User health monitoring device and method for sickbed

Abstract

The invention discloses a user health monitoring device and method for a sickbed. The device comprises an information acquisition module, a weight measurement module, a body fat measurement module, a human body composition measurement and analysis module, a data acquisition module and a data evaluation module. The information acquisition module is used for acquiring basic information of a user; the basic information comprises age, gender, disease information and operation information; the weight measuring module is used for measuring weight information of a user; the body fat measuring module is used for measuring the body fat rate value of the user; the human body composition measurement and analysis module is used for measuring to obtain a human body composition analysis information set of a user; and the data evaluation module is connected with the information acquisition module and the data acquisition module, and is used for performing health monitoring evaluation processing on the basic information, the weight information, the body fat rate value and the human body composition analysis information set to obtain health monitoring result information.

Description

Technical Field

[0001] The present invention relates to the medical field and the health monitoring field, and particularly relates to a user health monitoring device and method for a hospital bed. Background Art

[0002] During the hospital treatment process, for the care process of critically ill patients or those with limited mobility, due to their limited mobility, when performing a physical health examination on them, they need to be moved next to the health monitoring device, which is time-consuming and laborious and also causes many inconveniences to the users.

[0003] For the hospital beds of critically ill patients or those with limited mobility, currently, there is generally only a fixed ordinary call terminal or intelligent call terminal fixedly arranged at the head of the ward. Relying on the existing hospital bed environment, it is also difficult to comprehensively monitor the health status of users. For critically ill patients, continuous monitoring of their health status using the hospital bed platform is of great significance for their treatment and rehabilitation process. Summary of the Invention

[0004] The present invention mainly solves the problem of how to comprehensively monitor the health status of users based on the existing hospital bed environment, and discloses a user health monitoring device and method for a hospital bed.

[0005] In the first aspect of the embodiments of the present application, a user health monitoring device for a hospital bed is disclosed, including:

[0006] An information collection module, a weight measurement module, a body fat measurement module, a body composition measurement and analysis module, a data collection module, and a data evaluation module;

[0007] The information collection module is used to collect the basic information of the user; the basic information includes age, gender, disease information, and surgical information;

[0008] The weight measurement module is used to measure the weight information of the user;

[0009] The body fat measurement module is used to measure the body fat percentage value of the user;

[0010] The body composition measurement and analysis module is used to measure the body composition analysis information set of the user; the body composition analysis information set includes the total body water value, total protein value, total inorganic salt value, right upper muscle weight, left upper muscle weight, right lower muscle weight, left lower muscle weight, and extracellular water ratio value;

[0011] The data acquisition module is respectively connected to the information acquisition module, the weight measurement module, the body fat measurement module, and the body composition measurement and analysis module, and is used to collect basic information, weight information, body fat percentage values, and a set of body composition analysis information, and send the collected basic information, weight information, body fat percentage values, and the set of body composition analysis information to the data evaluation module;

[0012] The data evaluation module is connected to the information acquisition module and the data acquisition module, and is used to perform health monitoring and evaluation processing on the basic information, weight information, body fat percentage values, and the set of body composition analysis information to obtain health monitoring result information.

[0013] The weight measurement module is arranged on the upper surface of the bed board of the hospital bed;

[0014] The body fat measurement module includes measurement electrodes and a data processing sub-module; the measurement electrodes are connected to the data processing sub-module and are used to collect the skin electrical signals of the user; the data processing sub-module is used to perform body fat percentage measurement processing on the collected skin electrical signals of the user to obtain the body fat percentage value of the user; the measurement electrodes include electrode bodies and mounting brackets, and the electrode bodies are mounted on the mounting brackets; the mounting brackets are mounted at the middle positions on both sides of the upper surface of the bed board of the hospital bed.

[0015] The body composition measurement and analysis module includes measurement electrodes, mounting brackets, and a data analysis sub-module;

[0016] The measurement electrodes are mounted on the mounting brackets;

[0017] The mounting brackets are mounted at the middle positions on both sides of the upper surface of the bed board and at the middle position of the bottom of the upper surface of the bed board; the measurement electrodes are connected to the data analysis sub-module and are used to collect the skin electrical signals of the user; the data analysis sub-module is used to perform body composition analysis processing on the collected skin electrical signals of the user to obtain a set of body composition analysis information.

[0018] The data evaluation module is used to perform health monitoring and evaluation processing on the basic information, weight information, body fat percentage values, and the set of body composition analysis information to obtain health monitoring result information, including:

[0019] The data evaluation module uses the weight information, body fat percentage values, and the set of body composition analysis information collected at several moments to construct a set of health monitoring time series information; the set of health monitoring time series information includes a weight series, a body fat percentage series, a total body water value series, a total protein value series, a total inorganic salt value series, a right upper muscle weight series, a left upper muscle weight series, a right lower muscle weight series, a left lower muscle weight series, and an extracellular water ratio value series;

[0020] Perform data preprocessing on the set of health monitoring time series information to obtain a set of preprocessed sequences;

[0021] Determine the corresponding set of standard sequences according to the basic information; the set of standard sequences includes a weight standard sequence, a body fat percentage standard sequence, a total body water value standard sequence, a total protein value standard sequence, a total inorganic salt value standard sequence, a right upper muscle weight standard sequence, a left upper muscle weight standard sequence, a right lower muscle weight standard sequence, a left lower muscle weight standard sequence, and an extracellular water ratio value standard sequence; the set of standard sequences corresponding to each type of basic information is stored in the data evaluation module;

[0022] Perform a health assessment process on the set of preprocessed sequences and the set of standard sequences to obtain health monitoring result information.

