System, method, and apparatus for pet condition detection

1 . A method for generating a wellness assessment of a pet based on sensor data comprising, by one or more computing systems:

receiving, by one or more processors, sensor data associated with a pet from one or more sensors, wherein the one or more sensors are associated with a wearable device worn by or attached to the pet, the sensor data including:

a time value corresponding to when the pet performed an activity, and

an intensity point corresponding to when the pet performed the activity;

detecting, by the one or more processors, an orientation of the wearable device, wherein the orientation is calculated based on rotation data of the wearable device;

transforming, by the one or more processors, the sensor data into a consistent coordinate system based on the orientation of the wearable device, wherein transforming the sensor data includes modifying the sensor data based on the rotation data;

executing, by the one or more processors, an algorithm to generate a baseline value for the activity based on aggregated activity data of a plurality of similar pets, wherein the plurality of similar pets comprises pet data of a same pet breed, a same pet age, or a same pet weight as the pet;

predicting, by the one or more processors, one or more health indicators corresponding to the pet based on the transformed sensor data, wherein the one or more health indicators include one or more metrics associated with the activity;

executing, by the one or more processors, the algorithm on the one or more health indicators to generate a wellness assessment of the pet, wherein the wellness assessment includes an energy expenditure wellness value of the pet determined based on the time value the pet performed the activity at the intensity point compared to a goal intensity point, wherein the goal intensity point is calculated based on the baseline value for the activity and the received intensity point for the pet;

determining, by the one or more processors, a pet recommendation by inputting the wellness assessment of the pet into a prediction module trained on previous pet data corresponding to the pet, wherein the pet recommendation includes a medical recommendation or a product recommendation; and

transmitting, by the one or more processors, an alert that includes the pet recommendation to a user interface of a user device.

2 . The method of claim 1 , wherein the one or more sensors further comprise one or more of an actuator, a gyroscope, a magnetometer, a microphone, or a pressure sensor.

3 . The method of claim 1 , wherein detecting the

orientation of the wearable device further comprises:

training, by the one or more processors, a convolutional neural network with training data to determine an orientation of the wearable device on a pet collar, wherein the training data is associated with a known orientation of the wearable device.

4 . The method of claim 1 , wherein generating the wellness assessment of the pet further comprises:

training, by the one or more processors, the prediction module based on one or more of health status data, demographic information, genetic data, location, weather information of the location, or environment data of the location.

5 . The method of claim 1 , further comprising:

comparing, by the one or more processors, at least one of the predicted health indicators to at least one stored corresponding health indicator; and

detecting, by the one or more processors, a threshold difference between the at least one predicted health indicator and the at least one stored corresponding health indicator;

wherein the wellness assessment further comprises the detected threshold difference between the at least one predicted health indicator and the at least one stored corresponding health indicator.

6 . The method of claim 1 , wherein the method further comprises:

rescaling, by the one or more processors, one or more of the metrics into a predetermined range; and

generating, by the one or more processors, the one or more energy expenditure wellness values based on the one or more rescaled metrics.

7 . The method of claim 1 , wherein the one or more health indicators are associated with one or more weights, respectively, wherein the method further comprises:

generating, by the one or more processors, the one or more energy expenditure wellness values based on the one or more weights.

8 . The method of claim 1 , wherein the activity comprises one or more of:

a posture comprising one or more of a lying down posture, a sitting posture, a standing posture, a walking posture, or a vigorous posture; or

a behavior comprising one or more of a drinking behavior, an eating behavior, a licking an object behavior, a self-licking behavior, a petting behavior, a rubbing behavior, a scratching behavior, a shaking behavior, or a sniffing behavior.

9 . The method of claim 1 , wherein the medical recommendation corresponds to a medical condition including at least one of: a dermatological condition, an ear infection, arthritis, a cardiac episode, a gastrointestinal condition, malaise, a tooth fracture, a cruciate ligament tear, or a pancreatic episode.

10 . The method of claim 1 , wherein the wellness assessment comprises one or more alerts of a possible medical condition, wherein the method further comprises:

generating an estimated timeline for the possible medical condition; and

presenting, via the user interface on the user device, the estimated timeline.

