Remote monitoring systems and methods for elderly and patient in-home and senior living facilities care

Abstract

The present application relates generally to a monitoring system to assist with aging-in-place for elderly individuals and patients with chronic diseases either living at home, senior living or assisted living facilities. In one aspect, integrating the use machine learning and signals from an interoperable system of electricity usage, water usage, ballistocardiography (BGC), and ultra-wideband radar, WiFi based computer visioning, infrared. The system and methods may be used to identify and track common daily human activities, instrumental daily living activities, the patient or elderly personal physical status in the home or at senior and assisted living facilities. Anomalies to patterns can be determined by identifying disruptions in previously established patterns.

1 . A system for labeling daily living activities for a patient comprising:
 at least one processor;   and at least one memory including computer program code;   the at least one memory and the computer program code configured to, with the at least one processor; cause the system at least to:   receive at least one measurement from at least one sensor;   determine an activity of the patient based on the received at least one measurement; and   labeling an activity of the patient or elderly based on the determined behavior.   
     
     
         2 . The system of  claim 1 , wherein the predicted activity includes at least one of the activities of daily living:
 Ambulating;   Eating;   Bathing;   Dressing;   Toileting;   Transferring;   Continence;   Activities outside home;   Cooking;   Presence in kitchen;   Household Chores;   Taking medications;   Social Communications;   Banking;   Sleeping;   Lying in bed.   
     
     
         3 . The system of  claim 1 , wherein the determined activity includes one or more of:
 respiratory rate;   heart rate;   toilet flushes;   paroxysmal torso motion stemming from coughing;   use of a medical device;   night-time walking;   sleep angle;   sleep stages;   gait speed;   bed/chair-to-standing time;   stair ascent/descent time;   amount/speed of locomotion;   cooking;   eating;   bathing/showering;   personal hygiene;   household chores; and   home leaving regularity.   
     
     
         4 . The system of  claim 1 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to determine the activity of the patient further based on: (i) ground truth observations, and (ii) training data from a specific home of the patient or elderly and/or from a population of patients. 
     
     
         5 . The system of  claim 1 , wherein the activity includes a time the patient or elderly spends performing the activity. 
     
     
         6 . The system of  claim 1 , further including:
 an audio alarm;   wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the audio alarm to emit an audible alarm if the trends in activities moves outside a predefined time period or threshold.   
     
     
         7 . The system of  claim 1 , further including:
 a visual alarm;   wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the visual alarm to emit a visual alarm if the trends in activities moves outside a predefined time period or threshold.   
     
     
         8 . The system of  claim 1 , wherein the at least one sensor includes:
 a first electrical measurement device configured to measure an overall power usage of a home of the patient or elderly over time;   a second electrical measurement device configured to measure a power usage of a kitchen of the home over time;   a water sensor configured to monitor water usage of the home over time;   a water sensor configured to monitor water usage in a specific bathroom over time;   a sleeping sensor configured to measure sleep of the patient or elderly;   a vital sign sensor configured to measure a vital sign of the patient or the elderly;   a mobility sensor configured to measure mobility of the patient or elderly; and   a mobility sensor configured to measure gait, sit and stand up movements of the patient of elderly.   
     
     
         9 . The system of  claim 1 , wherein the at least one memory and the computer program code are further configured to; with the at least one processor, cause the system to:
 generate an amber alert if the score exceeds a first predetermined threshold; and   generate a red alert if the score exceeds a second predetermined threshold.   
     
     
         10 . The system of  claim 1 , further including:
 a water sensor configured to measure toileting of the patient; and   wherein:   the activity is excessive or scant toileting; and   the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to label toileting activity based on the water sensor and signaling a toileting activity of the patient or elderly.   
     
     
         11 . The system of  claim 1 , wherein the labeled activity is bathing, and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to labeling the bathing activity anomaly activity based on water usage of the patient or elderly and generating alerts when trends deviate from a set of pre-defined thresholds. 
     
     
         12 . The system of  claim 1 , wherein the labeled activity is eating and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to label the eating activity anomaly based on electricity and water usage of the patient or elderly over a period of time and deviates from a pre-defined threshold or based on movement in space of the patient or elderly over time. 
     
     
         13 . The system of  claim 1 , wherein the labeled activity is cooking and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to label the cooking activity anomaly based on electricity and water usage of the patient or movement in space of the patient or elderly. 
     
     
         14 . The system of  claim 1 , wherein the labeled activity is continence and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to label the continence activity anomaly based on water usage and bed behavioral activities of the patient or elderly. 
     
