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 fig1 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 and in use and for the purpose of classification of states the z - axis 110 of accelerometer 102 is assumed to be aligned with the gravitational vector 112 , as illustrated in fig1 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 fig1 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 fig2 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 . fig2 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 . fig3 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 . fig4 a illustrates the tilt angles of the accelerometer 102 and the gravitational vector for sitting and standing positions . fig4 b illustrates the tilt angle between the x - axis and the gravitational vector for devices mounted as for example shown in fig2 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 . fig5 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 fig5 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 fig5 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 fig6 , 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 fig7 . 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 fig8 . 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 .
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 .