Patent Publication Number: US-2019183427-A1

Title: Method and apparatus for patient bed load cell signal monitoring for patient movement classification

Description:
PRIORITY CLAIM 
     This application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/607,557, filed Dec. 19, 2017, which is expressly incorporated by reference herein. 
    
    
     BACKGROUND 
     A patient support apparatus is configured to operate as a sensing device to characterize patient movement by applying a statistical model to real-time data to discriminate the type of movement the patient is making from a predefined set of movements. 
     Known systems employ various sensors to detect the location of a patient on a patient support apparatus and predict patient activities based on real time signals from load sensors of the patient support apparatus. In general, these systems are limited to classifying the in-bed patient activity into two classes: exiting the bed or not. That is to say, other actions like turning over and sitting up in bed are difficult or impossible to be recognized. Thus these undefined actions will quite possibly be misclassified into exiting due to the high sensitivity. False alarms are therefore generated which will not only create unnecessary distractions but also cause false fatigue on the part of caregivers so that critical alarms are likely to be missed by the staff. 
     SUMMARY 
     The present disclosure includes one or more of the features recited in the appended claims and/or the following features which, alone or in any combination, may comprise patentable subject matter. 
     According to the present disclosure, using signals form load sensors on a patient support apparatus, a method combining dynamic principal component analysis (DPCA) and Gaussian mixture model (GMM) is used to model various classifications of patient movements. DPCA is utilized as the first step to describe both static and dynamic characteristics of the serial dependent data. Past values of each variable are taken into consideration because of the autocorrelation. Then principal component analysis is used to extract pivotal information from massive signals to improve the precision of the follow-up modeling. A GMM using the Figueiredo-Jain (FJ) algorithm is established with the data in low-dimensional principal component subspace and to represent different classifications. Then final classification is processed based on posterior probability calculated by Bayes Rule. An alarm will be triggered to alert the nursing staff when a dangerous exiting or other actions deserving special attention are detected. 
     According to one aspect of the present disclosure, a sensing system for detecting and characterizing a patient action comprises a frame, a plurality of load sensors supported from the frame, a patient supporting platform supported from the plurality of load sensors so that the entire load supported on the patient supporting platform is transferred to the plurality of load sensors, and a controller supported on the frame. The controller is electrically coupled to the load sensors and operable to receive a signal from each of the plurality of load sensors with each load sensor signal representative of a load supported by the respective load sensor. The controller includes a processor and a memory device. The memory device includes a non-transitory portion storing instructions that, when executed by the processor, cause the controller to utilize a dynamic principal component analysis and a mixture model to evaluate the temporal distribution of loads sensed by each of the respective load sensors to distinguish the pattern of patient action using real-time monitoring signals to create a model; and monitor the signals from the load sensors to classify the nature of the patient action into a particular one of a plurality of classifications using a probabilistic analysis and modify an operating characteristic of the patient support in response to the particular classification of the patient action. 
     In some embodiments, the mixture model is operable to draw a probabilistic inference about the likelihood of multiple patient actions in real time to characterize the likelihood of any one of the patient actions and thereby distinguish the likely resulting patient action from multiple patient actions indicated by the load sensor data. 
     In some embodiments, the dynamic principal component analysis extracts both static and dynamic relations from the signals. 
     In some embodiments, the mixture model is established by a Gaussian mixture model with a Figueiredo-Jain algorithm. 
     In some embodiments, median filtering is applied to the load signals to remove the random measurement noise. 
     In some embodiments, the load signals are normalized to eliminate the effect of the patient&#39;s weight. 
     In some embodiments, the classification is determined by applying Bayes&#39; Theorem. 
     Additional features, which alone or in combination with any other feature(s), such as those listed above and/or those listed in the claims, can comprise patentable subject matter and will become apparent to those skilled in the art upon consideration of the following detailed description of various embodiments exemplifying the best mode of carrying out the embodiments as presently perceived. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description particularly refers to the accompanying figures in which: 
         FIG. 1  is a perspective view from the foot end on the patient&#39;s right of a patient support apparatus; 
         FIG. 2  is a block diagram of a portion of the electrical system of the patient support apparatus of  FIG. 1  used to determine a tare weight of the patient support apparatus; 
         FIG. 3  is a diagrammatic representation of the positions of a number of load cells relative to the patient support apparatus of  FIG. 1 ; 
         FIGS. 4A-4F  are charts illustrating signals from multiple sensors during specific patient movements; 
         FIGS. 5A and 5B  are charts illustrating the activities of  FIGS. 4C and 4E , respectively, as processed by dynamic expansion according to the present disclosure; and 
         FIG. 6  is flowchart illustrating the steps used to implement the algorithm of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     An illustrative patient support apparatus  10  embodied as a hospital bed is shown in  FIG. 1 . The patient support apparatus  10  of  FIG. 1  has a fixed bed frame  20  which includes a stationary base frame  22  with casters  24  and an upper frame  26 . The stationary base frame  22  is further coupled to a weigh frame  30  that is mounted via frame member  32   a  and  32   b  to an adjustably positionable mattress support frame or deck  34  configured to support a mattress  18 . The mattress  18  defines a patient support surface  36  which includes a head section  38 , a seat section  40 , and a foot section  42 . The patient support apparatus  10  further includes a headboard  12  at a head end  46  of the patient support apparatus  10 , a footboard  14  at a foot end  48  of the patient support apparatus  10 , and a pair of siderails  16  coupled to the upper frame  26  of the patient support apparatus  10 . The siderail  16  supports a patient monitoring control panel and/or a mattress position control panel  54 . The patient support apparatus  10  is generally configured to adjustably position the mattress support frame  34  relative to the base frame  22 . 
     Conventional structures and devices may be provided to adjustably position the mattress support frame  34 , and such conventional structures and devices may include, for example, linkages, drives, and other movement members and devices coupled between base frame  22  and the weigh frame  30 , and/or between weigh frame  30  and mattress support frame  34 . Control of the position of the mattress support frame  34  and mattress  18  relative to the base frame  22  or weigh frame  30  is provided, for example, by a patient control pendant  56 , a mattress position control panel  54 , and/or a number of mattress positioning pedals  58 . The mattress support frame  34  may, for example, be adjustably positioned in a general incline from the head end  46  to the foot end  48  or vice versa. Additionally, the mattress support frame  34  may be adjustably positioned such that the head section  38  of the patient support surface  36  is positioned between minimum and maximum incline angles, e.g., 0-65 degrees, relative to horizontal or bed flat, and the mattress support frame  34  may also be adjustably positioned such that the seat section  40  of the patient support surface  36  is positioned between minimum and maximum bend angles, e.g., 0-35 degrees, relative to horizontal or bed flat. Those skilled in the art will recognize that the mattress support frame  34  or portions thereof may be adjustably positioned in other orientations, and such other orientations are contemplated by this disclosure. 
