A system and a method for predicting kinesthetic outcomes from observed position, posture, behavior or activity of an individual 1602, 1702. The system uses kinesthetic activity sensors 102, 104 each collecting one or more of audio, video, or physiological signals and capturing the activity of the individual or an ambient environment of the individual. These signals are delivered into a computer system 106 implementing a learning routine 108 which constructs one or more personalized kinetic state models 1510 of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models 1500 for the individual and uses the multidimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future, and provides notice of predicted unsafe or undesired outcomes.

A wearable device consists of a smart band and a display unit. The smart band comprises a microbial biosensor, a particulate matter sensor, an enviro sensor, a single board computer, a power supply unit, a band fastener, and a set of watch adapters. The microbial biosensor detects, measures, and monitors beneficial microorganisms and pathogens in a nasal cavity, an oral cavity, or on a surface. The microbial biosensor sterilizer kills pathogens. The particulate matter sensor detects, measures, and monitors a set of suspended particles in the surrounding air comprising beneficial microorganisms, pathogens, pollen grains, dust mite allergens, and an air quality index. The enviro sensor detects, monitors, and measures environmental conditions surrounding the user. A computing system comprises a wearable device, a microbiome mobile application, a user, a mobile device, a laboratory testing facility, a cloud server, and a physician.

Medical devices and related patient management systems and parameter modeling methods are provided. An exemplary method of operating a sensing device associated with a patient involves obtaining current operational context information associated with the sensing device, obtaining a parameter model associated with the patient, calculating a current parameter value based on the parameter model and the current operational context information, obtaining one or more signals from a sensing element configured to measure a condition in a body of the patient, and providing an output that is influenced by the calculated current parameter value and the one or more signals.

Bio-potentials are sensed on the skin of a subject. The bio-potentials include muscle bio-potentials, and nerve bio-potentials. Skin sensors are positioned to enable the sensing circuitry to emphasize the nerve bio-potentials and deemphasize the muscle bio-potentials in processed bio-potential signals generated by the sensing circuitry. A machine learning component identifies control sequences or tracks body motions based on the processed bio-potential signals.

A method and system that is integrated in order to provide an automated control system for the user, which provides messaging to bedroom environmental control systems as a function of the status of the user's sleep state is disclosed herein. The system comprises a sleep monitoring sub-system and a bedroom environmental control sub-system. The sleep monitoring sub-system is configured to transmit the subject's sleep progression data to an interface for the bedroom environmental control system. The bedroom environmental control system is configured to modify a bedroom environment based on the subject's sleep progression data.

Systems and methods for gesture control are disclosed. In some embodiments, a system may include a plurality of electrode pairs, a motion sensor, a controller, and a classifier. The system may be configured to: enter a monitoring state in which the system is configured to receive data; receive a first set of data; determine that the first set of data does not satisfy one or more action criteria; return to the monitoring state without transmitting the first set of data to the classifier; receive a second set of data; determine that the second set of data satisfies the one or more action criteria; transmit the second set of data to the classifier; using the classifier, analyze the second set of data to generate an interpreted output indicating a gesture performed by the person; and based on the interpreted output, generate a machine instruction.

Methods, systems and computer program products provide for a Risk Assessment Engine that receives body ambient temperature data captured by a sensor in contact with a person. The Risk Assessment Engine characterizes types of activities performed by the person during a time range associated with the body ambient temperature data. The Risk Assessment Engine determines a risk classification individualized for the person based on respective workloads and the corresponding allocations of work and rest experienced by the person during performance of the characterized types of activities.

An electronic device is provided. The electronic device includes a biometric sensor for detecting biometric data, a situation sensing module, memory, and a processor operatively coupled to the biometric sensor, the situation sensing module, and the memory. The processor can check whether a user is in an inactive state using the situation sensing module, check reference biometric data corresponding to the inactive state if the user is in the inactive state, extract biometric data of the user using the biometric sensor, and measure the blood pressure of the user on the basis of the extracted biometric data and the reference biometric data.

Provided is an apparatus configured to detect a light signal, the apparatus including a light source configured to radiate light toward an object, a light receiver configured to receive an ambient light and a transmitted light corresponding to the light radiated toward the object from the light source, at least one processor configured to control the light source to be turned off such that a first output signal is generated by the light receiver and control the light source to be turned on such that a second output signal is generated by the light receiver, and an operator configured to generate a transmitted light signal from which noise is removed by differentially operating the second output signal from the first output signal.

An arousal level prediction apparatus and method are disclosed. The arousal level prediction apparatus obtains first biological information indicating current biological information of the user, obtains first environment information indicating a current environment around the user, and obtains living information of the user indicating an activity history of the user. The arousal level predication apparatus includes a process that calculates a first arousal level indicating a current arousal level of the user based on the first biological information, predicts a second arousal level, which is an arousal level of the user at a certain period of time later, based on the first arousal level, the first environment information and the living information, and outputs the second arousal level.