[0023] The performing data preprocessing on the set of health monitoring time series information to obtain a set of preprocessed sequences includes:

[0024] Perform data cleaning on the set of health monitoring time series information to obtain a first set of sequences;

[0025] Perform data category detection on the first set of sequences to obtain a set of preprocessed sequences.

[0026] In the second aspect of the embodiments of the present invention, a method for user health monitoring for a hospital bed is disclosed, which is implemented by using the user health monitoring device for a hospital bed, and includes:

[0027] S1, use the information collection module to collect the basic information of the user; use the weight measurement module to measure the weight information of the user; use the body fat measurement module to measure the body fat percentage value of the user;

[0028] S2, use the body composition measurement and analysis module to measure the set of body composition analysis information of the user;

[0029] S3, use the data collection module to respectively collect the basic information, weight information, body fat percentage value, and the set of body composition analysis information from the information collection module, the weight measurement module, the body fat measurement module, and the body composition measurement and analysis module, and send the collected basic information, weight information, body fat percentage value, and the set of body composition analysis information to the data evaluation module;

[0030] S4, use the data evaluation module to perform a health monitoring and evaluation process on the basic information, weight information, body fat percentage value, and the set of body composition analysis information to obtain health monitoring result information.

[0031] The data evaluation module performs health monitoring and evaluation processing on the basic information, weight information, body fat percentage value, and human body composition analysis information set to obtain health monitoring result information, including:

[0032] S41. Using the weight information, body fat percentage value, and human body composition analysis information set collected at several moments, construct a health monitoring time series information set; the health monitoring time series information set includes a weight sequence, a body fat percentage sequence, a total body water value sequence, a total protein value sequence, a total inorganic salt value sequence, a right upper muscle weight sequence, a left upper muscle weight sequence, a right lower muscle weight sequence, a left lower muscle weight sequence, and an extracellular water ratio value sequence;

[0033] S42. Perform data preprocessing on the health monitoring time series information set to obtain a preprocessing sequence set;

[0034] S43. According to the basic information, determine the corresponding standard sequence set; the standard sequence set includes a weight standard sequence, a body fat percentage standard sequence, a total body water value standard sequence, a total protein value standard sequence, a total inorganic salt value standard sequence, a right upper muscle weight standard sequence, a left upper muscle weight standard sequence, a right lower muscle weight standard sequence, a left lower muscle weight standard sequence, and an extracellular water ratio value standard sequence;

[0035] S44. Perform health evaluation processing on the preprocessing sequence set and the standard sequence set to obtain health monitoring result information.

[0036] The performing data preprocessing on the health monitoring time series information set to obtain a preprocessing sequence set includes:

[0037] Perform data cleaning processing on the health monitoring time series information set to obtain a first sequence set;

[0038] Perform data category detection processing on the first sequence set to obtain a preprocessing sequence set.

[0039] The performing health evaluation processing on the preprocessing sequence set and the standard sequence set to obtain health monitoring result information includes:

[0040] Perform first evaluation processing on the weight sequence and the body fat percentage sequence in the preprocessing sequence set to obtain a first evaluation result value;

[0041] Perform second evaluation processing on the total body water value sequence, the total protein value sequence, the total inorganic salt value sequence, the right upper muscle weight sequence, the left upper muscle weight sequence, the right lower muscle weight sequence, the left lower muscle weight sequence, and the extracellular water ratio value sequence in the preprocessing sequence set to obtain a second evaluation result value;

[0042] Perform weight calculation processing on the set of preprocessing sequences to obtain a first weight value and a second weight value;

[0043] Use the first weight value and the second weight value to perform weighted summation processing on the first evaluation result value and the second evaluation result value to obtain health monitoring result information.

[0044] The expression of the first evaluation process is:

[0045]

[0046] a i = sin(|w 0i - w i | / |w 0i + w i |) + |exp(w i / w 0 ) - t1| / |w i + w 0 |,

[0047]

[0048] where q i is the i-th item of the weight sequence in the set of preprocessing sequences, r i is the i-th item of the cross-correlation sequence between the weight sequence and the weight standard sequence in the set of preprocessing sequences, q 0i is the i-th item of the weight standard sequence, ρ 1 and ρ 2 are respectively a preset first constant factor and a second constant factor, v i is the i-th item of the weight evaluation sequence, w 0i is the i-th item of the body fat percentage standard sequence, w i is the i-th item of the body fat percentage sequence in the set of preprocessing sequences, w 0 is the mean of all elements of the body fat percentage standard sequence, t1 is a preset third constant factor, a i is the i-th item of the body fat percentage evaluation sequence, and pj1 is the first evaluation result value.

