Abstract:
A state classifier uses learning obtained from a plurality of training algorithms each adapted to differentiate between states of physical orientation of an object in response to input data from an tri-axial accelerometer. At least two of the training algorithms are trained using data from an accelerometer mounted at a non-ideal angle. The classifier is trained to distinguish between the desired states from data collected from an tri-axial accelerometer device mounted at a plurality of respective angles with respect to a optimal axis on the object, wherein the angles are in the range of −180 degrees to +180 degrees. The classifier may include a plurality of classifiers and a decision fusion module used to combine the decisions from the respective classifiers to ascertain a state.

Description:
TECHNICAL FIELD 
   The disclosed subject matter relates generally to accelerometer devices, and more particularly to accelerometer devices used to determine the position or orientation of an object or person. 
   BACKGROUND 
   An accelerometer is a device for measuring the total specific external force on a sensor. This is sometimes referred to as the acceleration. A DC-coupled accelerometer sitting still on a table top has zero acceleration but will read the acceleration due to earth&#39;s gravity at that location, which is nominally one g. Accelerometers are thus useful in a wide variety of applications, including inertial navigation systems or for measuring acceleration due to gravity (inclination). 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1   a  illustrates a schematic block diagram of a sedentary state classifier device according to the inventive subject matter disclosed herein. 
       FIG. 1   b  illustrates the orientation of the axis of a tri-axial accelerometer according to the inventive subject matter disclosed herein. 
       FIG. 1   c  illustrates a perspective view of a classifier device mounted on a human subject and corresponding accelerometer axis assumptions according to the inventive subject matter disclosed herein. 
       FIGS. 2   a ,  2   b ,  3   a  and  3   b  illustrate mount angles of various degrees of error for a classifier device according to the inventive subject matter disclosed herein. 
       FIGS. 4   a  and  4   b  illustrate the tilt angles of the accelerometer and the gravitational vector for sitting and standing positions. 
       FIGS. 5   a  and  5   b  illustrate single and multiple classifier embodiments of a classifier device according to the inventive subject matter disclosed herein. 
       FIG. 6  illustrates a training and classification model for a multiple classifier system according to the inventive subject matter disclosed herein. 
       FIG. 7  illustrates a confusion matrix table for the classification model of  FIG. 6  according to the inventive subject matter disclosed herein. 
       FIG. 8  illustrates an accuracy and confusion table according to the inventive subject matter disclosed herein. 
   

   DETAILED DESCRIPTION 
   In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the inventive subject matter. However it will be understood by those of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present invention. Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” or the like, refer to the action and/or processes of a computer or computing system, or similar electronic computing device, that manipulates and/or transforms data represented as physical, such as electronic, quantities within the system&#39;s registers and/or memories into other data similarly represented as physical quantities within the system&#39;s memories, registers or other such information storage, transmission or display devices. In addition, the term “plurality” may be used throughout the specification to describe two or more components, devices, elements, parameters and the like. 
   Referring now to  FIG. 1   a  which illustrates a schematic diagram of a state classification device  100  used to classify different states of physical orientation of an object, for example but not limited to the sedentary states of a human subject, and in particular the states of sitting, standing or laying. Classification device  100  includes an accelerometer  102  that outputs measurements to a classifier  104  that in turn produces an output vector  106  that includes state classification data, in this particular example embodiment, sedentary state classification data. In one example embodiment, accelerometer  102  is a tri-axial accelerometer that can be used for the purpose of classification of states, where the z-axis  110  of accelerometer  102  is assumed to be aligned with the gravitational vector  112 , as illustrated in  FIGS. 1   b  and  1   c . The x and y axes  114  and  116 , respectively, are oriented at 90 degree angles with respect to the z axis. In  FIG. 1   c , device  100  is shown mounted on a human subject  130 . In this embodiment, a 90 degree ray  132  shown on protractor  134  is aligned with the gravitational vector and the device  100  is also ideally aligned with the gravitational vector  112 . 
   In use, classification device  100  is used to classify sedentary states, and is attached to a subject&#39;s body  130  and may, in one embodiment, continuously monitor the orientation thereof. In this example embodiment, device  100  may generate periodic measurements that may be correlated with, for example but not by way of limitation, periodic heart rate measurements. This correlation enables analysis of a subject&#39;s cardiac performance in each of the sedentary states identified by the classification device  100 . While the inventive subject matter has been described herein with respect to classifying sedentary states, it will be appreciated that in other embodiments a classification according to the inventive subject matter may classify other states, for example but not limited to walking or running. Further, while device  100  is presented herein as applied to classifying sedentary state, the device and method according to the inventive subject matter is in no way so limited, and may be used to classify any state of physical orientation of any object. 
   As described above, sedentary classification device  100  may include a tri-axial accelerometer. By determining the accelerometer axis angle with respect to the gravitational vector and an assumed axis orientation of the device  100  on the subject, device  100  may distinguish between sedentary level states. This determination is sensitive to the static component of the accelerometer, which is defined by the orientation of the device relative to the gravitational vector. However, because the device assumes a predetermined orientation of the tri-axial accelerometer device on the subject&#39;s body, any change in orientation of the device relative to the subject&#39;s body affects the accuracy of the results. In particular, changes in the mount angle on the subject adversely affect the inferences made as to the sedentary state of the subject. 
