Integrated Sensor Network Methods and Systems

Abstract

Methods and systems for an integrated sensor network are described. In one embodiment, sensor data may be accessed from a plurality of motion sensors and a bed sensor deployed in a living unit for a first time period. An activity pattern for the first time period may be identified based on at least a portion of sensor data associated with the first time period. The activity pattern may represent a physical and cognitive health condition of a person residing in the living unit. Additional sensor data may be accessed from the motion sensors and the bed sensor deployed for a second time period. A determination of whether a deviation of the activity pattern of the first time period has occurred for the second time period may be performed. An alert may be generated based on a determination that the derivation has occurred. In some embodiments, user feedback is captured on the significance of the alerts, and the alert method is customized based on this feedback. Additional methods and systems are disclosed.

What is claimed is: 
     
         1 . A method comprising:
 accessing health data of a person for a first time period;   accessing sensor data from a plurality of motion sensors and a bed sensor deployed in a living unit for the first time period, the person residing in the living unit;   correlating the health data to at least a portion of the sensor data for the first time period;   accessing additional sensor data from the plurality of motion sensors and the bed sensor deployed in the living unit for a second time period, the second time period occurring after the first time period; and   determining whether a change in a health condition of the person has occurred based on the additional sensor data and correlation of the health data to at least the portion of the sensor data for the first time period.   
     
     
         2 . The method of  claim 1 , further comprising:
 generating an alert when a determination is made that the change in the health condition has occurred.   
     
     
         3 . The method of  claim 1 , wherein the health condition is pulse pressure, pulse pressure being the difference between systolic blood pressure (SBP) and the diastolic blood pressure (DBP). 
     
     
         4 . A method comprising:
 accessing sensor data from a plurality of motion sensors and a bed sensor deployed in a living unit for a time period; and   generating a display based on access of the sensor data associated with the time period.   
     
     
         5 . The method of  claim 4 , wherein the sensor data is grouped on the display based on a plurality of categories, the plurality of categories include motion, pulse, breathing, and restlessness. 
     
     
         6 . The method of  claim 4 , further comprising:
 receiving a selection of a person and a date range,   wherein accessing sensor data from the plurality of motion sensors and the bed sensor for the time period is based on receipt of the selection.   
     
     
         7 . The method of  claim 4 , further comprising:
 receiving a time interval modification request,   wherein generation of the display is based on access of the sensor data associated with the time period and receipt of the time interval modification request.   
     
     
         8 . The method of  claim 4 , further comprising:
 receiving a time increment modification request,   wherein generation of the display is based on access of the sensor data associated with the time period and receipt of the time increment modification request.   
     
     
         9 . The method of  claim 4 , further comprising:
 determining an away-from-home time period for a person associated with the living unit during the time period,   wherein generation of the display is based on access of the sensor data and a determination of the away-from-home time period.   
     
     
         10 . The method of  claim 9 , wherein determining the away-from home time period comprises:
 analyzing the sensor data to determine whether a living unit departure sensor sequence and a living unit return sensor sequence has occurred; and   calculating a time difference between occurrence of the living unit departure sensor sequence and occurrence of the living unit return sensor sequence, wherein the away-from home time period is based on the time difference.   
     
     
         11 . The method of  claim 10 , wherein analyzing the sensor data comprises:
 applying fuzzy logic to at least a portion of the sensor data to determine whether a living unit departure sensor sequence and a living unit return sensor sequence has occurred.   
     
     
         12 . The method of  claim 9 , further comprising:
 computing a number of motion sensor hits for a plurality of hours, a single motion sensor hit being associated with a particular motion sensor of the plurality of motion sensors, the time period including the plurality of hours; and   calculating density for the plurality of hours, the density for a particular hour of the plurality of hours being based on the number of motion sensor hits during the particular hour and the determination of the away-from-home time period,   wherein generation of the display is based on calculation of the density.   
     
     
         13 . The method of  claim 12 , wherein generation of the display comprises:
 selecting a plurality of color mappings, a particular color mapping having a color based on the density and being associated with a position based on the particular hour and a particular day, the time period including a plurality of days,   wherein generation of the display is based on selection of the plurality of color mappings.   
     
     
         14 . A method comprising:
 accessing a first density map and a second density map, the first density map having a plurality of first color mappings, the second density map having a plurality of second color mappings, a particular first color mapping having a color based on density and being associated with a position based on a particular hour and a particular day, the density being based on a number of motion sensor hits during the particular hour and a determination of the away-from-home time period;   computing a dis-similarity between the first density map and the second density map based on a textual feature of the first density map and the second density map; and   generating a computational result based on computing the dis-similarity.   
     
     
         15 . The method of  claim 14 , wherein the textual features include spatial, frequency, and perceptual properties.