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Estimation of Body Postures on Bed Using Unconstrained ECG Measurements Hong Ji Lee, Su Hwan Hwang, Seung Min Lee, Yong Gyu Lim, and Kwang Suk Park, Senior Member, IEEE

Abstract—We developed and tested a system for estimating body postures on a bed using unconstrained measurements of electrocardiogram (ECG) signals using 12 capacitively coupled electrodes and a conductive textile sheet. Thirteen healthy subjects participated in the experiment. After detecting the channels in contact with the body among the 12 electrodes, the features were extracted on the basis of the morphology of the QRS (Q wave, R wave, and S wave of ECG) complex using three main steps. The features were applied to linear discriminant analysis, support vector machines with linear and radial basis function (RBF) kernels, and artificial neural networks (one and two layers), respectively. SVM with RBF kernel had the highest performance with an accuracy of 98.4% for estimation of four body postures on the bed: supine, right lateral, prone, and left lateral. Overall, although ECG data were obtained from few sensors in an unconstrained manner, the performance was better than the results that have been reported to date. The developed system and algorithm can be applied to the obstructive apnea detection and analyses of sleep quality or sleep stages, as well as body posture detection for the management of bedsores. Index Terms—Bedsore, body posture detection, capacitive sensors, QRS morphology.

I. INTRODUCTION RESSURE sores are common clinical problems for the patients in nursing homes and hospitals. Bedsores are areas of damaged skin caused by prolonged stays in the same posture for a long time. More precisely, continuous and repetitive pressure on a bony prominence leads to the necrosis of the skin due to lack of blood circulation in soft tissue [1], [2]. Groups such as unconscious patients, cerebral or spinal injury patients, critical patients, frail elderly patients, bed blockers, and bedridden patients are usually at high risk for bedsores. To reduce the risk of developing bedsore, it is necessary to relieve the skin pressure by changing the patient’s body position every 2 h [1], [3]. National surveys of pressure ulcer prevalence have been conducted

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Manuscript received August 29, 2012; revised November 19, 2012 and January 29, 2013; accepted March 11, 2013. Date of publication March 15, 2013; date of current version November 12, 2013. This work was supported by the National Research Foundation of Korea (NRF), Korea Government [Ministry of Education, Science and Technology (MEST)] under Grant 2012010714. H. J. Lee and S. H. Hwang are with the Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 110-799, Korea (e-mail: [email protected]; [email protected]). S. M. Lee is with the Department of Biomedical Engineering, College of Health Science, Korea University, Seoul 136-703, Korea (e-mail: [email protected]). Y. G. Lim is with the Department of Oriental Biomedical Engineering, College of Health Science, Sangji University, Wonju 220-702, Korea (e-mail: [email protected]). K. S. Park is with the Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul 110-799, Korea (e-mail: [email protected]). Digital Object Identifier 10.1109/JBHI.2013.2252911

