A panum's area measurement method includes: projecting a first parallax image of a spatial object to the left eye of a user under test, and projecting a second parallax image of the spatial object to the right eye of the user under test, the first parallax image comprising a first homologous point and the second parallax image comprising a second homologous point; adjusting a horizontal parallax amount between the first homologous point and the second homologous point until the user under test observes the spatial object producing a ghost; acquiring a parallax amount parameter Δn.sub.e; calculating a horizontal physical spacing Δx between the first homologous point and the second homologous point based on the parallax amount parameter Δn.sub.e; and calculating a panum's area range (μ.sub.in, μ.sub.out) of the user under test based on the horizontal physical spacing Δx.

A hearable comprises at least one microphone coupled with a wearable structure and a sensor processing unit disposed within the wearable structure and coupled with the microphone. A portion of the wearable structure is configured to be disposed within a user's ear. The sensor processing unit acquires audio data from the at least one microphone and head motion data from at least one motion sensor of the sensor processing unit. The head motion data describes motions of the user's head and comprises cranium motion data and mandible motion data. The sensor processing unit separates the mandible motion data from the head motion data, synchronizes the mandible motion data and the audio data into a synchronized data stream; classifies an activity of the head during a portion of the synchronized data stream; and generates a health indicator for the user based on the activity and the synchronized data stream.

Systems, devices, and methods are disclosed for the purpose of quantitatively determining the susceptibility of a human subject to injure their Anterior Cruciate Ligament (ACL). A method for determining injury susceptibility scores or risk categories includes determining hip extension angles and knee varus angles during the performance of a stork test and determining hip abduction angles during a squat test. Determination of angles during certain movements may be achieved using various systems and devices, including wearable devices.

There is provided a device for assisting exercise, comprising a video providing unit configured to provide a first video data including a first exercise movement, a data obtaining unit configured to obtain a second video data based on an input relating to the first video data, a joint information extracting unit configured to extract a first joint information obtained by detecting plural skeletons from the second video data, an analyzing unit configured to obtain an analysis information based on a similarity determined by comparing the first joint information with a second joint information of the first video data and a recommendation unit configured to obtain recommendation information for recommending an exercise movement to a user based on at least one of the first video data, the second video data and the analysis information from a database including plural exercise movements may be provided according to an embodiment.

Disclosed herein are techniques related to device performance mode adjustment based on activity detection. In some embodiments, the techniques involve detecting, based on processing sensor data obtained from one or more gesture sensors, an activity in which a user of the one or more gesture sensors is engaged. The techniques further involve generating information corresponding to the detected activity in which the user of the one or more gesture sensors is engaged. The techniques also involve controlling, based on providing the generated information to a device for monitoring a physiological characteristic of the user, adjustment of a performance mode of the device for monitoring the physiological characteristic of the user.

There is described a method of assessing severity of a respiratory distress of a patient. The method generally has, using a three dimensional (3D) camera, generating at least a 3D image encompassing at least a thorax region and an abdomen region of the patient; and using a computer, accessing said 3D image; identifying thorax coordinates indicating coordinates of at least a point of the thorax region of the patient in the 3D image; identifying abdomen coordinates indicating coordinates of at least a point of the abdomen region of the patient in the 3D image; determining a thoraco-abdominal distance based on the thorax coordinates and on the abdomen coordinates; comparing the thoraco-abdominal distance with a threshold; and generating a signal based on said comparison, said signal being indicative of a degree of severity of the respiratory distress of the patient.

Measurement devices include a housing featuring a channel, where the housing is dimensioned to be worn in an oral cavity of a user and the channel is positioned such that at least some of the user's teeth are positioned in the channel when the housing is worn, a first sensor positioned within or on a surface of the housing, where the first sensor is configured to generate a first measurement signal that includes information about a position of the measurement device, and a second sensor positioned within or on a surface of the housing, where the second sensor is configured to generate a second measurement signal that includes information about at least one of a biochemical and an electrical property of the user's oral cavity.

A head-mounted device includes one or more eye-tracking cameras and one or more computer-readable hardware storage devices having stored thereon computer-executable instructions, including a machine-learned artificial intelligence (AI) model. The head-mounted device is configured to cause the one or more eye-tracking cameras to take a series of images of one or more areas of skin around one or more eyes of a wearer, and use the machine-learned AI model to analyze the series of images to extract a photoplethysmography waveform. A heart rate is then detected based on the photoplethysmography waveform.

A detection device, a detection system and a detection method capable of detecting accurate position and orientation of a wearable device on a body are provided. A detection device includes a wireless communicator that receives orientation information indicating an orientation of a wearable device disposed on a body from the wearable device by wireless communication, measures an intensity of a radio wave radiated from the wearable device, and outputs intensity information indicating measured intensity of the radio wave; and a calculator that calculates a position and an orientation of the wearable device on the body based on the orientation information and the intensity information.

A wearable electronic device, comprising: a substrate: a first motion sensing region, comprising at least one first electrode on the substrate; a second motion sensing region, comprising at least one second electrode, wherein a shielding layer is provided on the second electrode, and the second electrode is between the shielding layer and the substrate, wherein a user causes more capacitance variation to the first electrodes and causes less capacitance variation to the second electrodes when the user wears the smart watch; a capacitance calculating circuit, coupled to the first electrode, configured to calculate a capacitance variation generated by the first electrode or the second electrode; and a motion determination circuit, configured to determine a motion of the wearable electronic device according to the capacitance variation of the first electrode or the capacitance variation of the second electrode.