A personalized parameter learning method, a sleep-aid device and a non-transitory computer readable medium are provided. The personalized parameter learning method for a sleep-aid device is provided. The personalized parameter learning method includes the following steps. A process device computes a measured sleep quality of a user after operating a sleep-aid device with an inputted parameter setting at least according to a subjective feedback from the user. The processing device generates a plurality of candidate parameter settings according to the measured sleep quality. The processing device generates a plurality of predicting sleep qualities corresponding the candidate parameter settings. The processing device obtains a recommending parameter setting by selecting one of the candidate parameter settings according to the predicting sleep qualities.

A personalized parameter learning method, a sleep-aid device and a non-transitory computer readable medium are provided. The personalized parameter learning method for a sleep-aid device is provided. The personalized parameter learning method includes the following steps. A process device computes a measured sleep quality of a user after operating a sleep-aid device with an inputted parameter setting at least according to a subjective feedback from the user. The processing device generates a plurality of candidate parameter settings according to the measured sleep quality. The processing device generates a plurality of predicting sleep qualities corresponding the candidate parameter settings. The processing device obtains a recommending parameter setting by selecting one of the candidate parameter settings according to the predicting sleep qualities.

A cloud-based healthcare diagnostic and treatment platform capable of making non-invasive healthcare diagnostics and treatment recommendations based on data captured from combinations of physical activity and mental activity. In an embodiment, the system and method comprise a healthcare diagnostics and treatment module comprising a HIPPA-compliant security gateway, an AI-assisted healthcare diagnostics module, and an alert and treatments module. The HIPPA-compliant security gateway separates out health-related data from streams of data which may include other data such as game data, provides the health-related data to the AI-assisted healthcare diagnostics module which performs diagnoses of the health-related data using machine learning algorithms, and provides the diagnoses to the alert and treatments module which generates alerts and treatments based on the diagnoses.

A cloud-based healthcare diagnostic and treatment platform capable of making non-invasive healthcare diagnostics and treatment recommendations based on data captured from combinations of physical activity and mental activity. In an embodiment, the system and method comprise a healthcare diagnostics and treatment module comprising a HIPPA-compliant security gateway, an AI-assisted healthcare diagnostics module, and an alert and treatments module. The HIPPA-compliant security gateway separates out health-related data from streams of data which may include other data such as game data, provides the health-related data to the AI-assisted healthcare diagnostics module which performs diagnoses of the health-related data using machine learning algorithms, and provides the diagnoses to the alert and treatments module which generates alerts and treatments based on the diagnoses.

A method of performing a dialysis treatment includes using a pump and a dialysate supply line to transport peritoneal dialysis fluid, the supply line having a proximal end into which peritoneal dialysis fluid is supplied and from which spend dialysate is withdrawn, and a distal end which is connected to a patient's peritoneal access. The method further includes generating proximal and distal pressure signals using pressure detectors located at both the proximal and distal ends, respectively, of said supply line. During a drain cycle in which spent dialysate is pumped from the patient, the method includes, responsively to the proximal and distal pressure signals, detecting a characteristic of a pressure difference between the distal and proximal ends whose magnitude is determined by a predicted change in dialysate properties, and responsively to the characteristic, generating a signal indicating the change in dialysate properties.

Provided is an exacerbation prediction device equipped with a respiration sensing means of continuously sensing respiration data of a patient, a calculation means of calculating stable respiration data that are respiration data during a condition in which a respiratory rate is lowered and stable for a certain period of time from the sensed continuous respiration data of the patient, and a prediction means of predicting occurrence of an acute exacerbation in the patient in accordance with the stable respiration data calculated during a certain period of time.

Provided is an exacerbation prediction device equipped with a respiration sensing means of continuously sensing respiration data of a patient, a calculation means of calculating stable respiration data that are respiration data during a condition in which a respiratory rate is lowered and stable for a certain period of time from the sensed continuous respiration data of the patient, and a prediction means of predicting occurrence of an acute exacerbation in the patient in accordance with the stable respiration data calculated during a certain period of time.

A system includes a mobile device having one or more cameras to take images; a sensor detecting reflected light from one or more lasers and a diffuser to detect object range or dimension; code for motion tracking, environmental understanding by detecting planes in an environment, and estimating light and dimensions of the surrounding based on the one or more lasers; code to estimate a three-dimensional (3D) volume of an object from multiple perspectives and from projected laser beams to measure size or scale and determine locations of points on the object's surface in a plane or a slice using time-of-flight, wherein positions and cross-sections for different slices are correlated to construct a 3D model of the object, including object position and shape; the device receiving user request to select a content from one or more augmented, virtual, or extended reality contents and rendering a reality view of the environment.

A system includes a mobile device having one or more cameras to take images; a sensor detecting reflected light from one or more lasers and a diffuser to detect object range or dimension; code for motion tracking, environmental understanding by detecting planes in an environment, and estimating light and dimensions of the surrounding based on the one or more lasers; code to estimate a three-dimensional (3D) volume of an object from multiple perspectives and from projected laser beams to measure size or scale and determine locations of points on the object's surface in a plane or a slice using time-of-flight, wherein positions and cross-sections for different slices are correlated to construct a 3D model of the object, including object position and shape; the device receiving user request to select a content from one or more augmented, virtual, or extended reality contents and rendering a reality view of the environment.

A processing device in one embodiment is configured to generate a first sound cue of a first type, the first sound cue comprising a primary entrainment cue for an entrainment sonification system, to generate one or more additional sound cues of a second type, each of the one or more additional sound cues comprising an auxiliary entrainment cue for the entrainment sonification system, to provide the first sound cue and the one or more additional sound cues to one or more audio devices of the entrainment sonification system for generation of sound for audible presentation to a user, to receive from one or more sensors of the entrainment sonification system one or more feedback signals, and to adjust one or more characteristics of at least one of the first sound cue and the one or more additional sound cues based at least in part on the one or more received feedback signals.