in several countries. Roughly, 412 000 people develop a new pressure sore every year in U.K. [4]. In the United States, the total number of inpatients with pressure ulcers among adults 18 years and older increased 78.9% from 1993 to 2006 [5] and the prevalence rate was 12.3% in combined care settings in 2009 [6]. The average result of a survey on the prevalence rate of bedsores among patients in 25 hospitals from Belgium, Italy, Portugal, the United Kingdom, and Sweden was 18.1% [7]. In the United States, the expense of treating pressure ulcers was estimated at $11 billion in 2006 [5]. The length of hospital stays, as well as the hospital costs, for treating bedsores has increased, whereas there was an increasing shortage of nurses. The body posture of patients with sleep disorders is also of interest. Sleep-related disorders have emerged as one of the serious problems in the modern societies, and the number of people suffering from such disorders is increasing every year. There are many kinds of sleep-related disorders, such as sleep apnea, insomnia, snoring, etc. [8]. Obstructive sleep apnea (OSA) is the most common type of sleep apnea, and it is caused by blocking of the upper airway. Patients with OSA repeatedly stop breathing for over 10 s during sleep. However, almost 85% of the people with OSA remain undiagnosed [9]. Patients with severe OSA have a higher risk of developing cardiovascular diseases if they do not receive proper medical treatment [10]. Many studies show that sleep apnea patients often prefer to sleep in a supine posture and that apnea occurs more frequently and severely in the supine posture than in other postures [11]–[13]. Therefore, it is important for sleep apnea patients to detect and change their body postures during sleep to prevent sleeping in a supine posture for a long time. We proposed a body posture estimation algorithm based on the QRS complex of ECG measured capacitively from 12 channels on a bed. Body postures were classified into four categories—supine, prone, right lateral, and left lateral— because we measured ECG under the upper body, regardless of the location of arms and legs. The feature sets were applied to linear discriminant analysis (LDA), support vector machines (SVMs), and artificial neural networks (ANNs). The algorithm was customized for all subjects, and the performance results of classifiers were compared to find suitable feature sets and classifiers. The main contributions of this paper are: 1) to establish a novel body posture detection algorithm based on ECG; 2) to use an unconstrained system that consists of one component modality on a bed; 3) to find the features sets that can be applied for all subjects without any calibration; 4) to compare the performances with five classifiers, LDA, SVM (linear and radial basis function (RBF) kernels), and ANN (two layers and one layer); and 5) to use relatively less sensors and less features. The rest of the paper is organized as follows. Section II summarizes the related works on classification of the sleep/body

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posture detection. Section III describes our unconstrained ECG system and the experiment protocol. Section IV presents data analysis method, data sets for classifiers, and statistical analyses for the performance evaluation. Section V proposes feature extraction method for the body posture detection and also introduces classifier models. Section VI summarizes the performances of body posture estimation on each classifier. Section VII discusses the experiment analysis and performance evaluation. Finally, Section VIII presents the conclusion of this paper.

II. RELATED WORKS Many approaches for determining a person’s body posture on a bed have been studied. Most of them used pressure sensor pads. Hsia et al. [1], [3] developed a mattress pad with a force-sensing resistor (FSR) that monitored changes in body pressure on the pad. As a further study, Yousefi et al. [14] proposed a pressure-mapping system with 2048 sensors and Huang et al. [15] introduced a multimodal system that included a 60-sensor FSR map and video image for posture classification. The advantage of these systems was that users do not need to wear any device. However, these systems needed high dimension and long processing time for image process and needed many sensors to improve performance. Ni et al. [16] combined Flexiforce sensor (FFS) with ultrawideband (UWB) tags where location was calculated by UWB sensors. A two-array FFS matrix (32 pressure sensors) was placed on a bed and 6 UWB tags were embedded in the pajama. Even though they achieved high accuracy, the main limitation was that users should wear the UWB-tag-embedded shirt whenever they sleep. In addition, two systems were integrated all the time to determine the body posture on the bed. Other method used an accelerometer sensor to detect postures. Chang and Liu [8] introduced a triaccelerometer modality for body posture detection. Although they took into account the prone posture, it was difficult to obtain accurate measurements of the prone posture because the accelerometer device was in the hard case on a chest belt. A second limitation of the system was that the location of the sensor could shift to an inappropriate place during sleep. To overcome the limitations, Borazio and Laerhoven [17] used a wrist-worn sensor that recorded threedimensional (3-D) acceleration for night segmentation, posture clustering, and myoclonic twitch detection. The sleep postures were modeled by a clustering method and the postures were represented by a color-code map. Since it was a wearable sensor, subjects should recharge the battery about every 2 weeks. Moreover, this system had big data processing due to the color-code map. Hoque et al. [18] used three RFID-based 3-D accelerometer tags that were placed near the subject on a bed. They extracted features from x-, y-, and z-axis values of each tag. Although this developed system was an unconstrained bed-related system like our system, they installed the tags for only purpose of posture estimation. Another approach was based on the fact that the QRS complex changes as body positions change, because the body position influenced the heart’s position. This, in turn, affects the body-surface electrocardiogram (ECG) [19]–[21]. The largest barrier to developing a practical ECG-sensing application to detect body postures is that users must be in direct and con-

