifier function may be used to predict a person's risk of falls based on inputs of pressure sensor data and inertial sensor data. Separate classifier functions may be generated for men and women, or separate classifier functions may be generated for patients who closed their eyes while data was being collected and for patients who opened their eyes while data was being collected.

Abstract: Methods and systems may provide for cognitive decline prediction or assessment. Baseline and follow-up inertial sensor data may be received from one or more inertial sensors attached to a person. Baseline and follow-up data indicative of cognitive decline may be received from the person. An indication of cognitive decline may be determined based on the baseline and follow-up cognitive decline data. A classifier function for predicting cognitive decline may be trained with the baseline inertial sensor data and the indication of cognitive decline. A classifier function for assessing cognitive decline may be trained with the baseline inertial sensor data, a difference between the baseline inertial sensor data and follow-up inertial sensor data, and the indication of cognitive decline.

Abstract: A system, method, and apparatus is provided for estimating a risk of falls from pressure sensor data and inertial sensor data. A classifier function may be generated to relate the inertial sensor and the pressure sensor data with the falls risk (e.g., falls risk or prospective falls data) of the person who generated the pressure sensor data and inertial sensor data. The classifier function may be used to predict a person's risk of falls based on inputs of pressure sensor data and inertial sensor data. Separate classifier functions may be generated for men and women, or separate classifier functions may be generated for patients who closed their eyes while data was being collected and for patients who opened their eyes while data was being collected.

Abstract: Methods and systems may provide for cognitive decline prediction or assessment. Baseline and follow-up inertial sensor data may be received from one or more inertial sensors attached to a person. Baseline and follow-up data indicative of cognitive decline may be received from the person. An indication of cognitive decline may be determined based on the baseline and follow-up cognitive decline data. A classifier function for predicting cognitive decline may be trained with the baseline inertial sensor data and the indication of cognitive decline. A classifier function for assessing cognitive decline may be trained with the baseline inertial sensor data, a difference between the baseline inertial sensor data and follow-up inertial sensor data, and the indication of cognitive decline.

Abstract: A system, method, and apparatus is provided for calculating a risk of falls from pressure measurements. Pressure data may be measured by a pressure sensor matrix, such as a pressure mat. Multiple snapshots of pressure data may be generated, each with a plurality of pressure points corresponding to coordinates in the pressure sensor matrix. Pressure-based metrics, including center of mass and center of pressure, may be derived from the pressure measurements. The plurality of centers of pressure and other pressure-related parameters may be used to generate a statistical model that predicts the risk of falls.

Abstract: A system for selectively applying a service to a packet in a network. In a specific embodiment, the system includes a mechanism for encoding service information in a network-compatible packet header and providing encoded data in response thereto. In a more specific embodiment, the network-compatible header includes a Multi-Protocol Label Switching (MPLS) header, a Generic Route Encapsulation (GRE) header, and/or a Layer-2 Tunneling Protocol (L2TP) header.