[0049] The beneficial effects of the present invention are:

[0050] Relying on the existing hospital bed environment, the present invention has developed a dedicated user health monitoring device and method to achieve comprehensive monitoring of the user's health status; especially for critically ill patients, the present invention uses the hospital bed platform to continuously monitor their health status, which is of great significance for their treatment and rehabilitation processes.

[0051] By setting up an information collection module, a weight measurement module, a body fat measurement module, and a human body composition measurement and analysis module, the present invention realizes continuous monitoring of multi-source health information of users; through a data evaluation module, the user's health monitoring information is divided into two categories: weight information and composition information, and different evaluation models are designed to monitor these two types of information separately; after obtaining the monitoring results, weight values are calculated for these two types of information respectively to obtain different weight values, and the accurate fusion of these two types of information is achieved by using the weight values, realizing the accurate evaluation of the user's health status. Description of the Drawings

[0052] Figure 1 It is a composition diagram of the device of the present invention;

[0053] Figure 2 It is an implementation flowchart of the method of the present invention. Detailed Embodiment

[0054] To better understand the content of the present invention, an embodiment is given here.

[0055] Figure 1 It is a composition diagram of the device of the present invention; Figure 2 It is an implementation flowchart of the method of the present invention.

[0056] In the first aspect of the embodiment of the present application, a user health monitoring device for a hospital bed is disclosed, including: an information collection module, a weight measurement module, a body fat measurement module, a human body composition measurement and analysis module, a data collection module, and a data evaluation module;

[0057] The information collection module is used to collect the basic information of the user; the basic information includes age, gender, disease information, surgical information, etc.; the disease information includes information about the diseases suffered, and the surgical information includes whether surgery has been performed and the type of surgery information;

[0058] The weight measurement module is used to measure the weight information of the user;

[0059] The body fat measurement module is used to measure the body fat percentage value of the user;

[0060] The human body composition measurement and analysis module is used to measure the human body composition analysis information set of the user; the human body composition analysis information set includes the total body water value, total protein value, total inorganic salt value, right upper muscle weight, left upper muscle weight, right lower muscle weight, left lower muscle weight, and extracellular water ratio value;

[0061] The data acquisition module is respectively connected to the weight measurement module, the body fat measurement module, and the body composition measurement and analysis module, and is used to collect weight information, body fat percentage values, and a set of body composition analysis information, and send the collected weight information, body fat percentage values, and the set of body composition analysis information to the data evaluation module;

[0062] The data evaluation module is connected to the information acquisition module and the data acquisition module, and is used to perform health monitoring and evaluation processing on the basic information, weight information, body fat percentage values, and the set of body composition analysis information to obtain health monitoring result information;

[0063] The data acquisition module collects corresponding information from the information acquisition module, the weight measurement module, the body fat measurement module, and the body composition measurement and analysis module;

[0064] The weight measurement module is arranged on the upper surface of the bed board of the hospital bed;

[0065] Before use, the weight measurement module needs to be calibrated to record the weight of the bedding on the bed board of the hospital bed and subtract the weight in subsequent measurements.

[0066] The weight measurement module can be implemented by using a weight measurement sensor;

[0067] The body fat measurement module can be implemented by using a body fat measurement device;

[0068] The body composition measurement and analysis module can be implemented by using a body composition analyzer.

[0069] The body fat measurement module includes measurement electrodes and a data processing sub-module; the measurement electrodes are connected to the data processing sub-module and are used to collect the skin electrical signals of the user; the data processing sub-module is used to process the collected skin electrical signals of the user to obtain the body fat percentage value of the user; the measurement electrodes include electrode bodies and mounting brackets, and the electrode bodies are mounted on the mounting brackets; the mounting brackets are mounted at the middle positions on both sides of the upper surface of the bed board of the hospital bed. Specifically, it can be the position corresponding to the user's hand when the user lies flat on the bed board.

[0070] The body composition measurement and analysis module includes measurement electrodes, a mounting bracket, and a data analysis sub-module;

[0071] The measurement electrodes are mounted on the mounting bracket;

[0072] The installation bracket is installed at the middle positions on both sides of the upper surface of the hospital bed board and the middle position at the bottom of the upper surface of the bed board; the measurement electrode is connected to the data analysis sub-module and is used to collect the skin electrical signals of the user; the data analysis sub-module is used to process the collected skin electrical signals of the user to obtain a set of body composition analysis information;

[0073] The data evaluation module is used to perform health monitoring and evaluation processing on the weight information, body fat percentage value, and set of body composition analysis information to obtain health monitoring result information, including:

[0074] The data evaluation module constructs a set of health monitoring time series information by using the weight information, body fat percentage value, and set of body composition analysis information collected at several moments; the set of health monitoring time series information includes a weight series, a body fat percentage series, a total body water value series, a total protein value series, a total inorganic salt value series, a right upper muscle weight series, a left upper muscle weight series, a right lower muscle weight series, a left lower muscle weight series, and an extracellular water ratio value series;