     In one illustrative embodiment shown diagrammatically in  FIG. 2 , the patient support apparatus  10  includes a weigh scale module  60  and an alarm system  90 . The weight scale module  60  is configured to determine a plurality set of calibration weights for each of a number of load cells  50  for use in determining a location and an accurate weight of the patient. To determine a weight of a patient supported on the patient support surface  36 , the load cells  50  are positioned between the weigh frame  30  and the base frame  22 . Each load cell  50  is configured to produce a voltage or current signal indicative of a weight supported by that load cell  50  from the weigh frame  30  relative to the base frame  22 . The weigh scale module  60  includes a processor module  62  that is in communication with each of the respective load cells  50 . The processor module  62  includes a microprocessor-based controller  52  having a flash memory unit  64  and a local random-access memory (RAM) unit  66 . The local RAM unit  66  is utilized by the controller  52  to temporarily store information corresponding to features and functions provided by the patient support apparatus  10 . The alarm system  90  is configured to trigger an alarm if the movement of the patient exceeds a predetermined threshold or meets an alarm classification as discussed in further detail below. The alarm may be an audible alarm  92  and/or a visual alarm  94 . The visual alarm  94  may be positioned, for example, on the mattress position control panel  54  and/or the patient control pendant  56 . 
     In the illustrated embodiment of  FIG. 3 , four such load cells  50   a - 50   d  are positioned between the weigh frame  30  and the base frame  22 ; one each near a different corner of the patient support apparatus  10 . All four load cells  50   a - 50   d  are shown in  FIG. 3 . Some of the structural components of the patient support apparatus  10  will be designated hereinafter as “right”, “left”, “head” and “foot” from the reference point of an individual lying on the individual&#39;s back on the patient support surface  36  with the individual&#39;s head oriented toward the head end  46  of the patient support apparatus  10  and the individual&#39;s feet oriented toward the foot end  48  of the patient support apparatus  10 . For example, the weigh frame  30  illustrated in  FIG. 3  includes a head end frame member  30   c  mounted at one end to one end of a right side weigh frame member  30   a  and at an opposite end to one end of a left side frame member  30   b . Opposite ends of the right side weigh frame member  30   a  and the left side weigh frame member  30   b  are mounted to a foot end frame member  30   d . A middle weigh frame member  30   e  is mounted at opposite ends to the right and left side weigh frame members  30   a  and  30   b  respectively between the head end and foot end frame members  30   c  and  30   d . The frame member  32   a  is shown mounted between the right side frame member  30   a  and the mattress support frame  34 , and the frame member  32   b  is shown mounted between the left side frame member  30   b  and the mattress support frame  34 . It will be understood that other structural support is provided between the weigh frame member  30  and the mattress support frame  34 . 
     A right head load cell (RHLC)  50   a  is illustratively positioned near the right head end of the patient support apparatus  10  between a base support frame  44   a  secured to the base  44  near the head end  46  of the patient support apparatus  10  and the junction of the head end frame member  30   c  and the right side frame member  30   a , as shown in the block diagram of  FIG. 2 . A left head load cell (LHLC)  50   b  is illustratively positioned near the left head end of the patient support apparatus  10  between the base support frame  44   a  and the junction of the head end frame member  30   c  and the left side frame member  30   b , as shown in the block diagram of  FIG. 3 . A right foot load cell (RFLC)  50   c  is illustratively positioned near the right foot end of the patient support apparatus  10  between a base support frame  44   b  secured to the base  44  near the foot end  48  of the patient support apparatus  10  and the junction of the foot end frame member  30   d  and the right side frame member  30   a , as shown in the block diagram of  FIG. 3 . A left foot load cell (LFLC)  50   d  is illustratively positioned near the left foot end of the patient support apparatus  10  between the base support frame  44   b  and the junction of the foot end frame member  30   d  and the left side frame member  30   b . In the exemplary embodiment illustrated in  FIG. 3 , the four corners of the mattress support frame  34  are shown extending beyond the four corners of the weigh frame  30 , and hence beyond the positions of the four load cells  50   a - 50   d.    
     A weight distribution of a load among the plurality of load cells  50   a - 50   d  may not be the same depending on sensitivities of each of load cells  50   a - 50   d  and a position of the load on the patient support surface  36 . Accordingly, a calibration constant for each of the load cells  50   a - 50   d  is established to adjust for differences in the load cells  50   a - 50   d  in response to the load. Each of the load cells  50   a - 50   d  produces a signal indicative of the load supported by that load cell  50 . The loads detected by each of the respective load cells  50   a - 50   d  are adjusted using a corresponding calibration constant for the respective load cell  50   a - 50   d . In some embodiments, the adjusted loads are then combined to establish the actual weight supported on the patient support apparatus  10 . As discussed below, the signals from the load cells  50   a - 50   d  may be processed by the processor module  62  to characterize the movement of a patient into one of several classes. Thus, as configured, the bed  10  is operable as a sensor system for detecting and characterizing patient movement to provide information about the patient movement to a user either through an alarm or other communication method. 
     For example, six movements that patients may frequently take are considered by theprocessor module  62  and, when a particular movement is detected with specificity, the processor module  62  will characterize the particular movement and act on that characterization according to pre-defined protocols. The movements characterized in the illustrative embodiment include the patient exiting from the bed  10 , turning over from right to left, turning over from left to right, stretching out for something (e.g. reaching for a glass of water on a nearby table), sitting up and lying down. The first five actions begin with the patients lying flat on the bed  10 , while the last one starts by sitting on the bed  10 . These movements are designate as one of an action class G 1 -G 6 , where: G 1  is patient exiting, G 2  is turning over from right to left, G 3  is turning over from left to right, G 4  is reaching, G 5  is sitting up, and G 6  is lying down. 
     Referring to the flowchart shown in  FIG. 4 , the system of the present disclosure utilizes an algorithm  98  that includes characterization sampling at step  100 , signal processing at step  102 , modeling at step  104 , monitoring patient activity at step  106 , and providing output at step  112 . 
     At the characterization sampling step  100 , test subjects are placed on the bed  10  and prompted to perform the various movements while the signals from each of the load cells  50   a - 50   d  is monitored. Appropriate sampling is implemented so that variations in the speed of movement and the manner in which various individuals execute the movements are monitored to provide a statistically significant sample over a range of patient demographics. 
     Once the characterization sampling step  100  is completed, the collected data is subjected to signal processing at step  102  to simplify the data analysis. For example, the initial signal is subjected to a tare analysis to remove any structure offsets applied to the load cells  50   a - 50   d  such as by the weight of components of the bed  10 . Also, median filtering is applied to the signals to remove any random measurement noise. The signals are also adjusted to a notionally common scale according to Equation (1): 
     
