Method and apparatus for determining probabilistic context awareness of a mobile device user using a single sensor and/or multi-sensor data fusion

An electronic device, comprising:

• a sensing unit comprising at least one sensor and being configured to acquire sensing data;
• a computing device configured to:
• extract sensor specific features from the sensing data;
• generate a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features;
• process the motion activity vector, voice activity vector, and spatial environment vector so as to determine a base level context of the electronic device relative to its surroundings, the base level context having a plurality of aspects each based on the motion activity vector, voice activity vector, and spatial environment vector, and
• determine meta level context of the electronic device relative to its surroundings as a function of the base level context, wherein the meta level context comprises at least one inference made from at least two aspects of the plurality of aspects of the base level context;
• wherein the computing device is configured to process the motion activity vector, voice activity vector, and spatial environment vector by:
• generating a motion activity posteriorgram as a function of the motion activity vector, the motion activity posteriorgram representing a probability of each element of the motion activity vector as a function of time;
• generating a voice activity posteriorgram as a function of the voice activity vector, the voice activity posteriorgram representing a probability of each element of the voice activity vector as a function of time;
• and
• generating a spatial environment posteriorgram as a function of the spatial environment vector, the spatial environment posteriorgram representing a probability of each element of the spatial environment vector as a function of time.

An electronic device, comprising:

• a sensing unit comprising at least one sensor and being configured to acquire sensing data;
• a printed circuit board (PCB) having at least one conductive trace thereon;
• system on chip (SoC) mounted on the PCB and electrically coupled to the at least one conductive trace;
• and
• a computing device comprising a sensor chip mounted on the PCB in a spaced apart relation with the SoC and electrically coupled to the at least one conductive trace such that the sensor chip and SoC are electrically coupled, wherein the sensor chip comprises an micro-electromechanical system (MEMS) sensing unit, and a control circuit configured to
• extract sensor specific features from the sensing data;
• generate a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features;
• process the motion activity vector, voice activity vector, and spatial environment vector so as to determine a base level context of the electronic device relative to its surroundings, the base level context having a plurality of aspects each based on the motion activity vector, voice activity vector, and spatial environment vector, and
• determine meta level context of the electronic device relative to its surroundings as a function of the base level context, wherein the meta level context comprises at least one inference made from at least two aspects of the plurality of aspects of the base level context.

An electronic device, comprising:

• a printed circuit board (PCB) having at least one conductive trace thereon;
• a system on chip (SoC) mounted on the PCB and electrically coupled to the at least one conductive trace;
• a sensor chip mounted on the PCB in a spaced apart relation with the SoC and electrically coupled to the at least one conductive trace such that the sensor chip and SoC are electrically coupled, and configured to acquire sensing data;
• wherein the sensor chip comprises:
• a micro-electromechanical system (MEMS) sensing unit;
• an embedded processing node configured to:
• preprocess the sensing data,
• extract sensor specific features from the sensing data,
• generate a motion activity posteriorgram, a voice activity posteriorgram, and a spatial environment posteriorgram as a function of the sensor specific features,
• process the motion activity posteriorgram, voice activity posteriorgram, and spatial environment posterior gram so as to determine a base level context of the electronic device relative to its surroundings, the base level context having a plurality of aspects each based on the motion activity posteriorgram, voice activity posteriorgram, and spatial environment posteriorgram, and
• determine meta level context of the electronic device relative to its surroundings as a function of the base level context and a pattern library stored in the cloud or a local memory, wherein the meta level context comprises at least one inference made from at least two aspects of the plurality of aspects of the base level context.

A method, comprising:

• acquiring sensing data from a sensing unit;
• extracting sensor specific features from the sensing data, using a computing device;
• generating a motion activity vector, a voice activity vector, and a spatial environment vector as a function of the sensor specific features, using the computing device;
• processing the motion activity vector, voice activity vector, and spatial environment vector so as to determine a base level context of the electronic device relative to its surroundings, the base level context having a plurality of aspects each based on the motion activity vector, voice activity vector, and spatial environment vector, using the computing device;
• and
• determining meta level context of the electronic device relative to its surroundings as a function of the base level context, wherein the meta level context comprises at least one inference made from at least two aspects of the plurality of aspects of the base level context, using the computing device;
• wherein the motion activity vector, voice activity vector, and spatial environment vector are processed by:
• generating a motion activity posteriorgram as a function of the motion activity vector, the motion activity posteriorgram representing a probability of each element of the motion activity vector as a function of time;
• generating a voice activity posteriorgram as a function of the voice activity vector, the voice activity posteriorgram representing a probability of each element of the voice activity vector as a function of time;
• and
• generating a spatial environment posteriorgram as a function of the spatial environment vector, the spatial environment posteriorgram representing a probability of each element of the spatial environment vector as a function of time.