# US 2019/0167103 A1 · Integrated Sensor Network Methods and Systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0167103 A1 - Document ID: US20190167103A1 - Status: Published application - Kind: A1 - Application number: 16/254,339 - Filed: 2019-01-22 - Published: 2019-06-06 - Earliest claimed: 2009-06-01 - Assignees: The Curators of the University of Missouri - Applicants: The Curators of the University of Missouri - Inventors: Marjorie Skubic; Marilyn J. Rantz; Mihail Popescu; Shuang Wang; Isaac J. Sledge; Rainer Dane A. Guevara; Elena Florea; James M. Keller - Web page: https://patentgrep.com/patent/US20190167103A1 ## CPC - A61B 5/002: {Monitoring the patient using a local or closed circuit, e.g. in a room or building (A61B5/0017 takes precedence)} - A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet} - A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition - A61B 5/1113: {Local tracking of patients, e.g. in a hospital or private home} - A61B 5/1118: {Determining activity level} - G08B 21/0423: {detecting deviation from an expected pattern of behaviour or schedule} - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16Z 99/00: Subject matter not provided for in other main groups of this subclass - A61B 5/021: Measuring pressure in heart or blood vessels - A61B 5/024: Measuring pulse rate or heart rate - A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 2503/08: Elderly - A61B 2505/07: Home care - A61B 2560/0242: adapted to measure environmental factors, e.g. temperature, pollution - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR ## 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. ## Claims ### Claim 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. ### Claim 2 (depends on 1) The method of claim 1, further comprising: generating an alert when a determination is made that the change in the health condition has occurred. ### Claim 3 (depends on 1) 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). ### Claim 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. ### Claim 5 (depends on 4) 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. ### Claim 6 (depends on 4) 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. ### Claim 7 (depends on 4) 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. ### Claim 8 (depends on 4) 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. ### Claim 9 (depends on 4) 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. ### Claim 10 (depends on 9) 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. ### Claim 11 (depends on 10) 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. ### Claim 12 (depends on 9) 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. ### Claim 13 (depends on 12) 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. ### Claim 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. ### Claim 15 (depends on 14) The method of claim 14, wherein the textual features include spatial, frequency, and perceptual properties.