A system for directly tracking human activity is described. The system comprises a plurality of interactive activity objects such as, construction toys, that interact with each other in one or more activities involving physical manipulation of the interactive activity objects by a person; at least one wearable device associated with the person to track the person's activity by sensing the person's movements, to obtain data directly relating to the person's activity; and a database to record the person's direct activity and at least some of the person's direct physical manipulation of with the interactive activity objects.

An image display device includes a display panel having a plurality of pixels, and a photoresistor pattern formed in a non-light emitting area between the plurality of pixels; a panel driving controller to drive light-emission control lines of the display panel; and a bio-signal detector that detects an output voltage of the photoresistor pattern which resistance is variable based on light reflected from the target object, and detects a bio-signal of the target object based on the detected output voltage, thereby, detecting the bio-signal of the target object using the image display light from the display panel may allow efficiently detecting the bio-signal without a separate light source or light source operation circuit for detecting the bio-signal.

An electronic device and a method for detecting apnea are provided. The method includes: transmitting a wireless signal to a human subject, receiving an echo corresponding to the wireless signal, and obtaining channel state information (CSI) from the echo; generating a detection result according to the CSI; outputting the detection result. Here, the detection result indicates whether an apnea event has occurred on the human subject.

Methods, techniques, apparatus, and algorithms are described for robustly measuring real-world distances using any mobile device equipped with an accelerometer and monocular camera. A general software implementation processes 2D video, precisely tracking points of interest across frames to estimate the unsealed trajectory of the device, which is used to correct the device's inertially derived trajectory. The visual and inertial trajectories are then aligned in scale space to estimate the physical distance travelled by the device and the true distance between the visually tracked points.

A smart controller with a sensor, according to one embodiment of the present inventive concept, communicates wirelessly with a mobile terminal and has a sensor for providing, to the mobile terminal, biometric information of a user or external environmental information, the smart controller comprising: a body; an input button unit which is arranged on the upper surface or a side surface of the body and which generates a control signal for the mobile terminal by means of operation by the user; a sensor for detecting biometric information of the user or external environmental information; and a control unit which is arranged inside the body and which receives an input of a detection value of the sensor and delivers same to the mobile terminal.

A first electronic device includes an interface; microphone circuitry; memory circuitry; and processor circuitry. The first electronic device is configured to discover, via the interface, a first wireless network. The first electronic device is configured to receive, via the interface, from a second electronic device discovering the first wireless network, a voice biometric indicative of a second user. The first electronic device is configured to activate the microphone circuitry to detect an input audio signal. The first electronic device is configured to determine whether the detected input audio signal satisfies one or more criteria. The first electronic device is configured to, when the detected input audio signal satisfies the one or more criteria, determine a parameter indicative of a level of risk of exposure; and generate, based on the parameter, contact data.

Analyte sensor comprises an electrode layer having an elongate body comprising a proximal end and a distal end. The electrode layer includes a first active working electrode area, a second electrode portion, and at least one gap electrically separating the first active working electrode portion and the second electrode portion. The first active working electrode area comprises at least one sensing spot with at least one analyte responsive enzyme disposed thereupon. Additional analyte sensors disclosed.

Methods, systems, and devices are disclosed for providing a portable enzymatic-ink dispensing system. The system includes an enzymatic-ink including one or more biocompatible binders, one or more biocompatible mediators, an enzyme, an enzyme stabilizer, and a conductive material. The system includes a dispenser including a chamber to hold the enzymatic-ink and an applicator to apply the enzymatic ink dispensed from the chamber onto a target substrate.

An integrated disease management system provides patients with simple, quick, and readily available counseling regarding a healthy diabetic lifestyle. The system can include an interactive engine with predictive analytics and machine learning to provide a customized experience for a user. The system can be configured to transmit data to a remote server to perform analysis of received data (e.g., disease management data), to provide feedback to the user (e.g., customized feedback with curated content based on a user's data and interface interactions), and send all or a portion of the data and/or curated content to another user device or remote health management access point (e.g., as cloud storage) where the information can be accessed by healthcare stakeholder.

An automated method and system for diagnosing sleep disorders and predicting treatment effectiveness using data collected on the user using an automated application including questionnaire, facial recognition technology, and historical user and patient data. The data can be used by a diagnostic and treatment prediction algorithm to diagnose sleep disorders, for example sleep apnea, and predict treatment effectiveness. The system is an automated, self-learning algorithm capable of assessing the risk for sleep disorders and predicting treatment effectiveness.