A method for collecting prone position data of a human body is disclosed. The method includes: collecting weight data, back contour data and height data of a standing subject; receiving the weight data, the back contour data, and the height data, pre-preprocessing such data, and sending the pre-processed pre-processing data to a simulated bed by a processing center; receiving the pre-processed data and adjusting an initial pressure of a plurality of elastic columns of the simulated bed according to the pre-processed data by the simulated bed; causing the subject to lie on the adjusted simulated bed and further adjusting the simulated bed according to a subjective description of the subject until the simulated bed is suitable for the subject; collecting adjustment data of the simulated bed suitable for the subject by the processing center. A system for collecting prone position data of a human body is also disclosed.

Apparatus and methods to monitor changes in user weight. The apparatus includes at least one sensor configured to capture superquotidien measurements of the user's weight and an accelerometer to measure movements of the user, and estimates cumulative weight of the user based on the superquotidien measurements, wherein only sudden changes in the user's weight are used in the estimation. The sensor(s) can be located, e.g., within a shoe insole, within a chair, within a floor, etc.

Apparatus and methods to monitor changes in user weight. The apparatus includes at least one sensor configured to capture superquotidien measurements of the user's weight and an accelerometer to measure movements of the user, and estimates cumulative weight of the user based on the superquotidien measurements, wherein only sudden changes in the user's weight are used in the estimation. The sensor(s) can be located, e.g., within a shoe insole, within a chair, within a floor, etc.

A load cell apparatus for use with a bed includes a housing having a top portion and a bottom portion, and a load cell device held by the bottom portion of the housing. The load cell device is structured to generate a signal having a magnitude that is proportional to a first force being applied to the load cell device. The load cell apparatus also includes a button member held by the housing in a manner wherein the button member is structured to engage the load cell device and apply the first force to the load cell device in response to a second force being applied to the top portion of the housing. Also, various systems for monitoring parameters such as weight, sleep quality, fall risk, and/or pressure sore risk that may incorporate such a load cell apparatus.

A method for determining the weight of an entity/item to be weighed on a weighing device (1), the weighing device comprising at least a weight sensor and a control unit (4),

the method comprising the following steps:
/a/ collecting weight raw samples (WSi) of the total weight sensed at the weight sensor(s), at a sampling frequency (F0), and converting each of the weight raw samples (WSi) into digitalized weight raw samples (DSi),
/b/ entering sequentially each of the digitalized weight raw samples (DSi) into a Butterworth filter, the latter issuing filtered weight samples (FSi),
/c/ defining a rolling window (RW) containing a parametrized number NS of latest filtered weight samples (FSi),
/d1/ determining, in the rolling window, the minimum (MIN) and maximum (MAX) values of filtered weight samples,
/d2/ comparing the value of MAX−MIN with regard to a parametrized Threshold (T),
/e/ if MAX−MIN is greater than Threshold (T), repeat steps/a/ to /d2/, and as soon as MAX−MIN is less than Threshold (T), output a final weight value (DV), obtained from one or more of the most recent filtered weight samples.

A method for determining the weight of an entity/item to be weighed on a weighing device (1), the weighing device comprising at least a weight sensor and a control unit (4),

the method comprising the following steps:
/a/ collecting weight raw samples (WSi) of the total weight sensed at the weight sensor(s), at a sampling frequency (F0), and converting each of the weight raw samples (WSi) into digitalized weight raw samples (DSi),
/b/ entering sequentially each of the digitalized weight raw samples (DSi) into a Butterworth filter, the latter issuing filtered weight samples (FSi),
/c/ defining a rolling window (RW) containing a parametrized number NS of latest filtered weight samples (FSi),
/d1/ determining, in the rolling window, the minimum (MIN) and maximum (MAX) values of filtered weight samples,
/d2/ comparing the value of MAX−MIN with regard to a parametrized Threshold (T),
/e/ if MAX−MIN is greater than Threshold (T), repeat steps/a/ to /d2/, and as soon as MAX−MIN is less than Threshold (T), output a final weight value (DV), obtained from one or more of the most recent filtered weight samples.

A changing table placed on a ground or a table and capable of weighting a child is provided. The changing table includes a frame, a weighting module and a fabric component. The frame includes a lower frame and an upper frame movably installed on the lower frame. The weighting module includes four load sensors disposed on four corners of the frame and between the lower frame and the upper frame. The fabric component covers the upper frame to form a supporting area between the upper frame and the lower frame for supporting the child. The changing table can measure a first weight of the child before feeding the child and further measure a second weight of the child after feeding the child, so as to determine a difference of the second weight and the first weight of the child as the feeding intake.

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.

For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.

For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.