11 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:

receive sensor data associated with a pet captured by one or more sensors, wherein the one or more sensors are associated with a wearable device worn by or attached to the pet, the sensor data including:

a time value corresponding to when the pet performed an activity, and

an intensity point corresponding to when the pet performed the activity-

detect an orientation of the wearable device, wherein the orientation is calculated based on rotation data of the wearable device;

transform the sensor data into a consistent coordinate system based on the orientation of the wearable device, wherein transforming the sensor data includes modifying the sensor data based on the rotation data;

execute an algorithm to generate a baseline value for the activity based on aggregated activity data of a plurality of similar pets, wherein the plurality of similar pets comprises pet data of a same pet breed, a same pet age, or a same pet weight as the pet;

predict one or more health indicators corresponding to the pet based on the transformed sensor data, wherein the one or more health indicators include one or more metrics associated with the activity;

execute the algorithm on the one or more health indicators to generate a wellness assessment of the pet, wherein the wellness assessment includes an energy expenditure wellness value of the pet determined based on the time value the pet performed the activity at the intensity point compared to a goal intensity point, wherein the goal intensity point is calculated based on the baseline value for the activity and the received intensity point for the pet;

determine a pet recommendation by inputting the wellness assessment of the pet into a prediction module trained on previous pet data corresponding to the pet, wherein the pet recommendation includes a medical recommendation or a product recommendation; and

transmit an alert that includes the pet recommendation to a user interface of a user device.

12 . A system comprising:

one or more processors; and

a memory storing instructions that, when executed by the one or more processors, cause the processors to:

receive sensor data associated with a pet captured by one or more sensors, wherein the one or more sensors are associated with a wearable device worn by or attached to the pet, the sensor data including:

a time value corresponding to when the pet performed an activity, and

an intensity point corresponding to when the pet performed the activity-

detect an orientation of the wearable device, wherein the orientation is calculated based on rotation data of the wearable device;

transform the sensor data into a consistent coordinate system based on the orientation of the wearable device, wherein transforming the sensor data includes modifying the sensor data based on the rotation data;

execute an algorithm to generate a baseline value for the activity based on aggregated activity data of a plurality of similar pets, wherein the plurality of similar pets comprises pet data of a same pet breed, a same pet age, or a same pet weight as the pet;

predict one or more health indicators corresponding to the pet based on the transformed sensor data, wherein the one or more health indicators include one or more metrics associated with the activity;

execute the algorithm on the one or more health indicators to generate a wellness assessment of the pet, wherein the wellness assessment includes an energy expenditure wellness value of the pet determined based on the time value the pet performed the activity at the intensity point compared to a goal intensity point, wherein the goal intensity point is calculated based on the baseline value for the activity and the received intensity point for the pet;

determine a pet recommendation by inputting the wellness assessment of the pet into a prediction module trained on previous pet data corresponding to the pet, wherein the pet recommendation includes a medical recommendation or a product recommendation; and

transmit an alert that includes the pet recommendation to a user interface of a user device.

13 . The system of claim 12 , wherein the one or more sensors further comprise one or more of an actuator, a gyroscope, a magnetometer, a microphone, or a pressure sensor.

14 . The system of claim 12 , wherein the instructions, when executed, further cause the one or more processors to:

train a convolutional neural network with training data to determine an orientation of the wearable device on the pet collar, wherein the training data is associated with a known orientation of the wearable device.

15 . The system of claim 12 , wherein the instructions, when executed, further cause the one or more processors to:

train the prediction module based on one or more of health status data, demographic information data, genetic data, location data, weather information data of the location, or environment data of the location.

16 . The system of claim 12 , wherein the instructions, when executed, further cause the one or more processors to:

compare at least one of the predicted health indicators to at least one stored corresponding health indicator; and

detect a threshold difference between the at least one predicted health indicator and the at least one stored corresponding health indicator;

wherein the wellness assessment further comprises the detected threshold difference between the at least one predicted health indicator and the at least one stored corresponding health indicator.

17 . The system of claim 12 , wherein the instructions, when executed, further cause the one or more processors to:

rescale one or more of the metrics into a predetermined range; and

generate the one or more energy expenditure wellness values based on the one or more rescaled metrics.

18 . The system of claim 12 , wherein the one or more health indicators are associated with one or more weights, respectively, wherein the instructions, when executed, further cause the one or more processors to:

generate the one or more energy expenditure wellness values based on the one or more weights.

19 . The system of claim 12 , wherein the activity comprises one or more of:

a posture comprising one or more of a lying down posture, a sitting posture, a standing posture, a walking posture, or a vigorous posture; or

a behavior comprising one or more of a drinking behavior, an eating behavior, a licking an object behavior, a self-licking behavior, a petting behavior, a rubbing behavior, a scratching behavior, a shaking behavior, or a sniffing behavior.

20 . The system of claim 12 , wherein the medical recommendation corresponds to a medical condition including at least one of: a dermatological condition, an ear infection, arthritis, a cardiac episode, a gastrointestinal condition, malaise, a tooth fracture, a cruciate ligament tear, or a pancreatic episode.