[0075] Perform data preprocessing on the set of health monitoring time series information to obtain a set of preprocessing sequences;

[0076] According to the basic information, determine the corresponding set of standard sequences; the set of standard sequences includes a weight standard sequence, a body fat percentage standard sequence, a total body water value standard sequence, a total protein value standard sequence, a total inorganic salt value standard sequence, a right upper muscle weight standard sequence, a left upper muscle weight standard sequence, a right lower muscle weight standard sequence, a left lower muscle weight standard sequence, and an extracellular water ratio value standard sequence; the set of standard sequences corresponding to each type of basic information is stored in the data evaluation module;

[0077] Perform health evaluation processing on the set of preprocessing sequences and the set of standard sequences to obtain health monitoring result information;

[0078] The performing data preprocessing on the set of health monitoring time series information to obtain a set of preprocessing sequences includes:

[0079] Perform data cleaning processing on the set of health monitoring time series information to obtain a first set of sequences;

[0080] Perform data category detection processing on the first set of sequences to obtain a set of preprocessing sequences;

[0081] The performing health evaluation processing on the set of preprocessing sequences and the set of standard sequences to obtain health monitoring result information includes:

[0082] Perform a first evaluation process on the weight sequence and body fat percentage sequence in the preprocessing sequence set to obtain a first evaluation result value;

[0083] Perform a second evaluation process on the total body water value sequence, total protein value sequence, total inorganic salt value sequence, right upper muscle weight sequence, left upper muscle weight sequence, right lower muscle weight sequence, left lower muscle weight sequence, and extracellular water ratio value sequence in the preprocessing sequence set to obtain a second evaluation result value;

[0084] Perform a weight calculation process on the preprocessing sequence set to obtain a first weight value and a second weight value;

[0085] Use the first weight value and the second weight value to perform a weighted summation process on the first evaluation result value and the second evaluation result value to obtain health monitoring result information;

[0086] The process of using the first weight value and the second weight value to perform a weighted summation process on the first evaluation result value and the second evaluation result value is to multiply the first weight value by the first evaluation result value, multiply the second weight value by the second evaluation result value, and add the results of the two multiplications to obtain health monitoring result information;

[0087] The expression of the first evaluation process is:

[0088]

[0089] a i = sin(|w 0i - w i | / |w 0i + w i |) + |exp(w i / w 0 ) - t1| / |w i + w 0 |,

[0090]

[0091] where q i is the i-th item of the weight sequence in the preprocessing sequence set, r i is the i-th item of the cross-correlation sequence between the weight sequence and the weight standard sequence in the preprocessing sequence set, q 0i is the i-th item of the weight standard sequence, ρ 1 and ρ 2 are the preset first constant factor and second constant factor respectively, v i is the i-th item of the weight evaluation sequence, w 0i is the i-th item of the body fat percentage standard sequence, w iis the i-th item of the body fat percentage sequence in the set of preprocessing sequences, w 0 is the mean of all elements of the body fat percentage standard sequence, t1 is a preset third constant factor, a i is the i-th item of the body fat percentage evaluation sequence, and pj1 is the first evaluation result value.

[0092] The expression of the second evaluation process is:

[0093] Using the total body water value sequence, total protein value sequence, total inorganic salt value sequence, right upper muscle weight sequence, left upper muscle weight sequence, right lower muscle weight sequence, left lower muscle weight sequence, and extracellular water ratio value sequence in the set of preprocessing sequences as row vectors, a body composition measurement matrix is constructed; the row vectors of the body composition measurement matrix are the total body water value sequence, total protein value sequence, total inorganic salt value sequence, right upper muscle weight sequence, left upper muscle weight sequence, right lower muscle weight sequence, left lower muscle weight sequence, and extracellular water ratio value sequence in the set of preprocessing sequences;

[0094] Using the total body water value standard sequence, total protein value standard sequence, total inorganic salt value standard sequence, right upper muscle weight standard sequence, left upper muscle weight standard sequence, right lower muscle weight standard sequence, left lower muscle weight standard sequence, and extracellular water ratio value standard sequence, a body composition standard matrix is constructed;

[0095] Multiply the body composition measurement matrix by the transposed matrix of the body composition standard matrix to obtain a correlation matrix;

[0096] Perform a decomposition process on the correlation matrix to obtain an eigenvector matrix and an eigenvalue matrix;

[0097] Discriminate each eigenvalue to determine whether the eigenvalue is greater than a preset eigenvalue threshold to obtain a discrimination result value; determine all eigenvalues with discrimination results greater than as the first eigenvalues;

[0098] Using the eigenvectors corresponding to all the first eigenvalues, a first eigenmatrix is constructed; the row vectors of the first eigenmatrix are the eigenvectors corresponding to the first eigenvalues;

[0099] Using the mean of each row vector of the first eigenmatrix and performing a weighted sum with the first eigenvalue corresponding to the row vector to obtain a second evaluation result value;

[0100] The weighted sum process of the second evaluation result value is:

[0101]