       
         
           
             
               
                 
                   
                     
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     where P 1  through P 4  are referred to herein as filtered force signal produced by the corresponding load cells  50   a - 50   d  for simplicity. G is the total patient weight and is calculated by G=P 1 +P 2 +P 3 +P 4 . x i  is the normalized data that contains all the values falling between 0 and 1, which has eliminated the effect of patient&#39;s weight. Thus, at each time interval t each load cell  50   a - 50   d  signal will have been normalized to a respective unit-less proportion of the total weight detected by all four of the load cells  50   a - 50   d.    
     This signal data is then characterized at step  104  in two sub-steps  108  and  110 . In the illustrative embodiment, step  108  includes signal characteristic by dynamic principle component analysis (DPCA) with each of the signals of the four load cells  50   a - 50   d  being subjected to the DCPA. At step  110 , a Gaussian mixture model is built using an Figueirdo-Jain (FJ) algorithm. 
     The data set from step  102  is deemed to contain redundant information resulting from the constraining relations between the signals of the respective load cells  50   a - 50   d . In this embodiment, extracting key variables and omitting uninformative variables is preferred. Principal component analysis (PCA) is one of the most popular dimension reduction methods. However, for the dynamic system of signal acquisition in bed  10 , the current value of each load cell  50   a - 50   d  partly depends on the past values due to the autocorrelation as described in W. Ku, R. H. Storer, C. Georgakis, “Disturbance detection and isolation by dynamic principal component analysis.”  Chemometrics and intelligent laboratory systems,  30.1 (1995), 179-196, which is incorporated by reference herein for the discussion of autocorrelation of related sensor signals. Conventional PCA can only demonstrate a linear static approximation. Therefore an extended method DPCA is performed in order to reveal both static and dynamic relations. 
     If we denote X(N×J) as the data matrix that is composed of the set of continuous load cell signal values belonging to a specific action, in which J is equal to 4 (the number of load cells  50   a - 50   d ) and N rests with the duration of the action. The vector of the measurement variables at time t is represented as x t  (4×1). The vector mentioned in this disclosure is a column vector when there is no special statement. 
     
