A patient support apparatus includes one or more readers and/or sensing devices to receive data that can be use to verify the identity of persons or objects, such as patients, caregivers, and medications. The sensing devices may include, for example, a biometric sensor, a physiological sensor, a bar code reader, a camera, a microphone, or a combination thereof. The patient support apparatus may use identity verification information obtained from these devices for a number of different purposes, including, for example, managing the delivery of therapy, medication, or treatment to a patient associated with the patient support apparatus.

Provided are a wearable personal digital device and related methods. The wearable personal digital device may comprise a processor, a display, biometric sensors, activity tracking sensors, a memory unit, a communication circuit, a housing, an input unit, a projector, a timepiece unit, a haptic touch control actuator, and a band. The processor may be operable to receive data from an external device, provide a notification to a user based on the data, receive a user input, and perform a command selected based on the user input. The communication circuit may be communicatively coupled to the processor and operable to connect to a wireless network and communicate with the external device. The housing may be adapted to enclose the components of the wearable personal digital device. The band may be adapted to attach to the housing and secure the wearable personal digital device on a user body.

A system and method that enables a person to unobtrusively quantify the effect of mobility, physical activity, learning, social interaction and diet on cognitive function. The method records on the electronic device one of global positioning system longitude and latitude coordinates, accelerometer coordinates, and gyroscope coordinates, one of outgoing and incoming phone calls, outgoing and incoming emails, and outgoing and incoming text messages, one of URLs visited on an internet browser application, books read on an e-reader application, games played on game applications, and the nutritional content of food consumed, performs the step of learning a function mapping from those recordings to measurements of cognitive function using a loss function to identify a set of optimal weights that produce a minimum for the loss function, uses those optimal weights to create the function mapping, and performs the step of computing the variance of the cognitive function measurements that is explained by the function mapping to assign an attribution to the effect of physical activity on measured changes in cognitive function.

Provided are a wearable personal digital device and related methods. The wearable personal digital device may comprise a processor, a display, biometric sensors, activity tracking sensors, a memory unit, a communication circuit, a housing, an input unit, a projector, a timepiece unit, a haptic touch control actuator, and a band. The processor may be operable to receive data from an external device, provide a notification to a user based on the data, receive a user input, and perform a command selected based on the user input. The communication circuit may be communicatively coupled to the processor and operable to connect to a wireless network and communicate with the external device. The housing may be adapted to enclose the components of the wearable personal digital device. The band may be adapted to attach to the housing and secure the wearable personal digital device on a user body.

Certain aspects of the disclosure are directed to a weighing scale including a platform, data-procurement circuitry, processing circuitry and foot-controlled user interface (FUI). The processing circuitry is electrically integrated with the data-procurement circuitry to process data obtained by the data-procurement circuitry while the user is standing on the platform and therefrom generate cardio-related physiologic data. The FUI provides data to the user while the user is standing on the platform and receive a foot-based user input from the user for the user to interact with the weighing scale.

Certain aspects of the disclosure are directed to an apparatus including a scale and external circuitry. The scale includes a platform, and data-procurement circuitry for collecting signals indicative of the user's identity and cardio-physiological measurements. The scale includes processing circuitry to process data obtained by the data-procurement circuitry, therefrom generate cardio-related physiologic data, and to send user data to the external circuitry. The external circuitry identifies a risk that the user has a condition based on the reference information and the user data provided by the scale and outputs generic information correlating to the condition to the scale that is tailored based on the identified risk.

A medical monitoring device may include a gesture control sensor for detecting gesture commands from users. The medical monitoring device may enable gesture control when it receives an identifier from a tag and the identifier is authenticated. The tag may include an RFID tag and/or a barcode. The identifier may be authenticated locally and/or by a remote device communicatively coupled to the medical monitoring device (e.g., over a network). Once gesture control is enabled, gestures by the user may be identified by the medical monitoring device, an action corresponding to the gesture may be determined, and the action may be performed. The gesture control sensor may be disabled manually or automatically when predetermined criteria are satisfied to prevent the gesture control sensor from receiving inadvertent commands.

Methods, devices, systems, and kits are provided that buffer the time spaced glucose signals in a memory, and when a request for real time glucose level information is detected, transmit the buffered glucose signals and real time monitored glucose level information to a remotely located device, process a subset of the received glucose signals to identify a predetermined number of consecutive glucose data points indicating an adverse condition such as an impending hypoglycemic condition, confirm the adverse condition based on comparison of the predetermined number of consecutive glucose data points to a stored glucose data profile associated with the adverse condition, where confirming the adverse condition includes generating a notification signal when the impending hypoglycemic condition is confirmed, and activate a radio frequency (RF) communication module to wirelessly transmit the generated notification signal to the remotely located device only when the notification signal is generated.

Systems, devices, and methods are provided that allow the authentication of devices within analyte monitoring systems. The analyte monitoring systems can be in vivo systems and can include a sensor control device with a sensor and accompanying circuitry, as well as a reader device for communicating with the sensor control device. The analyte monitoring systems can interface with a trusted computer system located at a remote site. Numerous techniques of authentication are disclosed that can enable the detection of counterfeit components, such as a counterfeit sensor control device.

A system and method that enables a person to unobtrusively quantify the effect of mobility, physical activity, learning, social interaction and diet on cognitive function. The method records on the electronic device one of global positioning system longitude and latitude coordinates, accelerometer coordinates, and gyroscope coordinates, one of outgoing and incoming phone calls, outgoing and incoming emails, and outgoing and incoming text messages, one of URLs visited on an internet browser application, books read on an e-reader application, games played on game applications, and the nutritional content of food consumed, performs the step of learning a function mapping from those recordings to measurements of cognitive function using a loss function to identify a set of optimal weights that produce a minimum for the loss function, uses those optimal weights to create the function mapping, and performs the step of computing the variance of the cognitive function measurements that is explained by the function mapping to assign an attribution to the effect of physical activity on measured changes in cognitive function.