tinuous contact with gel-type electrodes, as in the studies to date that have been performed with a standard 12-lead ECG measurement system. Yang et al. [22] developed a wearable system that included eight textile sensors embedded on a shirt to measure ECG. The main disadvantage of this system was that users should wear the certain shirt during sleep. Furthermore, since the control box was attached on the belly and the conductive fiber was put in the shirt, it could be uncomfortable for users. Lim et al. [23] also made eight active electrodes to measure ECG capacitively on a bed. Yang et al. [22] and Lim et al. [23] reported that ECG waveform was changed with different postures. However, they did not take a step further to find features and establish any algorithm for determination of different postures. Using ECG data is a more effective and competitive method than other sensors mentioned earlier because while the ECG measurement system basically measures ECG, it can provide the body posture and much more diagnostic information to users, such as arrhythmia, analyses of OSA severity, sleep stages, and sleep quality by the R–R intervals of the ECG as well as respiration detection with ECG-derived respiration. Therefore, we adapted the unconstrained ECG measurement system developed by Lim et al. [23] to find the best feature sets and establish algorithm for the body posture estimation. In summary, the advantages of our method are: 1) the system can be applied for multiple subjects without individual calibration; 2) users do not care about the battery because our system used power adaptor; 3) users do not need to wear any devices on their body; and 4) the system is made up one modality and fewer sensors.

III. MATERIALS AND METHODS A. System 1) Twelve Capacitively Coupled Electrodes: The active electrode developed by Lim et al. [23] was used. The thickness was adjusted to 0.6 cm, and the surface area of the electrode was set to 25 cm2 (5 cm × 5 cm). This increased the signalto-noise (SNR) ratio because impedance formed between the body and the electrode decreases as the surface area of the electrode increases. A total of 12 capacitively coupled electrodes (CC electrodes) were placed in a horizontal arrangement on the bed to measure various positions and postures. A holder was designed to arrange the wires and to prevent electrodes from sinking or rising when the patient position changed. Electrodes were fixed in the holder at the intervals of 3 cm to reduce interaction between them. The length of the holder was 94 cm, which was shorter than that of a general mattress. The holder was recessed into the upper part of mattress so as not to protrude. 2) Conductive Textile Sheet (Capacitive Ground): A conductive textile was used as a reference electrode to measure the electrical potential and as a ground to reduce common-mode noise. The textile connected to the system’s ground was laid on the lower part of the bed, beneath the subject’s legs. The conductive sheet covered as much of the lower part of the mattress as possible, because the impedance could be decreased by increasing the contact area. Moreover, the large size of the textile ensured that the body was always connected to the ground, regardless of the body posture.

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Fig. 1. Block diagram of an analog-signal processing module and data acquisition system. CC Electrode: capacitively coupled electrode; Fc: cutoff frequency; and Fs: sampling frequency.

3) Analog-Signal Processing and Data Acquisition: Fig. 1 shows a diagram of the signal processing module and data acquisition system. The hardware filter was a finite-impulse response (FIR) Sallen–Key Butterworth filter with a range of 0.5–35 Hz, because ECG signals are mostly in that frequency domain. The overall system’s high-pass cutoff frequency was 0.5 Hz since the high-pass cutoff frequency of our capacitive electrodes was the same as that of the analog module. The total gain was 100 V/V. An MP 150 module and AcqKnowledge software (BIOPAC Systems, Inc., Goleta, CA, USA) were used for data acquisition. Each channel was converted by 16-bit analog-to-digital converter at a sample rate of 500 Hz.

B. Experiment A total of 13 healthy subjects (11 males, 2 females) aged 28.08 ± 3.20 years participated in the experiment. They wore cotton clothes and lay on the bed. Every experiment was conducted in a laboratory. One cycle included four body postures: supine posture (Sp ), prone posture (Pp ), right lateral posture (Rp ), and left lateral posture (Lp ). The sequence was randomly decided for each subject. Each posture was held for 5 min, before a supervisor verbally instructed the subject to move to the next posture. The subject was allowed about 30–60 s to change posture and find a comfortable position or posture. After the subject’s movement became fewer, another posture was held for another 5 min. After finishing one cycle, a second cycle that had a different order of postures from the first cycle was conducted for reproducibility. For instance, subject 13 lay successively as follows: Lp , Pp , Rp , Sp (first cycle), and Pp , Sp , Rp , and Lp (second cycle). An additional experiment was also conducted using Ag/AgCl electrodes to verify the ECG signals measured from our system. For the four postures, Ag/AgCl electrodes were attached to the same part of the body where CC electrodes were contacted when lying on our developed system. For example, Ag/AgCl electrodes were attached on the subject’s left side (+) and on the left side of the left leg (– and ground) for Lp . As shown in Fig. 2, QRS complexes from both measurement devices had the same pattern of waveform for the same posture, regardless of the amplitude of the morphology.