       
         
           
             
               
                 
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     To explain the autocorrelation property in the static PCA model, take x t-1  into consideration at time k at least. In other words, in addition to the present one, the variable will be extended with L previous values. The vector x t  (4×1) is replaced by an augmented vector t t (4(L+1)×1), and thus a new data matrix X D  including dynamic behavior of an action in the bed  10  is reconstructed. 
     
       
         
           
             
               
                 
                   
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     By means of orthogonal transformation, linearly uncorrelated information in the corresponding principal component subspace (PCS) is expected to be extracted from X D . The transformation is defined by: 
         Y=X   D   U   (4)
 
     where Y is the output matrix that is composed of uncorrelated variables, in which most of the information of X D  is retained. Taking advantage of some conclusions from linear algebra, u is the transformation matrix consisting of the eigenvectors calculated from S, which is presented by: 
         S=UΛU   T   (5)
 
     where S(4(L+1)×4(L+1)) denotes the covariance matrix of X D . Λ is a diagonal matrix with elements being the eigenvalues (λ 1 , λ 2 , . . . λ k ) in descending order of S and is the covariance matrix of the principal components. 
     The DPCA transformation is defined so that the first principal component corresponding to the largest eigenvalue accounts for the variability of the data as much as possible, while the succeeding component has the second largest eigenvalue, and so on. It&#39;s worth noting that only the first n (n&lt;=k) eigenvalues are non-zero in A due to the redundancy mentioned above, and their corresponding eigenvectors can be used to establish the DPCA model. This has explained the reason why PCA is regarded as an effective method in dimensional reduction. What&#39;s more, usually only m (m&lt;n) principal components are chosen in practical application so as to simplify the data structure. Specifically, the parameter m can be determined by the cumulative contribution rate calculated by 
     
       
         
           
             
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     Then the final output Y(N′×m) of dynamic PCA model is given by: 
         Y=X   D   W   (6)
 
     where W(k×m) is transformation matrix formed by the m chosen eigenvectors. 
     It has been proved that mixture models are often regarded as better choice to represent arbitrarily class-conditional probability density functions (PDFs) than traditional models. And the Gaussian mixture model, as a powerful and flexible probabilistic method, has been successfully used to multivariate and univariate data especially in the area of statistical pattern recognition, where it demonstrates a superior performance in classifying the continuous process data collected from different patterns. Consequently, GMM is adopted here to learn the probability distribution of each action with the data Y gained by the dynamic PCA discussed above. 
     Let Y=[y 1   T  y 2   T  . . . y N′   T ] T  with y of dimension m×1. According to the underlying assumption of GMM, signals from different actions follow different Gaussian distributions with distinct covariances and means. For the signal vector y, it comes from several possible Gaussian distributions and GMM is the mixture of these Gaussian components which can thus be defined as: 
     
       
         
           
             
               
                 
                   
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     where M is the number of mixtures. P i  is the prior probability, or sometimes called weight of the ith Gaussian component. P i  must satisfy a constraint that 
     
       
         
           
             
               
                 
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     P i ≥0, i=1, 2, . . . , M. p(y|θ i ) denotes the conditional probability density function, in which θ i  is the set of parameters defining that corresponding component that consists of 2 elements: the covariance matrix Σ i  and the mean vector μ i . All the components are assumed to follows normal distributions, and hence p(y|θ i ) is given by: 
     
       
         
           
             
               
                 
                   
                     
                       
                         
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     The accuracy of the model is directly related to the component number M. That is to say, if M is small, GMM may not meet the demanded accuracy of modeling. However, the computational complexity will increase dramatically when M is increasing. The selection of M is actually a trade-off between the precision of the model and amount of computation. The conventional GMM method always sets M by experience and M is fixed in different classes. 
     In this disclosure, M, as well as other parameters (P,θ) are estimated by Figueiredo-Jain (FJ) algorithm which is proposed based on the standard expectation and maximization algorithm. E-step and M-step are alternately applied to yield the sequence of estimates until the convergence criterion is met. The procedure is iterated as follows: 
     In the E-step, 
     
       
         
           
             
               
                 
                   
                     
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                           θ 
                           i 
                         
                          
                         
                           y 
                           j 
                         
                       
                       ) 
                     
                   
                   = 
                   
                     
                       
                         P 
                         i 
                         r 
                       
                        
                       
                         p 
                          
                         
                           ( 
                           
                             
                               
                                 y 
                                 j 
                               
                                
                               
                                 μ 
                                 i 
                                 r 
                               
                             
                             , 
                             
                               Σ 
                               i 
                               r 
                             
                           
                           ) 
                         
                       
                     
                     
                       
                         ∑ 
                         
                           m 
                           = 
                           1 
                         
                         K 
                       
                        
                       
                           
                       
                        
                       
                         
                           p 
                           m 
                           r 
                         
                          
                         
                           p 
                            
                           
                             ( 
                             
                               
                                 
                                   y 
                                   j 
                                 
                                  
                                 
                                   μ 
                                   m 
                                   r 
                                 
                               
                               , 
                               
                                 Σ 
                                 m 
                                 r 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
     
     where P r  (θ i |y j ) is the posterior probability of the jth training sample produced by the ith Gaussian component. P i   r  is the corresponding prior probability. The superscript r denotes the rth iteration. And K is the possible number of Gaussian component that may change with the iteration times. 
     In the M-step, the parameter estimates are updated according to: 
     
