Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, the operating status of the features, the identity of a vehicle operator, risk levels for operation of the vehicle by the vehicle operator, or damage to the vehicle may be determined based upon sensor or other data. According to further aspects, decisions regarding transferring control between the features and the vehicle operator may be made based upon sensor data and information regarding the vehicle operator. Additional aspects may recommend or install updates to the autonomous operation features based upon determined risk levels. Some aspects may include monitoring transportation infrastructure and communicating information about the infrastructure to vehicles.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having autonomous or semi-autonomous operation features are provided. According to certain aspects, vehicle operation safety may be enhanced. An environmental or weather condition (e.g., hail, storm, wind) may be identified that presents a hazard to an autonomous or semi-autonomous vehicle. With the customer's permission, when it is determined that the vehicle is parked in an unprotected location, a protected or covered location to park the vehicle may be identified, a route to that location may be determined, and the vehicle may be directed to travel automatically to the protected location under the operation of autonomous operation features. Insurance discounts or cost savings may be provided to risk averse insurance customers based upon the self-parking functionality that will reduce or mitigate damage to insured vehicles caused by adverse conditions, falling trees or power lines, hail, etc.

A biological function measurement and analysis system, a biological function measurement and analysis method, and a recording medium storing program code causing a computer to execute the biological function measurement and analysis method. The biological function measurement and analysis system includes an input acceptance unit configured to accept selection of a biological function to be measured, and a measurement and analysis procedure determining unit configured to access a memory in which measurement and analysis procedure data, indicating a combination of measurement and analysis of reaction of a live subject caused by stimulation on a biological-function-by-biological-function basis, is stored to specify measurement and analysis procedure data corresponding to the selected biological function. The biological function measurement and analysis method includes accepting the selection of biological function to be measured, accessing the memory, and specifying the measurement and analysis procedure data corresponding to the selected biological function.

An apparatus, method, and machine-readable medium for health monitoring and response are described herein. The apparatus includes a processor and a number of sensors configured to collect data corresponding to a user of the device. The apparatus also includes a health monitoring and response application, at least partially including hardware logic. The hardware logic of the health monitoring and response application is to test the data collected by any of the sensors to match the collected data with a predetermined health condition, determine a current health condition of the user based on the predetermined health condition that matches the collected data, and automatically perform an action based on the current health condition of the user.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, the operating status of the features, the identity of a vehicle operator, risk levels for operation of the vehicle by the vehicle operator, or damage to the vehicle may be determined based upon sensor or other data. According to further aspects, decisions regarding transferring control between the features and the vehicle operator may be made based upon sensor data and information regarding the vehicle operator. Additional aspects may recommend or install updates to the autonomous operation features based upon determined risk levels. Some aspects may include monitoring transportation infrastructure and communicating information about the infrastructure to vehicles.

According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driverthe autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated. Insurance discounts may be provided for autonomous vehicles having this safety functionality.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having autonomous or semi-autonomous operation features are provided. In certain aspects, with the customer's permission, a computer-implemented method for updating an autonomous operation feature may be provided. An indication of a software update associated with the autonomous operation feature may be received, and several autonomous or semi-autonomous vehicles having the feature may be identified. The update may be installed within the several vehicles, such as via wireless communication. Also, a change in a risk level associated with the update to the autonomous operation feature may be determined, and an insurance discount may be determined or adjusted. As a result, an insurance discount may be provided to risk averse customers that affirmatively share their vehicle data with an insurance provider, and promptly and remotely receive new versions of software that operate autonomous vehicle safety features.

A method for determining and responding in real-time to an increased risk of death relating to a patient with epilepsy is provided. The method includes receiving cardiac data and determining a cardiac index based upon the cardiac data. The method includes determining an increased risk of death associated with epilepsy if the indices are extreme, issuing a warning of the increased risk of death and logging information related to the increased risk of death. Also presented is a second method for determining and responding in real-time to an increased risk of death relating to a patient with epilepsy comprising receiving at least one of arousal data, responsiveness data or awareness data and determining an arousal index, a responsiveness index or an awareness index, where the indices are based on arousal data, responsiveness data or awareness data respectively. The second method includes determining an increased risk of death related to epilepsy if indices are extreme values, issuing a warning of the increased risk of death and logging information related to the increased risk of death. A computer readable program storage device is also provided. Also provided is a method for receiving body data, determining a cardiac, an arousal, a responsiveness, or a kinetic index, determining an increased or increasing risk of death over a first time window relating to a patient with epilepsy and issuing a warning and logging relevant information.

A behavioral state of a brain is classified by automatically selecting one or more sensors based on the signals received from each sensor and one or more selection criteria using one or more processors, calculating at least one measured value from the signal(s) of the selected sensor(s), classifying the behavioral state as: (a) an awake state whenever the measured value(s) for the selected sensor(s) is lower than a first threshold value, (b) a sleep state (N2) whenever the measured value(s) for the selected sensor(s) is equal to or greater than the first threshold value and the measured value(s) is not greater than a second threshold value, or (c) a slow wave sleep state (N3) whenever the measured value(s) from the selected sensor(s) is greater than the first threshold value and the measured value(s) is greater than the second threshold value, and providing a notification of the classified behavioral state.

Techniques are described determining electrodes that are proximate or distal to location of an oscillatory signal source in a patient based on current source densities (CSDs). Processing circuitry may determine, for one or more electrodes of a plurality of electrodes, respective time-varying measurements of CSDs, aggregate, for the one or more electrodes of the plurality electrodes, the respective time-varying measurements of the CSDs to generate respective average level values for the one or more electrodes of the plurality of electrodes, determine, for one or more electrodes of the plurality of electrodes, respective phase-magnitude representations of the time-varying measurements of the CSDs. The respective phase-magnitude representations are indicative of respective magnitudes and phases of a particular frequency component of respective time-varying measurements of the CSDs. The particular frequency component is a frequency component having a largest transform coefficient in a time-varying measurement of a CSD having a largest average level value.