A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

A fitness tracking system includes a shoe, a monitoring device, and a controller. The monitoring device is mounted on the shoe and includes an accelerometer configured to generate acceleration data corresponding to acceleration of a foot received by the shoe. The controller is operably connected to the accelerometer and is configured to collect sampled acceleration data by sampling the generated acceleration data, to identify foot strike data of the sampled acceleration data, to identify a local minimum of the sampled acceleration data collected prior to the foot strike data, and to determine foot strike characteristic data corresponding to the foot strike data based on an acceleration value at the local minimum.

Systems and methods for detecting udder disease based on machine learning methods and complementary supporting techniques are presented. Included are methods for assembling time sequences of images of each animal of a herd or set for subsequent use in per-animal image analysis for disease detection. Methods presented also include image pre-processing methods used prior to image analysis, resulting in contrast and resolution optimization such as appropriate image intensity level adjustment and resolution downsampling for more rapid and more accurate disease detection. Combinatorial techniques for compositing whole-udder images or udder-quarter images from partial images captures are described. Methods are provided for power usage optimization in regard to computing resources used in the computing-intensive AI analysis methods. Location-based and animal history-based detection refinements are incorporated into described systems. Further presented are methods for multi-modal and multi-factor detection of udder disease, as well as methods for infection type classification.

A smartphone or other mobile computer device, general purpose or specialized, wherein the smartphone device is configured to actively control the configuration of one or more bladders, compartments, chambers or internal sipes and one or more sensors located in either one or both of a sole or a removable inner sole insert of the footwear of the user and/or located in an apparatus worn or carried by the user, glued unto the user, or implanted in the user. The one or more bladders, compartments, chambers, or sipes, and one or more sensors are configured for computer control. A sole and/or a removable inner sole insert for footwear, including one or more bladders, compartments, chambers, internal sipes and sensors in the sole and/or in a removable insert; or on an insole; all being configured for control by a smartphone or other mobile computer device, general purpose or specialized.

The present disclosure provides systems, methods and apparatuses for inducing a desired biological response in an organism through the use of one or more repetitive signals from one or a series of LED lights designed to emit the signal with multiple pulsed components. Each component of the signal contains a one or more light color spectrum or wavelength that is within 50 nm of the peak absorption of a photon receptor of the organism corresponding to the desired biological response. Each component has a repetitive ON duration with an OFF duration and an intensity where the relationship between the ON duration and OFF duration of the first component and the second component induces the desired response in the organism through the stimulation or excitation of a molecule associated with a photoreceptor and the reset of the molecule.

The disclosure provides wearable cardiac arrest detection and alerting device that incorporates a non-invasive sensor based on optical and/or electrical signals transmitted into and received from human tissue containing blood vessels, and that transcutaneously quantifies the wearer's heart rate. The heart-rate quantification enables the detection of the absence of any heart beat by the wearable detection and alerting device indicative of the occurrence of a cardiac arrest, wherein the heart is no longer achieving effective blood circulation in the individual wearing the device. The display on the wearable cardiac arrest detection and alerting device may include the elapsed time since the time of detection of a heart rate that is below a predetermine lower limit value, i.e., the detected occurrence of a cardiac arrest event.

A first optical measurement of tissue with a first optical device is initiated. The first optical measurement includes a first shallow optical reading and a first deeper optical reading. A second optical measurement of the tissue with a second optical device spaced is initiated. The second optical device is spaced apart from the first optical device. The second optical measurement includes a second shallow optical reading and a second deeper optical reading. A first difference value between the first shallow optical reading and the first deeper optical reading is determined. A second difference value between the second shallow optical reading and the second deeper optical reading is determined. A large vessel occlusion (LVO) alert is generated when a ratio of the first difference value to the second difference value is larger than a threshold value.

Disclosed are a multiple physiological data collection device and system that collect, manually mark, sampling—physiological data for machine learning and AI analyses in a single operation background. Physiological data uploaded by sensing devices of different type and function, described in different formation, recorded at different times and/or pertaining to different person can be processed in one system. The invented system comprises a data uploading device, a data storage device and a data edition device and, optionally, an automated data analysis device.

Systems and methods involving an intraocular implant with input and/or output electronics are described. In some embodiments, the system includes an intraocular lens having at least one optic operably coupled to a haptic, one or more input electronics on the haptic and/or the optic; and one or more output electronics on the haptic and/or the optic for receiving and/or transmitting data.

An energy balancing system scales portion sizes of user-selected meal recipes to control a digital scale to prompt the user to measure scaled quantities of meal ingredients to prepare scaled meals. Physical activity tracking devices provide data to generate physical activity energy expenditures for energy burned by physical activity. The physical activity energy is added to a basal metabolic rate of energy expenditure that is a function of sex, weight, height, and age. The total energy expended are scaled down for a weight-loss goal to obtain the total recommended energy. A recipe portion-size optimizer adjusts scaling factors for each recipe so that the total recommended energy is met by the scaled meals. Amounts of nutrients for the recipes can also be scaled by the scaling factors to match recommended nutrient amounts when the meal recipe optimizer generates the scaling factors using an over-determined linear system optimizer.