       
         
           
             
               
                 
                   
                     μ 
                     i 
                     
                       r 
                       + 
                       1 
                     
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           j 
                           = 
                           1 
                         
                         
                           N 
                           ′ 
                         
                       
                        
                       
                           
                       
                        
                       
                         
                           
                             P 
                             r 
                           
                            
                           
                             ( 
                             
                               
                                 θ 
                                 i 
                               
                                
                               
                                 y 
                                 j 
                               
                             
                             ) 
                           
                         
                          
                         
                           y 
                           j 
                         
                       
                     
                     
                       
                         ∑ 
                         
                           j 
                           = 
                           1 
                         
                         
                           N 
                           ′ 
                         
                       
                        
                       
                           
                       
                        
                       
                         
                           P 
                           r 
                         
                          
                         
                           ( 
                           
                             
                               θ 
                               i 
                             
                              
                             
                               y 
                               j 
                             
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   10 
                   ) 
                 
               
             
             
               
                 
                   
                     Σ 
                     i 
                     
                       r 
                       + 
                       1 
                     
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           j 
                           = 
                           1 
                         
                         
                           N 
                           ′ 
                         
                       
                        
                       
                           
                       
                        
                       
                         
                           
                             P 
                             r 
                           
                            
                           
                             ( 
                             
                               
                                 θ 
                                 i 
                               
                                
                               
                                 y 
                                 j 
                               
                             
                             ) 
                           
                         
                          
                         
                           ( 
                           
                             
                               y 
                               j 
                             
                             - 
                             
                               μ 
                               i 
                               
                                 r 
                                 + 
                                 1 
                               
                             
                           
                           ) 
                         
                          
                         
                           
                             ( 
                             
                               
                                 y 
                                 j 
                               
                               - 
                               
                                 μ 
                                 i 
                                 
                                   r 
                                   + 
                                   1 
                                 
                               
                             
                             ) 
                           
                           T 
                         
                       
                     
                     
                       
                         ∑ 
                         
                           j 
                           = 
                           1 
                         
                         
                           N 
                           ′ 
                         
                       
                        
                       
                           
                       
                        
                       
                         
                           P 
                           r 
                         
                          
                         
                           ( 
                           
                             
                               θ 
                               i 
                             
                              
                             
                               y 
                               j 
                             
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   11 
                   ) 
                 
               
             
             
               
                 
                   
                     P 
                     i 
                     
                       r 
                       + 
                       1 
                     
                   
                   = 
                   
                     
                       max 
                        
                       
                         { 
                         
                           0 
                           , 
                           
                             
                               ( 
                               
                                 
                                   ∑ 
                                   
                                     j 
                                     = 
                                     1 
                                   
                                   
                                     N 
                                     ′ 
                                   
                                 
                                  
                                 
                                     
                                 
                                  
                                 
                                   
                                     P 
                                     r 
                                   
                                    
                                   
                                     ( 
                                     
                                       
                                         θ 
                                         i 
                                       
                                        
                                       
                                         y 
                                         j 
                                       
                                     
                                     ) 
                                   
                                 
                               
                               ) 
                             
                             - 
                             
                               V 
                               2 
                             
                           
                         
                         } 
                       
                     
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         K 
                       
                        
                       
                           
                       
                        
                       
                         max 
                          
                         
                           { 
                           
                             0 
                             , 
                             
                               
                                 ( 
                                 
                                   
                                     ∑ 
                                     
                                       j 
                                       = 
                                       1 
                                     
                                     
                                       N 
                                       ′ 
                                     
                                   
                                    
                                   
                                       
                                   
                                    
                                   
                                     
                                       P 
                                       r 
                                     
                                      
                                     
                                       ( 
                                       
                                         
                                           θ 
                                           i 
                                         
                                          
                                         
                                           y 
                                           j 
                                         
                                       
                                       ) 
                                     
                                   
                                 
                                 ) 
                               
                               - 
                               
                                 V 
                                 2 
                               
                             
                           
                           } 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   12 
                   ) 
                 
               
             
           
         
       
     
     where μ i   r+1 , Σ i   r+1 , and P i   r+1  are respectively the mean, covariance, and prior probability representing the ith Gaussian component at the (r+1)th iteration that update the parameters got from rth iteration. The covariance matrix Σ has (m 2 +m)/2 parameters as it owns a symmetric structure. And the mean vector μ has m parameters. V is the total number of parameters defining each Gaussian component and is equal to (m 2 +3m)/2 in this paper. 
     Compared with the standard methods, the FJ algorithm is much less initialization dependent, which is able to get the optimal component number of each class by only retaining the non-zero-probability component during the iteration procedure. No prior knowledge is required for determining the specific number. The parameter K is usually set relatively large at first (e.g.  10 ) as it will decrease after iteration and the final value of K is to assigned to the parameter M to form the monitoring model. 
     Step  106  is applied when a patient is in the bed  10  and the signals from the load cells  50   a - 50   d  are monitored so that when a patient movement is detected, it may be classified into one of the six action classes (G 1  through G 6 ). For consistency, observations (continuous signals for a period of time) are denoted by having been processed by the dynamic PCA in advance as Y=[y 1   T  y 2   T  . . . y N′   T ] T , where y t  is a m-dimensional vector representing the expanded load cell signals at sample point t, N′ is the total sample number that is related to the duration of the observation. 
     Taking a single vector y as an example, Bayes&#39; theorem is adopted to determine its possible class, which is defined by the following expression: 
     