[0102] where pj2 is the second evaluation result value, aa iis the i-th first eigenvalue, bb i is the mean of the row vector corresponding to the i-th first eigenvalue, and M1 is the number of first eigenvalues;

[0103] The weight calculation process for the preprocessing sequence set to obtain the first weight value and the second weight value includes:

[0104] Performing statistical analysis on the weight sequence and the body fat percentage sequence respectively to obtain the corresponding mean and variance;

[0105] Performing weight calculation on the mean and variance of the weight sequence and the body fat percentage sequence to obtain the corresponding first weight value z1;

[0106] The calculation expression of the first weight value is:

[0107] z1 = tan|(μ 1 μ 2 ) / (σ 1 σ 2 )|,

[0108] where μ 1 and μ 2 are the means of the weight sequence and the body fat percentage sequence respectively, and σ 1 and σ 2 are the variances of the weight sequence and the body fat percentage sequence respectively;

[0109] Using the total body water value sequence, total protein value sequence, total inorganic salt value sequence, right upper muscle weight sequence, left upper muscle weight sequence, right lower muscle weight sequence, left lower muscle weight sequence and extracellular water ratio value sequence in the preprocessing sequence set as row vectors to construct a body composition measurement matrix;

[0110] Performing second weight value calculation on the body composition measurement matrix to obtain the second weight value;

[0111] The expression of the second weight value calculation process is:

[0112]

[0113] where A1 ij is the element in the i-th row and j-th column of the body composition measurement matrix, A2 ij is the element in the i-th row and j-th column of the body composition standard matrix, N2 and M2 are the row dimension and column dimension of the body composition standard matrix respectively, and z2 is the second weight value;

[0114] The decomposition process can be eigenvalue decomposition;

[0115] Performing decomposition processing on the correlation matrix to obtain an eigenvector matrix and an eigenvalue matrix includes:

[0116] K = UΛU T ,

[0117] where K is the correlation matrix, U is the eigenvector matrix, and Λ is the eigenvalue matrix;

[0118] Specifically, the middle position at the bottom of the upper surface of the bed board corresponds to the position of the user's feet when the user lies flat on the bed board; the middle positions on both sides of the upper surface of the bed board correspond to the positions of the user's hands when the user lies flat on the bed board.

[0119] The standard sequence is randomly constructed by taking values from the value range of each type of measurement.

[0120] The health monitoring result information is used to characterize the user's health condition, and the smaller its value, the healthier the user.

[0121] The data cleaning process includes filling in missing values, smoothing noisy data, and smoothing or deleting outlier points; for smoothing the noisy data, first identify the noisy data, and then smooth the noisy data according to the data before and after the noisy data; the noisy data is the value that is less than the detection sensitivity of the sensor for the observed data or greater than the measurement upper limit of the sensor for the observed data. The outlier points can be identified using the Kalman filtering method. For determining the filling value of the missing value, the average of the measurement values within a certain sampling interval before and after the missing value can be taken.

[0122] Performing data category detection processing on the first sequence set to obtain a preprocessed sequence set includes:

[0123] Judging whether the category of the data in each sequence in the first sequence set is consistent with the data type of the sequence, and deleting the inconsistent data from the sequence;

[0124] After completing the category judgment for all sequences, a preprocessed sequence set is obtained.

[0125] In the second aspect of the embodiments of the present application, a method for monitoring the health of a user for a hospital bed is disclosed, which is implemented using the device for monitoring the health of a user for a hospital bed, and includes:

[0126] S1, using the information collection module to collect the basic information of the user; using the weight measurement module to measure the weight information of the user; using the body fat measurement module to measure the body fat percentage value of the user;

[0127] S2, using the body composition measurement and analysis module to measure the body composition analysis information set of the user;

[0128] S3. Using the data acquisition module, respectively collect basic information, weight information, body fat percentage value, and human body composition analysis information set from the information acquisition module, weight measurement module, body fat measurement module, and human body composition measurement and analysis module, and send the collected basic information, weight information, body fat percentage value, and human body composition analysis information set to the data evaluation module;

[0129] S4. Using the data evaluation module, perform health monitoring and evaluation processing on the basic information, weight information, body fat percentage value, and human body composition analysis information set to obtain health monitoring result information.

[0130] The step of using the data evaluation module to perform health monitoring and evaluation processing on the basic information, weight information, body fat percentage value, and human body composition analysis information set to obtain health monitoring result information includes:

[0131] S41. Using the weight information, body fat percentage value, and human body composition analysis information set collected at several moments, construct a health monitoring time series information set; the health monitoring time series information set includes a weight sequence, a body fat percentage sequence, a total body water value sequence, a total protein value sequence, a total inorganic salt value sequence, a right upper muscle weight sequence, a left upper muscle weight sequence, a right lower muscle weight sequence, a left lower muscle weight sequence, and an extracellular water ratio value sequence;

[0132] S42. Perform data preprocessing on the health monitoring time series information set to obtain a preprocessing sequence set;

[0133] S43. According to the basic information, determine the corresponding standard sequence set; the standard sequence set includes a weight standard sequence, a body fat percentage standard sequence, a total body water value standard sequence, a total protein value standard sequence, a total inorganic salt value standard sequence, a right upper muscle weight standard sequence, a left upper muscle weight standard sequence, a right lower muscle weight standard sequence, a left lower muscle weight standard sequence, and an extracellular water ratio value standard sequence;

[0134] S44. Perform health evaluation processing on the preprocessing sequence set and the standard sequence set to obtain health monitoring result information.