     
         15 . The system of  claim 1 , wherein the labeled activity is dressing and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to label the dressing activity anomaly based on mobility sensors and electricity usage of the patient or elderly. 
     
     
         16 . The system of  claim 1 , wherein the labeled activity is transferring and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to predict the transferring activity anomaly based on electricity and mobility sensors of the patient or elderly. 
     
     
         17 . The system of  claim 1 , wherein the labeled activity comprises one or more activities away from the home and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to predict the shopping activity or non-activity based on water, electricity and mobility sensors located at the patient or elderly place. 
     
     
         18 . The system of  claim 1 , wherein the labeled activity is household chores and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to predict the household chores activities or non-activity based on electricity, water and mobility sensors located at the patient or elderly place. 
     
     
         19 . The system of  claim 1 , wherein the labeled activity is medication adherence and the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to label the medication adherence activities or non-activities as well as the side effects of medication based on electricity, water and mobility and sleep sensors located at the patient or elderly place. 
     
     
         20 . A system for determining an activity of a patient comprising:
 at least one processor;   and at least one memory including computer program code;   the at least one memory and the computer program code configured to, with the at least one processor, cause the system to:   receive at least one measurement from at least one sensor; and   determine the activity of the patient based on at least one of: (i) rule-based heuristics, (ii) training data from a home of one or more patients, and (iii) the received at least one measurement from the at least one sensor;   wherein the activity includes at least one of:   a common activity of daily living and common instrumental activity of daily living;   
     
     
         21 . The system of  claim 20 , wherein the at least one sensor includes a ballistocardiography (BCG) measurement device. 
     
     
         22 . The system of  claim 20 , wherein the at least one sensor includes at least one of an ultra-wideband (UWB) radar, WiFi computer aided visioning, Infrared sensors and the at least one measurement includes a presence measurement. 
     
     
         23 . The system of  claim 20 , wherein the at least one sensor includes an ultra-wideband (UWB) radar, and the at least one measurement includes a respiratory rate measurement. 
     
     
         24 . The system of  claim 20 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to determine the activity of the patient further based on ground truth observations. 
     
     
         25 . The system of  claim 20 , wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to determine the activity of the patient using machine learning classification algorithms. 
     
     
         26 . The system of  claim 20 , wherein the training data comprises data acquired within a two-week time period. 
     
     
         27 . The system of  claim 20 , wherein the heuristics and/or classification machine learning algorithms are adjusted based on data aggregated from the homes of other monitored individuals. 
     
     
         28 . The system of  claim 20 , further comprising a dashboard, and wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to:
 display an avatar of the patient on the dashboard; and   display a three-alarm system on the dashboard;   The data visualization dashboard is available on multiple platforms.   
     
     
         29 . The system of  claim 20 , further comprising a dashboard configured to connect to end user health record systems and into mobile applications. 
     
     
         30 . The system of  claim 20 , further comprising a hub configured for edge processing. 
     
     
         31 . The system of  claim 20 , wherein:
 the at least one sensor includes an electrical sensor, and the at least one measurement includes an electrical measurement from the at least one electrical sensor;   the system further includes a load identifier configured to receive electrical measurement and disaggregate the electrical measurement;   the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to determine the activity based on the disaggregated electrical measurement.   
     
     
         32 . The system of  claim 20 , further comprising a water sensor configured to measure water usage of the home;
 the at least one memory and the computer program code are further configured to, with the at least one processor, cause the system to determine the activity based on the measured water usage.   
     
     
         33 . The system of  claim 20 , further comprising a dashboard configured to connect to a wearable sensing device. 
     
     
         34 . The system of  claim 20 , further comprising a dashboard configured to connect to an internet-connected smart speaker. 
     
     
         35 . The system of  claim 20 , further comprising an edge processing hub configured to use artificial intelligence to prioritize of data streams based on anomalies in patient behavior. 
     
     
         36 . The system of  claim 20 , further comprising an edge processing hub configured to integrate a suite of disparate sensors to create comprehensive data streams based on behavior activities of 5 or more core common daily activities. 
     
     
         37 . The system of  claim 20 , further comprising a water disaggregation algorithm specific to at least one of bathing, toileting, and kitchen water usage related to common daily activities 
     
     
         38 . A method, comprising:
 receiving at least one measurement from at least one sensor;   determining an activity of a patient based on the received at least one measurement; and   generating alerts when trends deviate from a set of pre-defined thresholds.