       
         
           
             
               
                 
                   
                     
                       P 
                        
                       
                         ( 
                         
                           
                             G 
                             i 
                           
                            
                           y 
                         
                         ) 
                       
                     
                     = 
                     
                       
                         
                           P 
                            
                           
                             ( 
                             
                               y 
                                
                               
                                 G 
                                 i 
                               
                             
                             ) 
                           
                         
                          
                         
                           P 
                            
                           
                             ( 
                             
                               G 
                               i 
                             
                             ) 
                           
                         
                       
                       
                         
                           ∑ 
                           
                             j 
                             = 
                             1 
                           
                           6 
                         
                          
                         
                             
                         
                          
                         
                           
                             P 
                              
                             
                               ( 
                               
                                 y 
                                  
                                 
                                   G 
                                   j 
                                 
                               
                               ) 
                             
                           
                            
                           
                             P 
                              
                             
                               ( 
                               
                                 G 
                                 j 
                               
                               ) 
                             
                           
                         
                       
                     
                   
                   , 
                   
                     
 
                   
                    
                   
                     i 
                     = 
                     1 
                   
                   , 
                   2 
                   , 
                   … 
                   , 
                   6 
                 
               
               
                 
                   ( 
                   13 
                   ) 
                 
               
             
           
         
       
     
     where P(G i |y) is the conditional probability of the event that y belongs to class G i . And P(y|G i ) denotes the probability of observing y when the class G i  is given. P(G i ) is the prior probability that is usually got by experience and large amount of characterization data at step  100 . 
     Considering the fact that the denominator of Eq. (13) is used for normalization as it stays the same no matter which class is chosen, we here omit it and the discrimination function, indicated by h(y), can be deduced by combining the Eqs. (8) and (13). The formula is as follows: 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             h 
                             i 
                           
                            
                           
                             ( 
                             y 
                             ) 
                           
                         
                         = 
                           
                          
                         
                           
                             
                               
                                 - 
                                 
                                   1 
                                   2 
                                 
                               
                                
                               
                                 
                                   ( 
                                   
                                     y 
                                     - 
                                     
                                       μ 
                                       i 
                                     
                                   
                                   ) 
                                 
                                 T 
                               
                                
                               
                                 
                                   
                                     Σ 
                                     i 
                                   
                                   
                                     - 
                                     1 
                                   
                                 
                                  
                                 
                                   ( 
                                   
                                     y 
                                     - 
                                     
                                       μ 
                                       i 
                                     
                                   
                                   ) 
                                 
                               
                             
                             - 
                             
                               
                                 1 
                                 2 
                               
                                
                               ln 
                             
                           
                            
                           
                               
                           
                            
                           
                             
                               Σ 
                               i 
                             
                              
                           
                         
                       
                     
                   
                   
                     
                       
                           
                          
                         
                           
                             
                               - 
                               
                                 1 
                                 2 
                               
                             
                              
                             ln 
                              
                             
                                 
                             
                              
                             2 
                              
                             
                                 
                             
                              
                             π 
                           
                           + 
                           
                             ln 
                              
                             
                                 
                             
                              
                             
                               P 
                                
                               
                                 ( 
                                 
                                   G 
                                   i 
                                 
                                 ) 
                               
                             
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   14 
                   ) 
                 
               
             
           
         
       
     
     where μ i  and Σ i  are the parameters acquired beforehand and formed the known model of action class Gi. 
     Considering the assumption that the prior probabilities P(G 1 ) through P(G 6 ) are equal in the interest of simplicity, h(y) can be further simplified and the final classification rule is expressed according to the maximum a posterior (MAP) criterion. y will be classified to class G k , if: 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             h 
                             k 
                           
                            
                           
                             ( 
                             y 
                             ) 
                           
                         
                         = 
                         
                           
                             max 
                             
                               1 
                               ≤ 
                               i 
                               ≤ 
                               6 
                             
                           
                            
                           
                             
                               h 
                               i 
                             
                              
                             
                               ( 
                               y 
                               ) 
                             
                           
                         
                       
                     
                   
                   
                     
                       
                         = 
                         
                           
                             max 
                             
                               1 
                               ≤ 
                               i 
                               ≤ 
                               6 
                             
                           
                            
                           
                             { 
                             
                               
                                 
                                   
                                     - 
                                     
                                       1 
                                       2 
                                     
                                   
                                    
                                   
                                     
                                       ( 
                                       
                                         y 
                                         - 
                                         
                                           μ 
                                           i 
                                         
                                       
                                       ) 
                                     
                                     T 
                                   
                                    
                                   
                                     
                                       
                                         Σ 
                                         i 
                                       
                                       
                                         - 
                                         1 
                                       
                                     
                                      
                                     
                                       ( 
                                       
                                         y 
                                         - 
                                         
                                           μ 
                                           i 
                                         
                                       
                                       ) 
                                     
