Vital data including data regarding a pulse is continuously obtained from a wearable terminal worn by a person to be monitored and stored in a database as target quantitative information associated with disease information and physical condition information regarding the person; an analysis with time-series patterns based on the vital data as an explanatory variable and the disease information and/or the physical condition information at and after times when the time-series patterns were obtained as a response variable is performed using the target quantitative information accumulated in the database; a time-series pattern based on new vital data is compared with the time-series pattern found in the analysis to be correlated with the disease information and/or the physical condition information; and a physical event including disease onset and a physical condition change of a person who has shown the new vital data is estimated or predicted.

The present disclosure includes a medical monitoring hub as the center of monitoring for a monitored patient. The hub includes configurable medical ports and serial ports for communicating with other medical devices in the patient's proximity. Moreover, the hub communicates with a portable patient monitor. The monitor, when docked with the hub provides display graphics different from when undocked, the display graphics including anatomical information. The hub assembles the often vast amount of electronic medical data, associates it with the monitored patient, and in some embodiments, communicates the data to the patient's medical records.

A communication terminal includes a memory in which identification information associated with a user is stored, a controller that carries out authentication of the user, and a communication interface that transmits a signal including the identification information. When user authentication is successful, the controller sets the communication terminal to a first state in which the signal is transmitted to an external apparatus, and when user authentication is not successful, the controller sets the communication terminal to a second state in which the signal is not transmitted to the external apparatus.

Hypoglycemic event prediction using machine learning is described. A CGM platform includes a machine learning model trained using historical time series glucose measurements of a user population. Once trained, the machine learning model predicts hypoglycemic events for users. When predicting hypoglycemic events, a time series of glucose measurements for a day time interval is received. The glucose measurements of this time series for the day time interval are provided by a CGM system worn by the user. The machine learning model predicts whether a hypoglycemic event will occur during a night time interval that is subsequent to the day time interval by processing the time series of glucose measurements using the trained machine learning model. The hypoglycemic event prediction is then output, such as via communication and/or display of a notification about the hypoglycemic event prediction.

Techniques for remote monitoring of a patient and corresponding medical device(s) are described. The remote monitoring comprises determining identification data and identifying implantable medical device (IMD) information, initiating an imaging device and determining an imaging program, receiving one or more frames of image data including image(s) of an implantation site, identifying an abnormality at the implantation site, triggering a supplemental image capture mode, receiving one or more supplemental images of the implantation site, and outputting the one or more supplemental images of the implantation site.

Specially designed collection strips and their processing. By using specially designed collection strips, having a backer and one or more absorbent pads, in conjunction with a unique processing method, the processes of analyzing biological samples such as blood, or the like, may be done efficiency with the elimination of cross contamination risk. Identification of the sample stays with the sample throughout the process as it resides on the collection strip. The strip absorbs a known volume. The sample with identification is placed directly in an elution solution, without mechanically separating the sample from its identification information. Elimination of the need for mechanical separation tends to reduce cross contamination, as well as reducing sample processing time.

A system is described to create customized unique identification (UID) codes combined with customized printable optical or NFC sensors and to combine these unique sensors and unique IDs with unique environmental events, traceability, unique data from cell phones (including geolocation) and person-specific unique indicators such as biomarkers to create completely unique, low cost and proprietary printable genomic and environmental blockchain sensor networks for the Internet of Things (IoT), counterfeit identification, healthcare, pharmaceutical applications and small payment transactions worldwide.

An assembly for enabling a caregiver to secure a physiological monitoring device to an arm of a user can include the physiological monitoring device a cradle configured to removably secure to the physiological monitoring device and to the user’s arm. The physiological monitoring device can include a first connector port configured to electrically connect to a first cable and a first locking tab movable between an extended position and a retracted position. The cradle can include a base, first and second sidewalls, a back wall connected to the base and the first and second sidewalls. The cradle can further include a first opening in the back wall configured to receive the first connector port and a second opening in the first sidewall configured to receive the first locking tab when the physiological monitoring device is secured to the cradle and the first locking tab is in the extended position.

A biological-information acquisition device is provided that includes a biometric sensor part including a biometric sensor that detects biological information and a living-body wearable part that enables the biometric sensor part to be in contact with a living body and a main body part including an information processing section that processes biological information and a battery that supplies power. The biometric sensor part is configured to be replaceable by being separated from the main body part, and the main body part is detachably attached to the biometric sensor in such a manner as not to come into contact with a living body when the biometric sensor part is worn on the living body.

A two part patient monitoring device includes an activator module and a sensor device. The activator module includes a non-galvanic data port that creates a communication path with a non-galvanic data port on the sensor device. The activator module includes power contact pads that are each at least partially surrounded by a bias ring. A bias voltage is applied to the bias rings and a processor or circuit in the activator module monitors the voltage on the bias ring to detect a leakage current. The sensor module includes power contact pins that engage the power contact pads to transfer power from the activator module to the sensor device. Each of the contact pins are surrounded by a seal member such that the connection between the power contact pins and the power contact pads is protected from debris and/or moisture.