Automatic in-home senior care system augmented with internet of things technologies

A computer implemented method for providing in-home care for seniors, comprising:

• receiving a set of sensor outputs from sensors disposed within a living area of a senior according to a sensor floorplan;
• mapping the set of sensor outputs to events associated with features of symptoms of depression for the senior to generate an event vector for the senior;
• classifying the event vector into a likelihood that the senior has depression based on the set of sensor outputs;
• wherein the mapping comprises:
• identifying a first relationship between symptoms of depression and events, wherein the events are measurable physical or mental features associated with at least one of the symptoms;
• identifying a second relationship between the events and the set of sensor outputs based at least in part on the locations of each sensor and a sensor type of each sensor;
• and
• identifying a third relationship of sensor transformations required to transform sensor data into a format indicative of events;
• wherein the first relationship, the second relationship, and the third relationship is used to generate a normalized feature vector that is input to a logistic regression classifier of a machine learning model wherein the logistic regression classifier is trained to determine thresholds for identifying depression based on a training data set of a set of seniors.

A computer implemented method for providing in-home care for seniors, comprising:

• determining a sensor floor plan for a plurality of Internet of Thing (IOT) sensors to be installed in a living space of a senior to generate sensor data that correllates with symptoms of depression in seniors;
• receiving a set of sensor outputs from a set of Internet of Thing (IOT) sensors installed in a living space of a senior according to the sensor floorplan;
• mapping the set of sensor outputs to events associated with features of symptoms of depression for the senior to generate an event vector for the senior;
• classifying the event vector into a likelihood that the senior has depression based on the set of sensor outputs;
• wherein the mapping comprises:
• identifying a first relationship between symptoms of depression and events, wherein the events are measurable physical or mental features associated with at least one of the symptoms;
• identifying a second relationship between the events and the set of sensor outputs based at least in part on the locations of each sensor and a sensor type of each sensor;
• and
• identifying a third relationship of sensor transformations required to transform sensor data into a format indicative of events;
• wherein the first relationship, the second relationship, and the third relationship is used to generate a normalized feature vector that is input to a classifier of a machine learning model wherein the classifier is trained to determine thresholds for identifying depression based on a training data set of a set of seniors.