                                   
                                 
                                 - 
                                 
                                   
                                     1 
                                     2 
                                   
                                    
                                   
                                       
                                   
                                    
                                   ln 
                                 
                               
                                
                               
                                   
                               
                                
                               
                                 
                                   Σ 
                                   i 
                                 
                                  
                               
                             
                             } 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   15 
                   ) 
                 
               
             
           
         
       
     
     Considering that the human action in general lasts for a period of time, the discrimination function hence needs to be modified to fit the sequence data Y(N′×m). Y will be classified to class G k , if: 
     
       
         
           
             
               
                 
                   
                     
                       H 
                       k 
                     
                      
                     
                       ( 
                       y 
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       
                         N 
                         ′ 
                       
                     
                      
                     
                       
                         ∑ 
                         
                           j 
                           = 
                           1 
                         
                         
                           N 
                           ′ 
                         
                       
                        
                       
                           
                       
                        
                       
                         
                           h 
                           k 
                         
                          
                         
                           ( 
                           
                             y 
                             j 
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   16 
                   ) 
                 
               
             
             
               
                 
                   
                       
                   
                    
                   
                     = 
                     
                       
                         max 
                         
                           1 
                           ≤ 
                           i 
                           ≤ 
                           6 
                         
                       
                        
                       
                         { 
                         
                           
                             1 
                             
                               N 
                               ′ 
                             
                           
                            
                           
                             
                               ∑ 
                               
                                 j 
                                 = 
                                 1 
                               
                               
                                 N 
                                 ′ 
                               
                             
                              
                             
                                 
                             
                              
                             
                               [ 
                               
                                 
                                   
                                     
                                       - 
                                       
                                         1 
                                         2 
                                       
                                     
                                      
                                     
                                       
                                         ( 
                                         
                                           
                                             y 
                                             j 
                                           
                                           - 
                                           
                                             μ 
                                             i 
                                           
                                         
                                         ) 
                                       
                                       T 
                                     
                                      
                                     
                                       
                                         
                                           Σ 
                                           i 
                                         
                                         
                                           - 
                                           1 
                                         
                                       
                                        
                                       
                                         ( 
                                         
                                           
                                             y 
                                             j 
                                           
                                           - 
                                           
                                             μ 
                                             i 
                                           
                                         
                                         ) 
                                       
                                     
                                   
                                   - 
                                   
                                     
                                       1 
                                       2 
                                     
                                      
                                     
                                         
                                     
                                      
                                     ln 
                                   
                                 
                                  
                                 
                                     
                                 
                                  
                                 
                                   
                                     Σ 
                                     i 
                                   
                                    
                                 
                               
                               ] 
                             
                           
                         
                         } 
                       
                     
                   
                 
               
               
                 
                     
                 
               
             
           
         
       
     
     In the illustrative embodiment, the determination of the particular classification as G 1 -G 6  is tested for probability of the determination being a true condition and if the error is sufficiently small, the movement is characterized in the particular classification such that the processor module  62  signals that movement to the alarm system  90  so that a user, such as a nurse, may be notified of the movement and take corrective action. Various corrective actions may be implemented by the user/caregiver/nurse or other systems on the bed  10  may be signaled to initiate a corrective action. For example, portions of the bed  10  may be moved automatically to make the indicated movement easier for the patient. 
     Example 
     As mentioned earlier, 6 kinds of patient activities G 1 -G 6  related to use of the bed  10  are considered in the present disclosure, exiting from the bed (GI), turning over from right to left (G 2 ), turning over from left to right (G 3 ), stretching out for something (G 4 ), sitting up (G 5 ) and lying down (G 6 ). Experimental data was provided by 10 adults age of 22 through 30 and weigh between 45 to 80 kilograms. For the six actions mentioned before, the number of samples for each action is: G 1 =151; G 2 =276; G 3 =316; G 4 =149; G 5 =274; and G 6 =292. The corresponding load cell signals of each activity after filtering, initializing, and normalizing are shown in  FIGS. 5A-5F , respectively. In the illustrative example, the sampling frequency was adjusted to 100 Hz. The abscissa denotes the sample number ordered by sampling time, and the ordinate is the output of value. The fluctuations show the load cells  50   a - 50   d  four respective signals&#39; response to the experimental patient&#39;s movement. The proposed method tends to capture the intrinsic structure of each class. 
     Two movements G 3  and G 5  are compared in  FIGS. 6A and 6B  which illustrates these two kinds of signals shown in  FIGS. 5C and 5E , respectively, processed by the dynamic expansion as it&#39;s expressed in Eq. (3). The parameter L is set to be 2, which means for each load cell  50   a - 50   d , the last 2 signal values are taken into account at the current sampling time, with the purpose of considering the relation within each respective signal. Accordingly, the single observation vector is 12-dimensional instead of the original 4-dimensional. In addition, PCA is further applied to extract the first 4 directions that are most significant to discriminate between different human activities, while the uninformative variables are omitted at the same time. 
     Here, a conventional GMM classification method is also developed for comparison with the proposed method to evaluating the effects of DPCA. The conventional method builds the monitoring model directly using the original 4-dimensional signal data without DPCA. In other words, the relations between and within signals are not involved in modeling. 
     The accuracy of classification is used as an indicator so as to evaluate the performance, and the comparison results are shown in Table I, where for convenience, DPCA+GMM denotes the proposed method and GMM denotes the conventional method. Moreover, the specific classification results are summarized in Table II, in which the number in bold style shows the correct classification number. 
     
