Certain aspects of the disclosure are directed to an apparatus including a scale and risk-assessment circuitry which is configured to assess a condition likely linked to the user. The scale includes a platform, and data-procurement circuitry for collecting signals specific to the user and cardio-physiological measurements. The scale includes processing circuitry to process data obtained by the data-procurement circuitry, therefrom generates cardio-related physiologic data, and sends an alert of the condition. The risk-assessment 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.

Certain aspects of the disclosure are directed to an apparatus including a scale and risk-assessment circuitry which is configured to assess a condition likely linked to the user. The scale includes a platform, and data-procurement circuitry for collecting signals specific to the user and cardio-physiological measurements. The scale includes processing circuitry to process data obtained by the data-procurement circuitry, therefrom generates cardio-related physiologic data, and sends an alert of the condition. The risk-assessment 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.

According to an example embodiment, there is provided a method in a device (110) that comprises a sensor portion (119) for deriving, on basis of one or more sensor signals that are descriptive of respective physical characteristic pertaining to a user, at least one measurement signal that is descriptive of a characteristic pertaining to the user, the method comprising obtaining first measurement data that comprises one or more values indicated by the at least one measurement signal and transferring the first measurement data to a server device (150) over a first wireless link (104), receiving, from the server device (150), device information that comprises at least a device identifier and access information for another device that is capable of providing second measurement data that is descriptive of one or more characteristics pertaining to the same user, in response to receiving said device information, detecting presence of said another device (130) and establishing a second wireless link (106) with said another device (130) using said device information, and receiving the second measurement data from said another device (130) via the second wireless link (106) and transferring the second measurement data to the server device (150) via the first wireless link (104).

According to an example embodiment, there is provided a method in a device (110) that comprises a sensor portion (119) for deriving, on basis of one or more sensor signals that are descriptive of respective physical characteristic pertaining to a user, at least one measurement signal that is descriptive of a characteristic pertaining to the user, the method comprising obtaining first measurement data that comprises one or more values indicated by the at least one measurement signal and transferring the first measurement data to a server device (150) over a first wireless link (104), receiving, from the server device (150), device information that comprises at least a device identifier and access information for another device that is capable of providing second measurement data that is descriptive of one or more characteristics pertaining to the same user, in response to receiving said device information, detecting presence of said another device (130) and establishing a second wireless link (106) with said another device (130) using said device information, and receiving the second measurement data from said another device (130) via the second wireless link (106) and transferring the second measurement data to the server device (150) via the first wireless link (104).

A method for determining a normalized weight of a non-static item is disclosed. Weight data associated with the non-static item is received from a plurality of load cells. A load cell weight for the non-static item is determined based at least in part on the weight data. The load cell weight for the non-static item is received as an input for a machine learning algorithm. The normalized weight for the non-static item is generated as an output for the machine learning algorithm.

A method for determining a normalized weight of a non-static item is disclosed. Weight data associated with the non-static item is received from a plurality of load cells. A load cell weight for the non-static item is determined based at least in part on the weight data. The load cell weight for the non-static item is received as an input for a machine learning algorithm. The normalized weight for the non-static item is generated as an output for the machine learning algorithm.

Systems and methods use a three-dimensional snapshot of an infant, child, adolescent, or adult where the subject may have bent legs and an unaligned head position, to provide an estimate of human length. The system identifies anatomical features from the digital information and generates a virtual skeleton. The cumulative distances between pseudo-joints of the virtual skeleton provide an estimate of human length. Comparing length estimates from multiple three-dimensional snapshots of the same individual acquired over time provide an indication of growth of the infant, child, or adolescent. Daily estimates of length can detect growth faltering sooner than less frequent estimates of length, which can lead to timely intervention.

A purpose of the disclosure is to provide a measurement device for human bodies that can encourage a person who is reluctant to measure body composition to use the device. A measurement device has: an electric resistance value acquisition unit for acquiring an electric resistance value of one or more parts of a body of a person to be measured; and a type determination unit for using the electric resistance value to determine a type representing a physical characteristic of the person to be measured from one or more predetermined points of view.

A body composition analysis system having image scanning function includes an image capturing device, a body composition analyzer having a stand platform with two first electrode sets, two handrails and a calculating unit electrically connected with the image capturing device, and a control panel. Each handrail has a telescopic rod and a handle having a second electrode set. An end of each telescopic rod is pivotably attached to the stand platform. Each handle is disposed at another end of each telescopic rod. The calculating unit includes a body shape module database and a calculational logic, receives resistance and reactance measured by the first and second electrode sets and image data obtained by the image capturing device, and after comparing the image data with the body shape module database, calculates by the calculational logic to obtain a body composition data to be shown on the control panel.

The present disclosure relates to distance measurement methods and apparatuses. One example method includes receiving multiple first echo signals, determining multiple first spectrum data groups based on the multiple first echo signals, performing normalization processing on a signal strength value corresponding to each distance value comprised in each first spectrum data group to obtain a normalized signal strength value corresponding to each distance value, determining, based on a normalized signal strength value, a variance value of a signal strength value corresponding to each distance value comprised in the multiple first spectrum data ggroups, and determining a distance between a target obstacle on the to-be-measured object and the transmitting origin based on the variance value of the signal strength value corresponding to each distance value comprised in the multiple first spectrum data groups.