Fig. 2. ECG morphologies measured with (solid line) CC electrodes and (reference, dashed line) Ag/AgCl electrodes for subject 5 in: (a) supine posture, (b) right lateral posture, (c) prone posture, and (d) left lateral posture. Similar waveforms were seen for the same posture. FL a m p , M a m p , and BL a m p are the amplitudes of FL, M , and BL peaks, respectively.

IV. DATA ANALYSIS A. Digital-Signal Processing Off-line analysis was done with MATLAB software (MathWorks, Natick, MA, USA). The measured ECG signals were filtered from 0.5 to 35 Hz once more. Those electrodes (of the 12 channels) in contact with the body were manually detected. 1) Data division: The whole recorded data were divided into eight sections (two cycles) and each section had data for 5 min. The time index of each section varied from subject to subject. Data of each section were collected every 30 s and represented as a mean value of the period because it was pointless to estimate body posture every second. In other words, the data collection for a 5-min period resulted in ten mean values per feature. 2) Peak-detection algorithm: Maximum peaks (M peaks) were detected by the lab-developed algorithm [24] and checked manually once more. Front low peaks (FL peaks) were detected by finding the minimum value between the 15 samples before M peak and the index of M peak. Similarly, back low peaks (BL peaks) were detected by finding the minimum value between the index of M peak and the 15 samples after M peak.

B. Training, Validation, and Testing Sets Leave-one-out cross-validation (LOOCV) was applied to the formation of training and testing sets because of the small amount of data. Therefore, the training set was composed of the data from 12 subjects and the testing set was composed of the data of the remaining subject. The process was repeated 12 times (for a total of 13) until the data from each subject were used once as the testing set. For classifiers that needed a validation set like an SVM and ANN, tenfold cross-validation was used. In other words, 10% of the training set was randomly chosen as validation data each time. The distribution of samples in the training, validation, and testing sets depending on classifiers is summarized in Table I.

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TABLE I NUMBER OF TRAINING, VALIDATION, AND TESTING SETS IN CLASSIFIERS

C. Statistical Analyses Statistical analyses between reference postures and the outputs of our algorithm were performed. The classification performances were evaluated by two indices (accuracy [(TP+TN)/(TP+FP+FN+TN)] and kappa statistic). TP, TN, FP, and FN stand for “true positive,” “true negative,” “false positive,” and “false negative,” respectively. The kappa statistic measures the difference between the observed agreement and the expected agreement. The kappa value in the range from 0.61 to 0.80 means substantial agreement and that in the range from 0.81 to 0.99 indicates almost perfect agreement [25]. V. FEATURE EXTRACTION AND CLASSIFIERS A. Feature Extractions 1) Three Steps for Four Body Postures: Posture estimation progressed through three steps: the Sp Rp /Pp Lp , Sp /Rp , and Pp /Lp division steps. The sample number of the Sp /Rp step and the Pp /Lp step is half that of the Sp Rp /Pp Lp step as shown in Table I, because the Sp /Rp step and the Pp /Lp step are analyzed separately after going through the Sp Rp /Pp Lp step. In the Sp Rp /Pp Lp division step, four postures were divided into two groups: one included the supine and right lateral postures (Sp Rp ), and the other included the prone and left lateral postures (Pp Lp ). For this step, the average signal of the ECGs from the contacted electrodes was calculated. As shown in the ECG morphologies in Fig. 2, the amplitudes of the FL peaks and the BL peaks (FLam p and BLam p ) were significantly different between the two groups. Therefore, after the FL, M, and BL peaks of every ECG morphology were detected, the FLam p , Mam p , and BLam p were selected as features for the Sp Rp /Pp Lp division step. Their distributions are shown in Fig. 3(a). The two groups (Sp Rp and Pp Lp ) were separated by the left and right sides of the dashed line in Fig. 3(a). The Sp /Rp division step represented the separation of the supine and the right lateral postures. Despite ECG data measured on the same supine posture, the overall amplitude of the ECG waveform could decrease or increase at different recording times owing to the impedance difference between the electrodes and the body. To compensate for this, the ratios of each FLam p , Mam p , and BLam p were used in this step and named     as FLam p , Mam p , and BLam p . To get the FLam p , Mam p , and  BLam p , first, the electrodes of opposite ends among the channels used in the Sp Rp /Pp Lp division step were found. Second, the FLam p , Mam p , and BLam p of each ECG signal from the opposite ends of the channels were detected. Finally, the ratios of each FLam p , Mam p , and BLam p detected from the two