[0135] The step of performing data preprocessing on the health monitoring time series information set to obtain a preprocessing sequence set includes:

[0136] Perform data cleaning processing on the health monitoring time series information set to obtain a first sequence set;

[0137] Perform data category detection processing on the first sequence set to obtain a preprocessing sequence set;

[0138] Performing a health assessment process on the preprocessed sequence set and the standard sequence set to obtain health monitoring result information, including:

[0139] Performing a first evaluation process on the weight sequence and the body fat percentage sequence in the preprocessed sequence set to obtain a first evaluation result value;

[0140] Performing a second evaluation process on the total body water value sequence, the total protein value sequence, the total inorganic salt value sequence, the right upper muscle weight sequence, the left upper muscle weight sequence, the right lower muscle weight sequence, the left lower muscle weight sequence, and the extracellular water ratio value sequence in the preprocessed sequence set to obtain a second evaluation result value;

[0141] Performing a weight calculation process on the preprocessed sequence set to obtain a first weight value and a second weight value;

[0142] Using the first weight value and the second weight value to perform a weighted summation process on the first evaluation result value and the second evaluation result value to obtain health monitoring result information;

[0143] The process of using the first weight value and the second weight value to perform a weighted summation process on the first evaluation result value and the second evaluation result value is to multiply the first weight value by the first evaluation result value, multiply the second weight value by the second evaluation result value, and add the two resulting products to obtain health monitoring result information;

[0144] The expression of the first evaluation process is:

[0145]

[0146] a i = sin(|w 0i - w i | / |w 0i + w i |) + |exp(w i / w 0 ) - t1| / |w i + w 0 |,

[0147]

[0148] where q i is the i-th item of the weight sequence in the preprocessed sequence set, r i is the i-th item of the cross-correlation sequence between the weight sequence in the preprocessed sequence set and the weight standard sequence, q 0i is the i-th item of the weight standard sequence, ρ 1 and ρ 2 are the preset first constant factor and second constant factor respectively, vi is the i-th item of the weight evaluation sequence, w 0i is the i-th item of the body fat percentage standard sequence, w i is the i-th item of the body fat percentage sequence in the set of preprocessing sequences, w 0 is the mean value of all elements of the body fat percentage standard sequence, t1 is a preset third constant factor, a i is the i-th item of the body fat percentage evaluation sequence, pj1 is the first evaluation result value.

[0149] The expression of the second evaluation process is:

[0150] Using the total body water value sequence, total protein value sequence, total inorganic salt value sequence, right upper muscle weight sequence, left upper muscle weight sequence, right lower muscle weight sequence, left lower muscle weight sequence, and extracellular water ratio value sequence in the set of preprocessing sequences as row vectors, a body composition measurement matrix is constructed; the row vectors of the body composition measurement matrix are the total body water value sequence, total protein value sequence, total inorganic salt value sequence, right upper muscle weight sequence, left upper muscle weight sequence, right lower muscle weight sequence, left lower muscle weight sequence, and extracellular water ratio value sequence in the set of preprocessing sequences;

[0151] Using the total body water value standard sequence, total protein value standard sequence, total inorganic salt value standard sequence, right upper muscle weight standard sequence, left upper muscle weight standard sequence, right lower muscle weight standard sequence, left lower muscle weight standard sequence, and extracellular water ratio value standard sequence, a body composition standard matrix is constructed;

[0152] Multiply the body composition measurement matrix by the transposed matrix of the body composition standard matrix to obtain a correlation matrix;

[0153] Decompose the correlation matrix to obtain an eigenvector matrix and an eigenvalue matrix;

[0154] Discriminate each eigenvalue to determine whether the eigenvalue is greater than a preset eigenvalue threshold to obtain a discrimination result value; determine all eigenvalues with discrimination results greater than as the first eigenvalues;

[0155] Using the eigenvectors corresponding to all the first eigenvalues, a first eigenmatrix is constructed; the row vectors of the first eigenmatrix are the eigenvectors corresponding to the first eigenvalues;

[0156] Using the mean value of each row vector of the first eigenmatrix and performing weighted summation with the first eigenvalue corresponding to the row vector to obtain a second evaluation result value;

[0157] The weighted summation process of the second evaluation result value is:

[0158]

[0159] Among them, pj2 is the second evaluation result value, aa i is the i-th first eigenvalue, bb i is the mean of the row vector corresponding to the i-th first eigenvalue, and M1 is the number of first eigenvalues;

[0160] Performing weight calculation processing on the preprocessing sequence set to obtain a first weight value and a second weight value includes:

