A biological-measurement device includes a light-emitting unit configured to emit light on a body of a test-subject, a light-detecting unit configured to detect light reflected in the body of the test-subject, a control-unit configured to calculate information regarding a pulse-wave of the body of the test-subject based on the light detected by the light-detecting unit, a circuit-board that is flexible and has a first-surface on which the light-emitting unit and the light-detecting unit are provided, the circuit-board further having wiring connecting the light-emitting unit and the control-unit together and connecting the light-detecting unit and the control-unit together, a shielding-unit that is provided on the first-surface, the shielding-unit being situated between the light-emitting unit and the light-detecting unit and configured to protrude beyond the light-emitting unit and the light-detecting unit in a direction perpendicular to the first-surface, and an adhesive-part for firmly contacting with the body of the test-subject.

A system and method for providing sensory relief from distractibility, inattention, anxiety, fatigue, and/or sensory issues to a user in need. The user can be autistic/neurodiverse, or neurotypical. The system can be configured to connect to a datastore storing one or more sensory thresholds specific to a user of a wearable device of the system, the sensory thresholds selected from auditory, visual or physiological sensory thresholds; record, using one or more sensors of the wearable device, a sensory input stimulus to the user; compare the sensory input stimulus with the sensory thresholds to determine an intervention to be provided to the user, the intervention configured to provide the user relief from distractibility, inattention, anxiety, fatigue, or sensory issues; and provide the intervention to the user, the intervention comprising filtering, in real-time, an audio signal presented to the user or an optical signal presented to the user.

The embodiments of the present disclosure disclose a system and method. The system may include at least one storage device configured to storage computer instruction; and at least one processor, in communication with the storage device. When executing the computer instructions, the at least one processor is configured to direct the system to perform operations including: obtaining a sensing signal of at least one sensing device; identifying a signal feature of the sensing signal; and determining, based on the signal feature, an operation of a target object associated with the at least one sensing device.

Systems and methods are disclosed for analyzing data from oncology treatments such as immune checkpoint inhibitor or radiotherapy therapies, including predicting adverse events of the oncology therapies, predicting objective response of the oncology therapies, predicting symptoms from the oncology therapies, and use of such predictions by technological implementations to achieve improved system and medical outcomes. An example technique for generating a predicted treatment outcome includes: receiving patient data for a human subject, which provides patient-reported outcomes collected from the human subject relating to a particular oncology treatment; processing the patient data with a trained artificial intelligence (AI) prediction model, which receives the patient data as input and produces a prediction of a treatment outcome as output; and outputting data to modify a treatment workflow of an oncology treatment for the human subject, based on the prediction of the treatment outcome.

A method includes: receiving first data including physiological information obtained from a subject and a first result that a disease is developing; specifying a first time point at which the physiological information included in the first data is obtained; receiving second data including the physiological information obtained from the subject and a second result that the disease is not developing; specifying a second time point at which the physiological information included in the second data is obtained; upon determining that a time interval between the first time point and the second time point is smaller than a first predetermined value, assigning, to the second data, a first training label indicating that the disease is developing and a weighting index that is capable of taking a plurality of values according to the time interval; and performing machine learning of a model by using the second data as training data.

Provided herein are systems, devices and methods for monitoring the progression of, determining and/or assessing brain related conditions in a subject based on pupil light responses (PLRs) to focal central and peripheral chromatic light stimuli, in particular, by classifying the PLR based on one or more PLR parameter values, wherein the classifying allows monitoring the progression of, determining and/or assessing the brain related condition

Patient sleep is staged using personalized circadian models built with data collected by wearable devices over daytime and nighttime hours, thus capturing a patient's personal circadian rhythms. The circadian model is used to identify sleep intervals in incoming nightly data for the patient. The identified sleep intervals are analyzed by the machine learning system which stages epochs of sleep. Methods include receiving patient heart rate data from over a plurality of circadian cycles; creating a circadian model for the patient with a defined operation for applying sleep labels to new data from the wearable device; applying the circadian model to nightly test data from the device to identify a sleep interval; and assigning, with a classifier, sleep stages to epochs of the sleep interval.

Measurement of an induced shear wave in tensioned tissue of a given individual is provided to a machine learning system trained to determine absolute load from shear wave signal data. The machine learning system uses a teaching set linking shear wave signal data to absolute load, however, does not require normal calibration data based on measured loads allowing reduced or no calibration for absolute load determinations.

A physiological monitoring device is provided and includes a physiological sensing device, a first PPG sensor, a vital signs detector, and a PPG controller. The physiological sensing device senses at least one physiological feature of a subject to generate at least one sensing signal. The first PPG sensor senses pulses of a blood vessel of the subject to generate a first PPG signal when the first PPG sensor is activated. The vital signs detector obtains vital signs data according to the at least one sensing signal. The PPG controller detects whether a specific event is happening to the subject according to the vital signs data. In response to detecting that the specific event is happening to the subject, the PPG controller activates the first PPG sensor. The physiological monitoring apparatus obtains a blood oxygen level of the subject according to the first PPG signal.

A pulse oximeter includes a light emitting device that emits a first light and a second light, a light detecting device that outputs a first signal and a second signal respectively corresponding to an intensity of the first light and an intensity of the second light after interacting with a tissue of a subject, a processing device that calculates a pulsation rate of at least one of the first signal and the second signal, calculates a percutaneous arterial oxygen saturation of the subject, and estimates a capillary refill time of the tissue based on a time taken for at least one of the pulsation rate and the percutaneous arterial oxygen saturation, which change along with compression on the tissue, to return to a predetermined threshold range with respect to each value before the compression, and an output device that outputs information indicating the capillary refill time.