Fig. 3. Distributions of features: (a) FL a m p , M a m p , and BL a m p in S p R p /P p L p step, (b) FL a m p , M a m p , and BL a m p in S p /R p step, and (c) FL a m p , M a m p , and BL a m p in P p /L p step. Samples were taken every 30 s. The dashed line separates different postures on the left-hand and righthand sides, as shown by the different values for the same feature.

channels were calculated. For example, if channels 4, 5, and 6 were in contact with the body at the same time when it was in a supine posture, each Mam p detected from the ECG of channel   4 was divided by the Mam p of channel 6 (Mam p ). The FLam p  and BLam p were calculated by the same method as the Mam p. Provided that the number of contacted electrodes was one, the same FLam p , Mam p , and BLam p used in the Sp Rp /Pp Lp separation step were used, because the amplitude ratio could not be calculated. The amplitude having the biggest difference between the supine and the right lateral postures was FLam p , as   shown in Fig. 3(b). The values of FLam p , Mam p , and BLam p were chosen as the features for the Sp /Rp division step.

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are closest to the separating hyperplane; they have a significant impact on the decision regarding the location of the decision boundary. An equation for the decision hyperplane is given by  class 1, d(x) > 0 T x= (2) d(x) = w x + b, class 2, d(x) < 0

Fig. 4. Mean amplitude of FL a m p , M a m p , and BL a m p for two groups in: (a) S p R p /P p L p step, (b) S p /R p step, and (c) P p /L p step. Error bar indicates standard deviation of the mean. ∗ p-value < 0.001.

The Pp /Lp division step meant the classification of the prone and the left lateral postures. In the step, values of the FLam p , Mam p , and BLam p that were the same as those in the Sp Rp /Pp Lp step were used. Hence, three features, which were the values of FLam p , Mam p , and BLam p were extracted for the last step. Fig. 4 illustrated mean amplitude and standard deviation of FLam p , Mam p , and BLam p for two groups in each step. There were statistically significant differences between two groups for all used features (p-value < 0.001). 2) Normalization: The values of each feature were normalized to have zero mean and unit variance before applying to classifiers. For this, after the mean and standard deviation of each feature were calculated, the values of each feature subtracted its mean value and then each feature having zero mean was divided by its standard deviation. B. Classifier Models The normalized feature set in each division step was applied to LDA, SVM, and ANN, respectively, and the performances of the classifiers were compared. 1) Linear Discriminant Analysis: LDA is a method used in pattern recognition and machine learning to find a linear combination of variables that separates two classes. Classes are linearly discriminated using the largest difference in mean scores for the classes (between-class scatter) and the smallest overlap between the discriminant score distributions (withinclass scatter). The discriminant score y is a weighted linear combination calculated upon this principle and expressed as follows: y = wT x

