Providing safety and environmental features using human presence detection

A system, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to perform operations comprising:
• receiving a plurality of received signal strength indications (RSSIs) respectively associated with a plurality of Wi-Fi signals, wherein each Wi-Fi signal of the plurality of Wi-Fi signals is transmitted by a respective module in a plurality of modules that are arranged to form a detection zone around a user space, wherein a portion of the plurality of modules each comprise at least one speaker of a home entertainment system, and the user space comprises a smart television of the home entertainment system;
• receiving ambient sound recorded by a microphone incorporated into a module of the plurality of modules;
• forming a Wi-Fi signature based at least on the plurality of RSSIs and the recorded ambient sound;
• recognizing an intruder-indicative sound in the ambient sound;
• providing at least the Wi-Fi signature as input to a machine learning model that is configured to determine whether an intruder is present based on at least the Wi-Fi signature and the recognized intruder-indicative sound;
• based on determining that a presence of a user is not detected in the detection zone, and in response to receiving a determination from the machine learning model that the intruder is present and determining that the intruder is within the detection zone, sending a message to a user device of the user;
• and
• based on receiving a user response to the message;
• saving metadata based on a type of the user response to an in-memory database of the smart television;
• playing a sound on at least one speaker of the portion of the plurality of modules via the smart television;
• and
• transmitting the metadata over a network to a cloud computing environment configured to train the machine learning model based on the metadata and on metadata from a plurality of other smart televisions corresponding to a plurality of other users.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• receiving a plurality of received signal strength indications (RSSIs) respectively associated with a plurality of Wi-Fi signals, wherein each Wi-Fi signal of the plurality of Wi-Fi signals is transmitted by a respective module in a plurality of modules that are arranged to form a detection zone around a user space, wherein a portion of the plurality of modules each comprise at least one speaker of a home entertainment system, and the user space comprises a smart television of the home entertainment system;
• receiving ambient sound recorded by a microphone incorporated into a module of the plurality of modules;
• forming a Wi-Fi signature based at least on the plurality of RSSIs and the recorded ambient sound;
• recognizing an intruder-indicative sound in the ambient sound;
• providing at least the Wi-Fi signature as input to a machine learning model that is configured to determine whether an intruder is present based on at least the Wi-Fi signature and the recognized intruder-indicative sound;
• based on determining that a presence of a user is not detected in the detection zone, and in response to receiving a determination from the machine learning model that the intruder is present and determining that the intruder is within the detection zone, sending a message to a user device of the user;
• and
• based on receiving a user response to the message:
• saving metadata based on a type of the user response to an in-memory database of the smart television;
• playing a sound on at least one speaker of the portion of the plurality of modules via the smart television;
• and
• transmitting the metadata over a network to a cloud computing environment configured to train the machine learning model based on the metadata and on metadata from a plurality of other smart televisions corresponding to a plurality of other users.