[0161] Performing statistical analysis processing on the weight sequence and the body fat percentage sequence respectively to obtain the corresponding mean and variance;

[0162] Performing weight calculation processing on the mean and variance of the weight sequence and the body fat percentage sequence to obtain the corresponding first weight value z1;

[0163] The calculation expression of the first weight value is:

[0164] z1 = tan|(μ 1 μ 2 ) / (σ 1 σ 2 )|,

[0165] Among them, μ 1 and μ 2 are the means of the weight sequence and the body fat percentage sequence respectively, and σ 1 and σ 2 are the variances of the weight sequence and the body fat percentage sequence respectively;

[0166] Using the total body water value sequence, total protein value sequence, total inorganic salt value sequence, right upper muscle weight sequence, left upper muscle weight sequence, right lower muscle weight sequence, left lower muscle weight sequence and extracellular water ratio value sequence in the preprocessing sequence set as row vectors to construct a body composition measurement matrix;

[0167] Performing second weight value calculation processing on the body composition measurement matrix to obtain a second weight value;

[0168] The expression of the second weight value calculation processing is:

[0169]

[0170] Among them, A1 ij is the element in the i-th row and j-th column of the body composition measurement matrix, A2 ij is the element in the i-th row and j-th column of the body composition standard matrix, N2 and M2 are the row dimension and column dimension of the body composition standard matrix respectively, and z2 is the second weight value;

[0171] The decomposition process may be an eigenvalue decomposition process;

[0172] The decomposition process of the correlation matrix to obtain an eigenvector matrix and an eigenvalue matrix includes:

[0173] K = UΛU T ,

[0174] where K is the correlation matrix, U is the eigenvector matrix, and Λ is the eigenvalue matrix;

[0175] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A user health monitoring device for a hospital bed, characterized in that: include: Information collection module, weight measurement module, body fat measurement module, human body composition measurement and analysis module, data collection module, data evaluation module; The information collection module is used to collect basic information of the user; the basic information includes age, gender, disease information, and surgery information; The weight measurement module is used to measure and obtain the weight information of the user; The body fat measurement module is used to measure the body fat percentage of the user; The human body composition measurement and analysis module is used to measure and obtain a human body composition analysis information set of the user; the human body composition analysis information set includes a total body water value, a total protein value, a total inorganic salt value, a right upper muscle weight, a left upper muscle weight, a right lower muscle weight, a left lower muscle weight and an extracellular water ratio value; The data acquisition module is connected to the information acquisition module, the weight measurement module, the body fat measurement module, and the human body composition measurement and analysis module, respectively, and is used to acquire basic information, weight information, body fat percentage value, and human body composition analysis information set, and send the acquired basic information, weight information, body fat percentage value, and human body composition analysis information set to the data evaluation module; The data evaluation module is connected to the information acquisition module and the data acquisition module, and is used to perform health monitoring evaluation processing on the basic information, weight information, body fat percentage value, and human body composition analysis information set to obtain health monitoring result information.

2. The user health monitoring device for a hospital bed according to claim 1, characterized in that: The weight measurement module is arranged on the upper surface of the bed board of the hospital bed; The body fat measurement module includes a measuring electrode and a data processing submodule; the measuring electrode is connected to the data processing submodule and is used to collect the user's skin electrical signals; the data processing submodule is used to perform body fat percentage measurement processing on the collected skin electrical signals of the user to obtain the user's body fat percentage value; the measuring electrode includes an electrode body and a mounting bracket, and the electrode body is mounted on the mounting bracket; the mounting bracket is installed in the middle position of both sides of the upper surface of the bed board of the bed.

3. The user health monitoring device for a hospital bed according to claim 1, characterized in that: The human body composition measurement and analysis module comprises a measurement electrode, a mounting bracket and a data analysis submodule; The measuring electrode is mounted on the mounting bracket; The mounting bracket is installed at the middle position of both sides of the upper surface of the bed board and the middle position of the bottom of the upper surface of the bed board; the measuring electrode is connected to the data analysis submodule to collect the user's skin electrical signals; the data analysis submodule is used to perform human body composition analysis on the collected user's skin electrical signals to obtain a human body composition analysis information set.

4. The user health monitoring device for a hospital bed according to claim 1, characterized in that: The data evaluation module is used to perform health monitoring evaluation processing on the basic information, weight information, body fat percentage value, and human body composition analysis information set to obtain health monitoring result information, including: The data evaluation module constructs a health monitoring time series information set using the weight information, body fat percentage value, and human body composition analysis information set collected at several moments; the health monitoring time series information set includes a weight sequence, a body fat percentage sequence, a total body water value sequence, a total protein value sequence, a total inorganic salt value sequence, a right upper muscle weight sequence, a left upper muscle weight sequence, a right lower muscle weight sequence, a left lower muscle weight sequence, and an extracellular water ratio value sequence; Performing data preprocessing on the health monitoring time series information set to obtain a preprocessed sequence set; According to the basic information, a corresponding standard sequence set is determined; the standard sequence set includes a weight standard sequence, a body fat percentage standard sequence, a body total water value standard sequence, a total protein value standard sequence, a total inorganic salt value standard sequence, a right upper muscle weight standard sequence, a left upper muscle weight standard sequence, a right lower muscle weight standard sequence, a left lower muscle weight standard sequence and an extracellular water ratio value standard sequence; the standard sequence set corresponding to each type of basic information is stored in the data evaluation module; Perform health assessment processing on the pre-processing sequence set and the standard sequence set to obtain health monitoring result information.