where w is the weight vector and b is the bias. The values of w and b that maximize the margin between the parallel hyperplanes are found through a learning process. Apart from performing linear classification, SVM is effectively applied to the classes that cannot be divided linearly. In other words, by way of a kernel, the data map into a higher dimensional space where they can be separated linearly. Therefore, it is important to consider kernel function and parameters related to the kernel in order to avoid an overfitting or underfitting problem. In this paper, linear and RBF kernels were used as kernel functions of the SVM. The linear kernel parameter C is adjusted as a reasonable value for the soft margin. In the RBF kernel, it was necessary to consider a particular value of (σ, C), where σ is the degree of the flexible decision boundary and C is the soft margin constant. Therefore, kernel parameters were determined by validation set as follows: C = 1.3 in the SVM with the linear kernel and σ = 1 and C = 1 in the SVM with the RBF kernel for the Sp Rp /Pp Lp step, C = 1 in the SVM with the linear kernel and σ = 1.2 and C = 1 in the SVM with the RBF kernel for the Sp /Rp step, and C = 1 in the SVM with the linear kernel, and σ = 0.8 and C = 1.5 in the SVM with the RBF kernel for the Pp /Lp step. After choosing proper kernel parameters with the best tenfold cross-validation accuracy, the final classifier was constructed using the training and the validation sets with the kernel parameters [27], [28]. 3) Artificial Neural Networks: ANNs are computational models inspired by models of the biological neural networks in the brain. The name refers to the interconnection between the different layers of neurons, the weights of the interconnections, and the activation function for outputs. For the data that are not linearly separable, a multilayer perceptron (MLP) is introduced as an extended version of a single-layer perceptron. MLP is a feedforward neural network that uses backpropagation for training. It includes input, hidden, and output layers. The hidden and output layers sequentially execute two arithmetic operations, which are the weighted sum of the input and the sigmoid activation function. Connection weights are changed by a learning process until the mean squared error (MSE) between the outputs and the expected results is the lowest. The equation for MSE is given by 1  (tk − ok )2 m m

E=

(3)

k =1

(1)

where x is the number of samples and w is the projection axis. Therefore, a point to be considered as a measure of the success of the method is the degree of overlap between the discriminant score distributions [26]. 2) Support Vector Machines: An SVM is a supervised learning model that constructs hyperplanes with the largest margin between two classes. Support vectors are lists of values that

where m is the number of data, t the expected output, and o is the output from MLP [26]. In this study, the neural network had three layers (input, hidden, and output) and the scaled conjugate gradient backpropagation was chosen for training algorithm. The activation function for the hidden layer and for the output layer was a hyperbolic tangent sigmoid transfer function. Two-layer ANN had two-dimensional binary output vectors and one-layer ANN had

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Fig. 5. Flowchart for classification of four body postures on a bed. The algorithm first divides the four body postures into two groups based on the FL a m p , M a m p , and BL a m p . One group includes the data for the supine and right lateral postures. The other group includes data for the prone and left lateral postures. In the second step, the supine and right lateral postures are estimated by the values of FL a m p , M a m p , and BL a m p that are extracted from the proportion of features in the two channels. The FL a m p , M a m p , and BL a m p , which are the same as in the S p R p /P p L p step, are used for classification between the prone and left lateral postures. TABLE II ALGORITHM PERFORMANCES OF EACH CLASSIFIER WITH BEST FEATURE SETS IN EACH DIVISION STEP

four-dimensional output vectors. The number of neurons in the single hidden layer was adjusted to be 7 for the two-layer ANN and 11 for the one-layer ANN based on validation data. The MSE was used as a performance function (MATLAB’s Neural Network Toolbox). Because one-layer ANN makes multiple decisions, the feature sets extracted in each step were simultaneously put into the input of ANN for classifying four body postures at the same time. Since the neural network starts with random initial weights and biases, the results differ slightly every time ANN is run. Therefore, after repeating the run ten times, the average performance was calculated. The flow of feature extractions and classifiers for the estimation of four body postures on a bed is shown in Fig. 5. VI. RESULTS The performance was determined by the classifier evaluation with the testing set after the data set that included the training set and the validation set was used for training the classifier. Table II summarizes the performance of the developed algorithm for each classifier in classifying four body postures. The final performances were based on the feature sets that had the best training accuracy in each division step. If several parameter sets had same accuracy of training performance in the same step, the lowest number of feature combinations was chosen. In terms of SVM with RBF kernel, all features, FLam p , Mam p , and BLam p , were finally selected as the best feature set for Sp Rp /Pp Lp division step. FLam p for Sp /Rp step and FLam p , Mam p , and BLam p for Pp /Lp division step were chosen. The confusion matrices for each classifier are also shown in Table III. All the correct predictions were located in the diagonal of the table. The predicted values of one-layer ANN had decimal points because

