Portable Multi-Modal Emergency Situation Anomaly Detection and Response System

A highly portable set of algorithms to detect and analyze multiple signatures associated with emergency or non emergency situations which includes multiple sensors to automatically detect anomalies and emergency situations and automatically communicate the location of the subscriber to the call center and to activate a communication system to allow the call center to communicate with the user and assess the situation with connectivity between the device, the call center and the appropriate response system or emergency service to an individual or set of individuals comprising:

• a) algorithms and models to support a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities onboard the vehicle,
• b) algorithms and models a portable multi-mode signature sensor and processing suite device capable of publishing sensor information onboard the vehicle to a global information grid,
• c) algorithms and models a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities external to the vehicle,
• d) algorithms and models a portable multi-mode signature sensor and processing suite device capable of publishing sensor information and external environmental information to a global information grid,
• e) algorithms and models a global positioning system capable of identification of position and tracking of position,
• f) algorithms and models a two way communication system to a Network Centric Enterprise Service Center
• g) algorithms and models an emergency alert two way communications system

A highly portable set of event models to detect multiple signatures associated with emergency or non emergency situations which includes multiple sensors to automatically detect anomalies and emergency situations and automatically communicate the location of the subscriber to the call center and to activate a communication system to allow the call center to communicate with the user and assess the situation with connectivity between the device, the call center and the appropriate response system or emergency service to an individual or set of individuals comprising:

• a) anomaly and event models to support a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities onboard the vehicle,
• b) anomaly and event models to support a portable multi-mode signature sensor and processing suite device capable of publishing sensor information onboard the vehicle to a global information grid,
• c) anomaly and event models to support a portable multi-mode signature sensor and processing suite device capable of receiving and analyzing abnormalities external to the vehicle,
• d) anomaly and event models to support a portable multi-mode signature sensor and processing suite device capable of publishing sensor information and external environmental information to a global information grid,
• e) anomaly and event models to support a global positioning system capable of identification of position and tracking of position,
• f) anomaly and event models to support a two way communication system to a Network Centric Enterprise Service Center
• g) anomaly and event models to support an emergency alert two way communications system

The device which utilizes sophisticated composite signatures to reduce false positive alerts and alarms. These composite signatures comprise system learning of physics, environmental and behavioral based signatures through a sequence of feed back and feed forward analysis based on rules based templates comprising of operational steps which:

• a) receive and analyze temporal parameters recorded by the onboard system
• b) receive and analyze intensity parameters recorded by the onboard system
• c) receive and analyze frequency parameters recorded by the onboard system
• d) receive and analyze feature, temperature, and pressure parameters recorded by the onboard system and compared to known and trusted data source information
• e) receive and analyze geospatial parameters recorded by the onboard system and compared to known and trusted data source information
• f) combined temporal, intensity, frequency, direction, feature, temperature, pressure, geospatial, space and other sensor data into analytic models to indicate that a personal emergency situation is occurring as apposed to a false positive.

The methodology which utilizes sophisticated composite signatures to reduce false positive alerts and alarms. These composite signatures comprise system learning of physics, environmental and behavioral based signatures through a sequence of feed back and feed forward analysis based on rules based templates comprising of operational steps and personnel which:

• a) receive and analyze temporal parameters recorded by the onboard system
• b) receive and analyze intensity parameters recorded by the onboard system
• c) receive and analyze frequency parameters recorded by the onboard system
• d) receive and analyze feature, temperature, and pressure parameters recorded by the onboard system and compared to known and trusted data source information
• e) receive and analyze geospatial parameters recorded by the onboard system and compared to known and trusted data source information
• f) combined temporal, intensity, frequency, direction, feature, temperature, pressure, geospatial, space and other sensor data into analytic models to indicate that a personal emergency situation is occurring as apposed to a false positive.

The algorithms which utilizes sophisticated composite signatures to reduce false positive alerts and alarms. These composite signatures comprise system learning of physics, environmental and behavioral based signatures through a sequence of feed back and feed forward analysis based on rules based templates comprising of operational steps which:

• a) receive and analyze temporal parameters recorded by the onboard system
• b) receive and analyze intensity parameters recorded by the onboard system
• c) receive and analyze frequency parameters recorded by the onboard system
• d) receive and analyze feature, temperature, and pressure parameters recorded by the onboard system and compared to known and trusted data source information
• e) receive and analyze geospatial parameters recorded by the onboard system and compared to known and trusted data source information
• f) combined temporal, intensity, frequency, direction, feature, temperature, pressure, geospatial, space and other sensor data into analytic models to indicate that a personal emergency situation is occurring as apposed to a false positive.

The processing models which utilizes sophisticated composite signatures to reduce false positive alerts and alarms. These composite signatures comprise system learning of physics, environmental and behavioral based signatures through a sequence of feed back and feed forward analysis based on rules based templates comprising of operational steps which:

• a) receive and analyze temporal parameters recorded by the onboard system
• b) receive and analyze intensity parameters recorded by the onboard system
• c) receive and analyze frequency parameters recorded by the onboard system
• d) receive and analyze feature, temperature, and pressure parameters recorded by the onboard system and compared to known and trusted data source information
• e) receive and analyze geospatial parameters recorded by the onboard system and compared to known and trusted data source information
• f) combined temporal, intensity, frequency, direction, feature, temperature, pressure, geospatial, space and other sensor data into analytic models to indicate that a personal emergency situation is occurring as apposed to a false positive.