# US 2019/0167102 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 2019/0167102 A1 - Document ID: US20190167102A1 - Status: Published application - Kind: A1 - Application number: 16/251,478 - Filed: 2019-01-18 - 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/US20190167102A1 ## 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 sensor data from a plurality of motion sensors and a bed sensor deployed in a living unit for a first time period; identifying an activity pattern for the first time period based on at least a portion of sensor data associated with the first time period, the activity pattern of the first time period representing a health condition of a person residing in the living unit; 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; identifying an activity pattern for the second time period based on at least a portion of sensor data associated with the second time period, the activity pattern of the second time period also representing a health condition of the person; calculating statistical parameters for at least a portion of the sensor data; based on the statistical parameters, determining whether a deviation of the activity pattern of the second time period from the activity pattern of the first time period exceeds a statistical parameter threshold; generating an alert based on a determination that the deviation has occurred; receiving a feedback response to the alert, the feedback response including feedback regarding clinical relevance of the alert; and adjusting the statistical parameter threshold based on the feedback response to the alert. ### Claim 2 (depends on 1) The method of claim 1, wherein the activity pattern includes a plurality of features and the deviation is associated with a particular feature of the plurality of features. ### Claim 3 (depends on 1) The method of claim 1, further comprising: recording ignored indicia for the person based on the receipt of the feedback response; and determining whether the deviation has occurred based in part on the recorded ignored indicia. ### Claim 4 (depends on 1) The method of claim 1, further comprising: analyzing health data associated with the person; wherein generation of the alert is based on (1) when the deviation exceeds the statistical parameter threshold and (2) an analysis of the health data. ### Claim 5 (depends on 1) The method of claim 1, further comprising: accessing the sensor data from a stove sensor deployed in the living unit; and accessing the additional sensor data from the stove sensor deployed in the living unit. ### Claim 6 (depends on 1) The method of claim 1, wherein the plurality of motion sensors and the bed sensor are passive, non-wearable sensors. ### Claim 7 (depends on 1) The method of claim 1, further comprising: identifying at least a portion of the sensor data associated with the first time period as being associated with the person; wherein identification of the activity pattern for the first time period is based on at least the portion of sensor data associated with the first time period associated with the person. ### Claim 8 (depends on 1) The method of claim 1, wherein the first time period has a same time duration as the second time period. ### Claim 9 (depends on 1) The method of claim 1, wherein the calculating step comprises calculating the statistical parameters of at least a portion of the sensor data for the first time period; and wherein the statistical parameter threshold is an individualized statistical parameter threshold. ### Claim 10 (depends on 1) The method of claim 1, further comprising: transmitting the alert including a link to a web interface, the web interface including the sensor data of the second time period in the context of the sensor data of the first time period. ### Claim 11 (depends on 1) The method of claim 1, further comprising: adjusting a sensor parameter used for the alert based on the receipt of the feedback response. ### Claim 12 (depends on 1) The method of claim 1, wherein the activity pattern includes a plurality of features. ### Claim 13 (depends on 1) The method of claim 1, wherein the activity patterns of the first and second time periods are based on a total number of sensor hits during the first and second time periods. ### Claim 14 (depends on 13) The method of claim 13 wherein the first time period is longer than the second time period, wherein the activity pattern for the first time period comprises a plurality of first time period data values, wherein each first time period data value corresponds to a subinterval of the first time period and is indicative of a total number of sensor hits for its corresponding subinterval of the first time period, and wherein the calculating step comprises calculating statistics about the first time period data values, the calculated statistics including a statistical measure indicative of an amount of variation in the first time period data values, and wherein the method further comprises: defining the statistical parameter threshold based on the statistical measure. ### Claim 15 (depends on 14) The method of claim 14 wherein the statistical measure comprises a standard deviation for the first time period data values, and wherein the statistical parameter threshold comprises a defined number of the standard deviations. ### Claim 16 (depends on 15) The method of claim 15 wherein the subinterval corresponds to a daily subinterval within the first time period. ### Claim 17 (depends on 15) The method of claim 15 wherein the determining step comprises comparing a data value indicative of a total number of sensor hits for the second time period with the calculated statistics to determine whether the second time period data value exceeds a calculated statistic about the first time period data values by more than the threshold number of the standard deviations, and wherein the generating step comprises generating the alert in response to the second time period data value exceeding the calculated statistic about the first time period data values by more than the threshold number of the standard deviations. ### Claim 18 (depends on 17) The method of claim 17 wherein the second time period is the same as the subinterval. ### Claim 19 (depends on 1) The method of claim 1, wherein the activity patterns of the first and second time periods are based on a total time for sensor firings during the first and second time periods. ### Claim 20 (depends on 19) The method of claim 19 wherein the first time period is longer than the second time period, wherein the activity pattern for the first time period comprises a plurality of first time period data values, wherein each first time period data value corresponds to a subinterval of the first time period and is indicative of a total time for sensor firings for its corresponding subinterval of the first time period, and wherein the calculating step comprises calculating statistics about the first time period data values, the calculated statistics including a statistical measure indicative of an amount of variation in the first time period data values, and wherein the method further comprises: defining the statistical parameter threshold based on the statistical measure. ### Claim 21 (depends on 20) The method of claim 20 wherein the statistical measure comprises a standard deviation for the first time period data values, and wherein the statistical parameter threshold comprises a defined number of the standard deviations. ### Claim 22 (depends on 21) The method of claim 21 wherein the subinterval corresponds to a daily subinterval within the first time period. ### Claim 23 (depends on 21) The method of claim 21 wherein the determining step comprises comparing a data value indicative of a total time for sensor firings for the second time period with the calculated statistics to determine whether the second time period data value exceeds a calculated statistic about the first time period data values by more than the threshold number of the standard deviations, and wherein the generating step comprises generating the alert in response to the second time period data value exceeding the calculated statistic about the first time period data values by more than the threshold number of the standard deviations. ### Claim 24 (depends on 23) The method of claim 23 wherein the second time period is the same as the subinterval. ### Claim 25 (depends on 1) The method of claim 1, wherein the activity patterns of the first and second time periods are based on a density of sensor hits during the first and second time periods. ### Claim 26 (depends on 25) The method of claim 25, wherein the calculating step includes calculating a dis-similarity between the activity pattern densities as between the first and second time periods. ### Claim 27 (depends on 26) The method of claim 26, wherein the dis-similarity calculating comprises calculating the dis-similarity based on at least one member of the group consisting of a spatial frequency and perceptual property of the activity pattern densities. ### Claim 28 (depends on 1) The method of claim 1, wherein the calculated statistical parameter comprises a standard deviation. ### Claim 29 (depends on 1) The method of claim 1 wherein the health condition is representative of a physical and cognitive health condition of the person.