   As may be appreciated from  FIGS. 2   a ,  2   b ,  3   a  and  3   b , maintaining the necessary or “ideal” mount angle of device  100  is sometimes difficult due to body movement or the way the device is mounted on the body.  FIGS. 2   a  and  2   b  illustrate possible errors in mount angle of −10 degrees and −15 degrees due to tilting to the left of the device  100  and in turn the accelerometer  102 .  FIGS. 3   a  and  3   b  illustrate possible errors in mount angle of +10 degrees and +15 degrees due to tilting to the right. Any of these mounting angle anomalies can adversely affect the inference of a subject&#39;s sedentary position by classifier  108 .  FIG. 4   a  illustrates the tilt angles of the accelerometer  102  and the gravitational vector for sitting and standing positions.  FIG. 4   b  illustrates the tilt angle between the x-axis and the gravitational vector for devices mounted as for example shown in  FIGS. 2   a ,  2   b ,  3   a  and  3   b . Using these measurements of tilt angle a classifier  108  can infer the sedentary state of a subject. 
   As explained herein below, the device  100  according to the inventive subject matter is adapted to accurately classify sedentary states even in the presence of deviances from the ideal mount angle. According to one example embodiment, classifier  108  may be trained for class boundaries using data collected from all possible orientations of both the mount angle and the sedentary states. The classifier  108  thus has additional knowledge to distinguish between the mount angles and is able to predict the right state, substantially or entirely independent of an ideal mount angle of the device on a subject. 
   According to one example embodiment, classifier  108  is trained offline to differentiate between various states defined in the training regimen based on features passed to it. Various training algorithms for a multi-class problem are discussed in the art and may be used for training purposes. Since learning the class boundaries is sensitive to the data passed to the training algorithm, it is essential to collect data for different mount angles of the device  100  on a subject. The output from the training algorithm may be in the form of a decision tree or a probability distribution, which may be used to build a classifier  108  that distinguishes the states. 
     FIG. 5   a  demonstrates a training approach for a single classifier embodiment  500  of classifier  108  in the solution. In this embodiment, each training module  502  learns to distinguish between the desired states from the data  505   a ,  505   b  and  505   c  collected by mounting the device at an ideal angle and at less than ideal angles. This learning  506  from each training module  502  is fed into the single classifier of classifier  108  and it is used to differentiate between the desired states, i.e., sitting, standing and lying for the sedentary classifier according to the inventive subject matter. The data collection and training can be done in increments of X+/−10 degrees where X ideally starts with 0 degree with respect to gravity. 
   According to another example embodiment  550  illustrated in  FIG. 5   b , classifier  108  is implemented as a multiple classifier solution, wherein the output from each classifier  1 −N can be combined to obtain the correct inference. In this case each classifier  1 −N may be trained for the chosen mount angles as shown in  FIG. 5   a . For example, Classifier  1  may be trained for all positive mount angles (X+N degree) and it predicts the correct state given the accelerometer data for a positive mount angles. Similarly, Classifier  2  may be trained to predict the correct state for negative mount angles (X−N degree). A decision fusion module  108   b  combines the output from each of the classifiers  1 −N for the final prediction. The confusion in decision for each classifier is considered to calculate the confidence level for each state. According to one example embodiment, the output vector  106  includes the confidence levels for all the states after combining the decision from the individual classifiers. The output can also be represented as the probability of each state given the input data. 
   Referring to  FIG. 6 , there is illustrated a training and classification model  600  that combines the decision from the individual classifiers  1 - 5  and leverages the confusion matrix of the individual classifiers  1 - 5  for various inputs to infer the final probability distribution represented in the output vector. The confusion matrix for the combiner is described in Table 1 is illustrated in  FIG. 7 . According to one embodiment, for training purposes the mount angle can be incremented in steps of +/−10 degree. The intermediate states may be X+/−20 degree together with the original cases of X+/−0 degree. Thus the sedentary states are +20 degrees sitting, −20 degrees sitting, 0 degrees sitting, +20 degrees standing, −20 degrees sitting, standing, +20 degrees lying, −20 degrees lying, and lying. The reliability for each state for input data from four different subjects is described in Table 2 illustrated in  FIG. 8 . 
   Thus, according to the multiple classifier embodiments, each model may be trained based on appropriate features from different mount angles to build a classifier that can reliably distinguish between the states it is trained for. The models may be chosen so that they can reliably distinguish the given states, and the confusion can be used by the decision fusion to decide the final probability distribution. 
   Thus, there is described herein a sedentary classification device for classification of the sedentary states of a subject wearing the device. The classification device of the inventive subject matter provides for accurate classification of sedentary states like sitting, standing, and lying has valuable health applications among a wide range of other applications. The classification device may be used to generate data useful, for example, in analyzing cardiac performance, such as heart rate and blood pressure data, or human subjects. Alternatively, the device may be used, for example but not by way of limitation, in monitoring elderly and post-operative patients. 
   Furthermore, while the description herein is directed particularly to the task of sedentary classification, the inventive subject matter is in no way limited. In particular, the inventive subject matter is fully applicable to any classification problem that requires static acceleration or orientation. Accordingly, according to another example embodiment, the above described classification devices are applied to classification of any state of physical orientation using an accelerometer.