       
         
           
               
             
               
                 TABLE I 
               
             
            
               
                   
               
               
                 CLASSIFICATION ACCURACY COMPARISON BETWEEN 
               
               
                 THE TWO METHODS 
               
            
           
           
               
               
            
               
                   
                 Classification Accuracy (%) 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                 Stretch 
                   
                   
               
               
                 Method 
                   
                 Turn over 
                 Turn over 
                 out for 
                   
                 Lie 
               
               
                 Class 
                 Exit 
                 to left 
                 to right 
                 sth. 
                 Sit up 
                 down 
               
               
                   
               
               
                 Proposed method: 
                 60.26 
                 80.43 
                 67.41 
                 88.59 
                 75.55 
                 75.00 
               
               
                 DPCA + GMM 
               
               
                 Conventional 
                 13.91 
                 53.25 
                 12.03 
                 89.93 
                 15.69 
                 75.34 
               
               
                 method: GMM 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
             
               
                 TABLE II 
               
             
            
               
                   
               
               
                 CLASSIFICATION RESULTS USING THE TWO METHODS 
               
            
           
           
               
               
            
               
                   
                 samples 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                 Turn over 
                 Turn over 
                 Stretch out 
                   
                   
               
               
                   
                 Exit 
                 to left 
                 to right 
                 for sth. 
                 Sit up 
                 Lie down 
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                 Classification 
                 DPCA + 
                   
                 DPCA + 
                   
                 DPCA + 
                   
                 DPCA + 
                   
                 DPCA + 
                   
                 DPCA + 
                   
               
               
                 results 
                 GMM 
                 GMM 
                 GMM 
                 GMM 
                 GMM 
                 GMM 
                 GMM 
                 GMM 
                 GMM 
                 GMM 
                 GMM 
                 GMM 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
               
               
               
               
            
               
                 Exit 
                 
                   91 
                 
                 
                   21 
                 
                 10 
                 3 
                 23 
                 3 
                 7 
                 92 
                 10 
                 12 
                 10 
                 20 
               
               
                 Turn over to left 
                 4 
                 1 
                 
                   222 
                 
                 
                   147 
                 
                 4 
                 0 
                 3 
                 44 
                 23 
                 26 
                 20 
                 58 
               
               
                 Turn over to right 
                 10 
                 0 
                 25 
                 5 
                 
                   213 
                 
                 
                   38 
                 
                 41 
                 164 
                 12 
                 25 
                 15 
                 84 
               
               
                 Stretch out 
                 2 
                 2 
                 0 
                 1 
                 12 
                 7 
                 
                   132 
                 
                 
                   134 
                 
                 1 
                 1 
                 2 
                 4 
               
               
                 Sit up 
                 5 
                 0 
                 23 
                 12 
                 13 
                 14 
                 5 
                 43 
                 
                   207 
                 
                 
                   43 
                 
                 21 
                 162 
               
               
                 Lie down 
                 11 
                 0 
                 17 
                 5 
                 11 
                 22 
                 12 
                 22 
                 22 
                 21 
                 
                   219 
                 
                 
                   222 
                 
               
               
                   
               
            
           
         
       
     
     It can be seen from the result tables that confusions are likely to happen among exiting, turning over from left to right and stretching. It can be partly explained by the experimental conditions in the lab in that a bedside table is positioned on the right. A test subject is deemed to complete a stretching action when he has got some documents or drinks from the table. In addition, most test subjects participating in the research are accustomed to exiting the bed  10  from the right side. The above three kinds of actions thus share a similar trend of signals for the first half of the movement as they are all moving towards the right side. In that case, the probability of each class is approximately equal. The deficiency is more obvious in the conventional method, and the disclosed method has significantly improved it. 
     It is clear that the disclosed method has illustrated a superior classification performance, especially for the actions deserving more attention including exiting and turning over. The conventional methods only achieve slightly higher accuracy in the classes of stretching and lying down. To be more specific, for the proposed method, 213 samples are correctly classified among a total of 316 samples when the volunteer is turning over from right to left, reaching accuracy as high as 67.41%. Nevertheless, the accuracy is down to 12.03% with the conventional method, in which only 38 samples are successfully distinguished. As for the exiting action, the proposed method achieves a correct number of 91, compared with  21  from the other method. The conventional method has greater possibility in classifying an unauthorized exit into stretching or any other actions, which may put the patient in a dangerous situation. 
     The disclosed approach is a data-driven human activity classification method based on load cell signals for a hospital bed. By combining the two multivariate statistical analysis algorithms, dynamic PCA and GMM, dynamic correlations are taken into account, which has significantly increased the precision of modeling. The final classification process is implemented by Bayes&#39; theorem to calculate the probability of each class. In comparison with the conventional method, the disclosed method turns out to be more effective in recognizing the six kinds of common actions G 1 -G 6  for patients on the hospital bed. 
     Although this disclosure refers to specific embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made without departing from the subject matter set forth in the accompanying claims.