Patent ID: 11950900
Assignee: TATA CONSULTANCY SERVICES LIMITED
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A  G

Claim 14:
15. A non-transitory computer readable medium for frailty detection, wherein the non-transitory computer readable medium comprising a plurality of instructions, which when executed, cause:
obtaining, via one or more hardware processors, Passive Infrared (PIR) sensor data and door sensor data, from a plurality of PIR sensors and door sensors respectively, positioned at different locations of a building where a user being monitored is located, as a first input, wherein the PIR sensors are installed at selected locations of the building to track user's movement in and out of each room or selected rooms, wherein the door sensors are installed at main door of the building to track the user's movement whenever the user is going out of the building and coming back to the building;
obtaining, via the one or more hardware processors, pedometer data with respect to movement of the user, using one or more pedometer sensors, as a second input, wherein the first input and the second input are obtained simultaneously,
wherein the pedometer sensors collect information on counts of steps being taken by the user while the user is moving and the pedometer sensors are present in one or more gadgets configured to be attached to the user,
wherein the PIR sensors, the door sensors, the pedometer sensors are configured to communicate with the one or more processors and transmit the information of the counts of steps, and the user's movement to the one or more processors in real-time;
processing the PIR sensor data and the door sensor data in raw form to extract feature data to generate a first feature dataset and further using the first feature dataset to build a room movement data model, a day-time bedroom-stay data model, and an outdoor home movement model, for the user, via the one or more hardware processors, wherein the first feature dataset includes information of room change indicator as Yes or No (Y/N), time difference, weekend/weekday, wherein to track whether the user is moving out of a home, a door location is considered;
wherein the day-time bedroom-stay is determined by querying the first feature dataset to obtain information about “FromLocation”, “ToLocation”, “RoomchangeInd” and “Time Period” features,
wherein the outdoor home movement model provides information on how many times the user goes outside of the home, and outdoor home movement is detected by querying the first feature dataset to get information on the “ToLocation” feature,
processing the pedometer sensor data by extracting feature data, to generate a user activity model for the user, via the one or more hardware processors, and a dataset of the user activity model is termed as a second feature dataset, wherein the user activity model contains time series data on parameters including resident identification (ID), day, week, month, year, weekday/weekend, wherein the user activity model includes information on step count of the user and obtains weekly data of the step counts of the user by querying the second feature dataset with a week number and number of steps in the corresponding week number, wherein while processing the pedometer sensor data, the one or more hardware processors assess signs of frailty based on movement of the user within home through a number of steps taken and the user activity model provide insights on movement of the user in terms of the number of steps taken by the user on weekly, monthly, yearly, day wise, and weekend;
assigning weights to data points deduced from the room movement data model, the day-time bedroom stay data model, the outdoor home movement model, and the user activity model, wherein the data points comprises a total count of reduced movement of the user with the room, a total count of instances in which the user was identified as spending increased amount of time inside the bedroom, a percentage decrease in outdoor movement of the user, and a step count of the user over the period of time, wherein the weights are considered as hyperparameter values that are subject to change based on one or more factors including an assisted living environment, culture of the user, habits of the user being monitored, geography of implementation;
calculating a weighted cumulative percentage value for each of the one or more data points, based on the assigned weights and a measured value of each of the data points;
calculating a cumulative frailty percentage value, based on the weighted cumulative percentage value of the data points;
determining that the user has frailty if the cumulative frailty percentage value exceeds a frailty threshold and the one or more hardware processors interprets that the user is spending less time in moving between the rooms, spending more time in Bedroom during the day time, spending less time in moving out of his home, showing a decrease in number of steps, and the user shows an indication of frailty;
communicating information on the frailty to the user or an authorized person using an interface by sending an alert to the user or the authorized person and the one or more hardware processors storing store information on results of the frailty assessment performed from time to time to compare health condition of the user over time; and
determining a value of change in frailty for the user by conducting multiple surveys at varied dates and the value of the change in frailty greater than zero indicates more frail, the value of the change in frailty less than zero indicates less frail, and the value of the change in frailty equal to zero indicates no change.