5. The user health monitoring device for a hospital bed according to claim 4, characterized in that: The data preprocessing of the health monitoring time series information set to obtain a preprocessed sequence set includes: Performing data cleaning processing on the health monitoring time series information set to obtain a first sequence set; Perform data category detection processing on the first sequence set to obtain a preprocessed sequence set.

6. A user health monitoring method for a hospital bed, characterized in that: The method is implemented by using the user health monitoring device for a hospital bed according to any one of claims 1 to 5, comprising: S1, using the information collection module to collect basic information of the user; using the weight measurement module to measure the weight information of the user; using the body fat measurement module to measure the body fat percentage of the user; S2, using the body composition measurement and analysis module to measure and obtain a body composition analysis information set of the user; S3, using the data acquisition module to respectively acquire basic information, weight information, body fat percentage value and human body composition analysis information set from the information acquisition module, weight measurement module, body fat measurement module and human body composition measurement and analysis module, and sending the acquired basic information, weight information, body fat percentage value and human body composition analysis information set to the data evaluation module; S4, using the data evaluation module, performing health monitoring evaluation processing on the basic information, weight information, body fat percentage value, and human body composition analysis information set to obtain health monitoring result information.

7. The user health monitoring method for a hospital bed according to claim 6, characterized in that: The data evaluation module is used to perform health monitoring evaluation processing on the basic information, weight information, body fat percentage value, and human body composition analysis information set to obtain health monitoring result information, including: S41, constructing a health monitoring time series information set using the weight information, body fat percentage value, and human body composition analysis information set collected at several moments; the health monitoring time series information set includes a weight sequence, a body fat percentage sequence, a total body water value sequence, a total protein value sequence, a total inorganic salt value sequence, a right upper muscle weight sequence, a left upper muscle weight sequence, a right lower muscle weight sequence, a left lower muscle weight sequence, and an extracellular water ratio value sequence; S42, performing data preprocessing on the health monitoring time series information set to obtain a preprocessed sequence set; S43, determining a corresponding standard sequence set according to the basic information; the standard sequence set includes a weight standard sequence, a body fat percentage standard sequence, a body total water value standard sequence, a total protein value standard sequence, a total inorganic salt value standard sequence, a right upper muscle weight standard sequence, a left upper muscle weight standard sequence, a right lower muscle weight standard sequence, a left lower muscle weight standard sequence, and an extracellular water ratio value standard sequence; S44, performing health assessment processing on the pre-processing sequence set and the standard sequence set to obtain health monitoring result information.

8. The user health monitoring method for a hospital bed according to claim 7, characterized in that: The data preprocessing of the health monitoring time series information set to obtain a preprocessed sequence set includes: Performing data cleaning processing on the health monitoring time series information set to obtain a first sequence set; Perform data category detection processing on the first sequence set to obtain a preprocessed sequence set.

9. The user health monitoring method for a hospital bed according to claim 7, characterized in that: The health assessment process is performed on the pre-processing sequence set and the standard sequence set to obtain health monitoring result information, including: Performing a first evaluation process on the weight sequence and the body fat percentage sequence in the preprocessing sequence set to obtain a first evaluation result value; Performing a second evaluation process on the body total water value sequence, total protein value sequence, total inorganic salt value sequence, right upper muscle weight sequence, left upper muscle weight sequence, right lower muscle weight sequence, left lower muscle weight sequence and extracellular water ratio value sequence in the preprocessing sequence set to obtain a second evaluation result value; Performing weight calculation processing on the preprocessing sequence set to obtain a first weight value and a second weight value; The first evaluation result value and the second evaluation result value are weighted and summed using the first weight value and the second weight value to obtain health monitoring result information.

10. The user health monitoring method for a hospital bed according to claim 9, characterized in that: The expression of the first evaluation process is: and i =sin(|in 0i -In i | / |in 0i +in i |)+|exp(in i / w0)-t1| / |w i +w0|, Among them, q i is the i-th item of the weight sequence in the preprocessing sequence set, r i is the i-th item of the cross-correlation sequence between the weight sequence and the weight standard sequence in the pre-processing sequence set, q 0i is the i-th item of the weight standard sequence, ρ1 and ρ2 are respectively the preset first constant factor and the second constant factor, v i is the i-th item in the weight evaluation sequence, w 0i is the i-th item in the standard sequence of body fat percentage, w i is the i-th item of the body fat percentage sequence in the preprocessing sequence set, w0 is the mean of all elements of the body fat percentage standard sequence, t1 is the preset third constant factor, a i is the i-th item in the body fat percentage evaluation sequence, and pj1 is the first evaluation result value.

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