Abstract: A filter processes acceleration magnitude signals from an accelerometer device to output spectral content related to walking and running. A device containing the accelerometer determines steps by qualitatively analyzing the processed acceleration signals to determine whether increased acceleration magnitude results from a step impact from running or walking activity. The device may analyze the acceleration signals to determine crossings of an axis at zero magnitude, which crossings typically correspond to a person's foot impacting the ground, and may analyze the period between the zero crossings. The step count can indicate whether the device, in a height determination mode, is moving in a vehicle; if analysis of accelerometer signals indicates no stepping or running, but another circuit of the device indicates rapid movement, the device assumes it is moving in a vehicle, and resets a height above ground value to zero upon determining resumption of walking or running activity.

Abstract: A filter processes acceleration magnitude signals from an accelerometer device to output spectral content related to walking and running. A device containing the accelerometer determines steps by qualitatively analyzing the processed acceleration signals to determine whether increased acceleration magnitude results from a step impact from running or walking activity. The device may analyze the acceleration signals to determine crossings of an axis at zero magnitude, which crossings typically correspond to a person's foot impacting the ground, and may analyze the period between the zero crossings. The step count can indicate whether the device, in a height determination mode, is moving in a vehicle; if analysis of accelerometer signals indicates no stepping or running, but another circuit of the device indicates rapid movement, the device assumes it is moving in a vehicle, and resets a height above ground value to zero upon determining resumption of walking or running activity.

Abstract: Methods reduce the likelihood of an MPERS device falsely reporting a high acceleration event as a fall impact. The device stores acceleration data acquired before the high acceleration and afterward. If a number of samples of magnitude values from accelerometer sensors in the device acquired during as interval before the high acceleration that approach 0G exceeds a predetermined number, the high acceleration is deemed from a non-fall. Acceleration sensors can also indicate an orientation change before/after the high acceleration, and a barometric pressure sensor can do the same, to further characterize an event as a non-fall. A method compares current event data to composite data sets that have been determined from a plurality of empirically derived data sets of fall and non-fall events. High correlations can indicate falls, or non-falls, respectively. Further statistical analysis of data acquired after an event reduces the likelihood of falsely indicating a fall.

Abstract: A device monitors sensor data generated by movement of a wearer and determines whether the data indicates a fall. The device may include accelerometers, barometer(s), and sensors that detect, light, sound, temperature, magnetic and electric fields, strain-force on the device, and other environmental conditions. A processor determines whether the data meets a first criterion for a parameter (i.e., exceeding an acceleration or barometric pressure maximum threshold). The first criterion corresponds to a first set of known-fall event data sets. If the first criterion is met, the processor generates a full indication. If the data does not meet the first criterion, the processor compares the data to a second criterion for the same, or different, parameter. If the second parameter is met, further processing confirms a fall determination by comparing the data to other criteria corresponding to known-fall event data sets that differ from the first set.

Abstract: A method of generating targeted advertisements includes receiving vehicle information, such as vehicle diagnostics information, from on-board devices installed in vehicles. A processor may then predict, based on the vehicle information, that a given vehicle user will engage in a financial transaction involving their vehicle, such as a purchase of a replacement vehicle or repair, within a predetermined amount of time. This prediction may be used to match a vendor to the given vehicle user, and a targeted advertisement from the matched vendor, the subject matter of which correspond to the predicted financial transaction, may be delivered to the given user.

Abstract: A device may receive information that identifies a first health metric value associated with a user and a first time, and may receive information that identifies a second health metric value associated with the user and a second time that is different from the first time. The device may receive medical record information associated with the user and a third time, where the medical record information is different from the first health metric value and the second health metric value. The device may infer a relationship between the first health metric value, the second health metric value, and the medical record information, based on the first time, the second time, and the third time. The device may provide an indication of the inferred relationship.

Abstract: A filter processes acceleration magnitude signals from an accelerometer device to output spectral content related to walking and running. A device containing the accelerometer determines steps by qualitatively analyzing the processed acceleration signals to determine whether increased acceleration magnitude results from a step impact from running or walking activity. The device may analyze the acceleration signals to determine crossings of an axis at zero magnitude, which crossings typically correspond to a person's foot impacting the ground, and may analyze the period between the zero crossings. The step count can indicate whether the device, in a height determination mode, is moving in a vehicle; if analysis of accelerometer signals indicates no stepping or running, but another circuit of the device indicates rapid movement, the device assumes it is moving in a vehicle, and resets a height above ground value to zero upon determining resumption of walking or running activity.

Abstract: A filter processes acceleration magnitude signals from an accelerometer device to output spectral content related to walking and running. A device containing the accelerometer determines steps by qualitatively analyzing the processed acceleration signals to determine whether increased acceleration magnitude results from a step impact from running or walking activity. The device may analyze the acceleration signals to determine crossings of an axis at zero magnitude, which crossings typically correspond to a person's foot impacting the ground, and may analyze the period, between the zero crossings. The step count can indicate whether the device, in a height determination mode, is moving in a vehicle; if analysis of accelerometer signals indicates no stepping or running, but another circuit of the device indicates rapid movement, the device assumes it is moving in a vehicle, and resets a height above ground value to zero upon determining resumption of walking or running activity.

Abstract: Methods reduce the likelihood of an MPERS device falsely reporting a high acceleration event as a fall impact. The device stores acceleration data acquired before the high acceleration and afterward. If a number of samples of magnitude values from accelerometer sensors in the device acquired during as interval before the high acceleration that approach 0G exceeds a predetermined number, the high acceleration is deemed from a non-fall. Acceleration sensors can also indicate an orientation change before/after the high acceleration, and a barometric pressure sensor can do the same, to further characterize an event as a non-fall. A method compares current event data to composite data sets that have been determined from a plurality of empirically derived data sets of fall and non-fall events. High correlations can indicate falls, or non-falls, respectively. Further statistical analysis of data acquired after an event reduces the likelihood of falsely indicating a fall.

Abstract: A device monitors sensor data generated by movement of a wearer and determines whether the data indicates a fall. The device may include accelerometers, barometer(s), and sensors that detect, light, sound, temperature, magnetic and electric fields, strain-force on the device, and other environmental conditions. A processor determines whether the data meets a first criterion for a parameter (i.e., exceeding, an acceleration or barometric pressure maximum threshold). The first criterion corresponds to a first set of known-fall event data sets. If the first criterion is met, the processor generates a full indication. If the data does not meet the first criterion, the processor compares the data to a second criterion for the same, or different, parameter. If the second parameter is met, further processing confirms a fall determination by comparing the data to other criteria corresponding to known-fall event data sets that differ from the first set.