According to an embodiment, an electronic device may comprise: a flexible display, at least one microphone, a sensor, a communication module comprising communication circuitry, and at least one processor operatively connected with the flexible display, the at least one microphone, the sensor, and the communication module. The at least one processor may be configured to: based on receiving a call signal from an external electronic device through the communication module, identify whether the call signal is a video call signal, based on the call signal being the video call signal, identify a folding state of the electronic device through the sensor, adjust a sound signal received through the at least one microphone based on the folding state, and control the communication module to transmit the adjusted sound signal to the external electronic device.

An example computing device includes a photodetector to measure an amount of light incident on a detection surface of the photodetector. The example computing device includes a state sensor to activate the photodetector responsive to the computing device being in a detection state. The example computing device also includes a processor. An example processor identifies, during the detection state, a user gesture based on an output of the photodetector. The user gesture blocks light incident on the detection surface of the photodetector. The example processor also alters an operation of the computing device based on the user gesture.

Methods and systems of enabling a transportation mode on a telematics device coupled to a vehicle are provided. One method includes detecting a first event or receiving a command for enabling a transportation mode, running a transportation mode power-saving scheme in response to receiving the first event or the command, and exiting the transportation mode power-saving scheme in response to detecting a second event.

Computing devices and methods for determining opening and closing of touch sensitive interfaces are disclosed. In one example, a computing device comprises a touch screen display on a first substrate that is rotatably coupled to a second substrate that includes a trackpad. A trackpad identification signal transmitted by the trackpad is received at the touch screen display, and a touch screen identification signal transmitted by the touch screen is received at the trackpad. If the trackpad identification signal matches a trackpad identification key and the touch screen identification signal matches a touch screen identification key, then an energy level of one or both signals is compared to an energy level threshold. Based at least in part on the comparison of the energy level to the threshold, a power state transition is initiated.

Computing devices and methods for determining opening and closing of touch sensitive interfaces are disclosed. In one example, a computing device comprises a touch screen display on a first substrate that is rotatably coupled to a second substrate that includes a trackpad. A trackpad identification signal transmitted by the trackpad is received at the touch screen display, and a touch screen identification signal transmitted by the touch screen is received at the trackpad. If the trackpad identification signal matches a trackpad identification key and the touch screen identification signal matches a touch screen identification key, then an energy level of one or both signals is compared to an energy level threshold. Based at least in part on the comparison of the energy level to the threshold, a power state transition is initiated.

A machine learning model is trained for user activity detection and context detection on a mobile device. The machine learning model is configured to learn a statistical relationship between an always-on sensing modality of the mobile device and actual user context. Rather than user annotations, the machine learning model is enhanced and personalized for the always-on sensing modality by automated annotations obtained from non-always-on sensing modalities. The non-always-on sensing modality opportunistically provides an imperfect label of user context, where the imperfect label has a known associated probability of error.

Eyewear including a support structure defining a region for receiving a head of a user. The support structure supports optical elements, electronic components, and a use detector. The use detector is coupled to the electronic components and is positioned to identify when the head of the user is within the region defined by the support structure. The electronic components monitor the use detector and transition from a first mode of operation to a second mode of operation when the use detector senses the head of the user in the region.

Eyewear including a support structure defining a region for receiving a head of a user. The support structure supports optical elements, electronic components, and a use detector. The use detector is coupled to the electronic components and is positioned to identify when the head of the user is within the region defined by the support structure. The electronic components monitor the use detector and transition from a first mode of operation to a second mode of operation when the use detector senses the head of the user in the region.

A computer implemented method for energy or resource management of a human-machine interface comprises the following steps carried out by computer hardware components of the human-machine interface: determining a level of attention of a user of the human-machine interface to the human-machine interface; and setting an energy and/or resource utilization related setting of the human-machine interface based on the determined level of attention.

A computer implemented method for energy or resource management of a human-machine interface comprises the following steps carried out by computer hardware components of the human-machine interface: determining a level of attention of a user of the human-machine interface to the human-machine interface; and setting an energy and/or resource utilization related setting of the human-machine interface based on the determined level of attention.