the results were average values of ten times. The accuracies and kappa values in Table II were calculated from the confusion matrices in Table III. The SVM with RBF kernel had the highest performance at 98.4% accuracy and 0.967 kappa value. The next best one was the SVM with linear kernel, at 98.3% accuracy and 0.965 kappa value. Although one-layer ANN showed relatively low performance, all classifiers had almost perfect agreement. To provide another point of view and to give better results, after the classifier with the best performance in each division step was chosen from among LDA and SVM with linear or RBF kernel, the performances of the classifiers were finally combined together. As shown in Table IV, a different classifier was selected for each division step. For example, SVM with RBF kernel was found to be the best classifier in the Sp /Rp division step since the accuracy of the SVM with RBF kernel (99.6%) was better than that of the other classifiers (LDA: 97.7%, SVM with linear kernel: 99.2%). Fig. 6 shows hyperplane of the threshold trained by classifiers in Table IV and the distributions of the testing set in each division step. The classification rates of subject 11 in all steps were 100%, as shown in Fig. 6. Even though the classification accuracy of the best classifier combination was improved at 98.8%, the accuracy was no significant difference with accuracy of SVM with RBF kernel in Table II. VII. DISCUSSION In this study, we estimated four different body postures using features extracted from ECG data measured capacitively. Four classifiers had over 97% of correct classifications, except one-layer ANN. Although SVM with RBF kernel had a better performance than other classifiers, as shown in Tables II and III, the differences between the results of SVM with RBF kernel

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TABLE III CONFUSION MATRICES FOR EACH CLASSIFIER

TABLE IV PERFORMANCE RATES OF THE BEST CLASSIFIER COMBINATION SELECTED IN EACH DIVISION STEP

TABLE V DETECTION PERFORMANCES OF RELATED WORKS DEPENDING ON SENSOR TYPE, THE NUMBER OF SENSORS, AND THE NUMBER OF POSTURES

and other classifiers were ranged from 0.1% to 5.1% in Table II. Therefore, we can conclude that the features were very well extracted to represent the characteristics of four body postures, because there was no significant performance difference among all classifiers. It is novel that our research used ECG signals as features and evaluated the performances with five classifiers. Moreover, because the number of estimated body postures and used sensors are considerably different, a direct comparison between our results and performances of other studies is not appropriate. But we summarized various information of the systems that were mentioned in Section II. As shown in Table V, the highest classification accuracy to date was about 98% using 32 pressure sensors (FFS) with 6 UWB tags on 5 postures. Although the accuracy was almost the same with ours, our system does not require users to wear the tag embedded shirt and uses much less sensors with one modality. When the body postures were divided using three steps, the high average accuracy showed a distinction between the prone posture and left lateral posture, and the low classification accuracy was in the Sp /Rp division step. The reason is because variation in the electric axis of the heart is small when the distance from the heart to the electrode is far, such as when lying in a right lateral posture. Furthermore, people do not actually change their body posture from supine to prone immediately and vice versa, but pass through a lateral posture. However, our experiment made subjects change their postures from right to left lateral posture or from prone to supine posture and vice versa because of picking postures randomly. Therefore, the time that signals took to stabilize varied constantly and the data sets for analysis included fluctuating sections.

