A computer implemented method for providing pain management using a wearable device determines a predictive model estimating an intensity level of pain as a function of at least one physiological parameter of a user of the wearable device and at least one activity of the user. The activity of the user includes one or combination of a type of the activity, a level of the activity, a location of the activity, and a duration of the activity. The method determines measurements of physiological and activity sensors of the wearable device to produce values of the physiological parameter and the activity of the user and predicts the intensity level of the pain based on the predictive model and the values of the physiological parameter and the activity of the user. The method executes actions based on the predicted intensity level of pain.

An embodiment of the invention provides a method and system including a sensor on a vehicle and a processor connected to the sensor. The processor determines a probability of falling based on input from the sensor, whether the probability of falling exceeds a threshold, and a state of an operator of the vehicle. An actuator connected to the processor receives a signal from the processor when the probability of falling exceeds the threshold and when the state of the operator includes an impaired state. Stabilizer wheels are connected to the actuator, where the signal includes a command to deploy the stabilizer wheels.

A motion-sensing floor mat, an assembly of such floor mats, and a monitoring system with such floor mats are provided; wherein the floor mat can be joined with another such floor mat and electrically connected to a monitoring device to form the monitoring system; the monitoring device stores a queue list and a topology matrix and uses a topological algorithm to store the identification tag of each such floor mat detected into the queue list in order, to gradually establish the topology matrix for the floor mats detected; and to thereby obtain the relative positions of the floor mats detected. When any of the floor mats is subjected to pressure (e.g., when someone falls on the floor mat accidentally) and generates a sensing signal, the monitoring device can pinpoint the position of that floor mat (i.e., the location of the fall) rapidly according to the topology matrix.

A system, method, and computer program product for generating item recommendations based on sensor data captured by sensors on client machines. A server processes collected sensor data to estimate user physical activity based on sensor data patterns, and forms a user profile based on the estimated user physical activity. The server associates the user profile with relevant product or service recommendations, based on previous purchases by a user or by others matching the user profile. The user may make a purchase based on the displayed recommendations or forward the recommendations to others. The recommendations include discounts based on the user profile. Subsequent sensor data showing satisfaction with a purchased item serves as a testimonial to user satisfaction with the item. The sensor data may be collected from a user in an augmented or virtual reality system, and the recommendations may be displayed in such a system.

A wearable drug delivery device, techniques, and computer-readable media that provide an application that implements a diabetes treatment plan for a user are described. The drug delivery device may include a controller operable to direct operation of the wearable drug delivery device. The controller may provide a selectable activity mode of operation for the user. Operation of the drug delivery device in the activity mode of operation may reduce a likelihood of hypoglycemia during times of increased insulin sensitivity for the user and may reduce a likelihood of hyperglycemia during times of increased insulin requirements for the user. The activity mode of operation may be manually activated by the user or may be activated automatically by the controller. The controller may automatically activate the activity mode of operation based on a detected activity level of the user and/or a detected location of the user.

A quantitative gait training and/or analysis system includes one or more footwear modules that may include a piezoresistive sensor, an inertial sensor and an independent logic unit. The footwear module functions to permit the extraction of gait kinematics and evaluation thereof in real time, or data may be stored for later reduction and analysis. Embodiments relating to calibration-based estimation of kinematic gait parameters are described.

An electronic device and a method for controlling the same are provided. The electronic device includes a communicator including circuitry, a first sensor configured to detect movement information of the electronic device, a memory including a first determination module configured to determine whether a user carries the electronic device and a second determination module configured to determine a detecting method for detecting a user location, and a processor configured to identify whether a user of the electronic device carries the electronic device based on the movement information of the electronic device obtained by the first sensor by using the first determination module, and determine a detecting method for detecting location information of the user according to whether the user carries the electronic device by using the second determination module.

An apparatus for providing information about a user's brain resources is provided. The apparatus includes at least sensor interface circuitry and processing circuitry coupled to the sensor interface circuitry. In a calibration mode, the sensor interface circuitry is configured to receive first sensor data from an electroencephalography sensor. The first sensor data are indicative of an electroencephalogram of the user. Further, the sensor interface circuitry is configured to receive second sensor data from a physiological sensor in the calibration mode. The second sensor data are indicative of a physiological property of the user. In the calibration mode, the processing circuitry is configured to train a brain-physiological model for the user based on the first sensor data and the second sensor data.

Methods, systems and devices are provided for motion-activated display of messages on an activity monitoring device. In one embodiment, method for presenting a message on an activity monitoring device is provided, including the following method operations: downloading a plurality of messages to the device; detecting a stationary state of the device; detecting a movement of the device from the stationary state; in response to detecting the movement from the stationary state, selecting one of a plurality of messages, and displaying the selected message on the device.

A wearable health and lifestyle device including at least a measurement module configured to be worn by a user in at least a first wearing position, the measurement module comprising a 3-axis accelerometer unit configured to provide acceleration data and inclination data, a temperature measurement unit configured to provide temperature data, a light radiation measurement unit configured to provide light radiation data, said light radiation measurement unit comprising at least one multi-spectral sensor configured to measure wavelength bands over the range 290 nm to 1150 nm, a storage module configured to receive and store said acceleration data, said inclination data, said temperature data and said light radiation data, and an analysis module configured to analyze a data set comprising acceleration data, inclination data, temperature data and light radiation data.