Polysomnography is the most traditional sleep examination. However, it is a time-consuming method, and the data measured may not reflect the patient’s real sleep activity because many sensors attached on the head and face during the examination limit the patient’s ability to move freely on the bed. In addition, diagnosis is based on the data for only one night. Therefore, for simple and continuous examination at home or in a clinic, detection of OSA or analysis of sleep stages by R–R intervals from ECG has been widely investigated [29]–[32]. In terms of this, our designed system can be considered as a diagnostic device, providing valuable information on sleep as well as sleep postures. For example, because the developed algorithm prevents patients with OSA from lying in a supine posture for a long time, the frequency of OSA could be decreased. At the same time, how many and how long OSA events occur during sleep can be detected by R–R intervals or the R peak envelopes of ECG. Sleep quality also can be evaluated by seeing how often sleep posture changes during sleep. Therefore, we are planning to apply our system and algorithm to research related to sleep. In addition, electrodes in contact with the body were detected manually in this study. However, an automatic detection method would be needed to apply it in real life. For this, we conducted a preliminary analysis. After histograms of ECG data on each channel were computed, a probability density function (PDF) was applied to the histogram. The results from the channels

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sufficiently difficult to be regarded as another research subject. Therefore, more studies are needed to determine how to detect the contacted electrodes automatically. The main limitation of the developed system and algorithm occurs due to the characteristic of capacitive ECG measurement. First, large movement artifacts are shown by triboelectricity and by variation in impedance between the body and the electrode [33], [34]. In particular, whenever the subject changes postures, the effect of variation owing to clothing is increased. Thus, the SNR of ECG signals is also low immediately after changing body postures. Second, the quality of the ECG signals is sensitive to the clothing fabric and surrounding environment. It is known that a combination of clothes made of 100% cotton and high humidity gives better signals [23], but the fact that the patient gown is cotton does not present any problems. VIII. CONCLUSION Bedsores and OSA diseases are closely related to body postures on a bed and many pressure sensors are usually used for posture detection. In the present study, unconstrained ECG data measured from 12 CC electrodes on a bed were used for classification of four basic lying postures. The features were extracted based on the fact that the morphology of ECG varies according to the body posture. The algorithm performances of SVM with RBF kernel are better than the results that have been reported to date, even if only 12 electrodes (the smallest number of sensors ever to have been studied) were used. Unlike other sensors, the developed system and algorithm have the potential for daily ECG monitoring and sleep monitoring in an unobtrusive way, as well as for body posture detection. REFERENCES

Fig. 6. Hyperplane or threshold and testing sets of subject 11: (a) SVM with linear kernel (FL a m p , M a m p , and BL a m p ) in S p R p /P p L p step, (b) SVM with RBF kernel (M a m p ) in S p /R p step, and (c) LDA (M a m p and BL a m p ) in P p /L p step.

without contact with the body showed a Gaussian distribution because the channels were noise signals. However, a PDF of channels in contact with the body represented a sharp and narrow distribution. Therefore, two parameters could be used to select the contacted electrodes. One is the maximum peak value of distribution in each channel. The other parameter used the area of the PDF within certain periods because the entire area of the PDF was 1. To sum these up, when the normalized maximum peak value was over 0.7 or the normalized area was over 0.6, the channel was detected as a contacted electrode. The accuracy showed approximately 93%. Although PDF is easy to use and the result was high, noise detection in multichannels is

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Hong Ji Lee received the B.S. degree in information communication engineering from Ewha Womans University, Seoul, Korea, in 2010. She is currently working toward the combined M.Sc. and Ph.D. degrees in interdisciplinary program in bioengineering from Seoul National University, Seoul. Her current research interests include the study of biomedical instrumentations and signal processing for ubiquitous healthcare and extending nonintrusive biological signal measurement technologies into brain–computer interface for practical use.

Su Hwan Hwang received the B.S. degree in biomechatronics and electronic electrical engineering from Sungkyunkwan University, Suwon, Korea, in 2009. He is currently working toward the combined M.Sc. and Ph.D. degrees in interdisciplinary program in bioengineering from Seoul National University, Seoul, Korea. His current research interests include biomedical signal measurement and processing in sleep medicine with the goal of translating the engineering works into practical medical research and clinical applications.

Seung Min Lee received the B.S. degree in electronics engineering from Sogang University, Seoul, Korea, in 2005, and the M.S and Ph.D. degrees in interdisciplinary program in bioengineering from Seoul National University, Seoul, Korea, in 2007 and 2012, respectively. He is currently a Research Professor with the Department of Biomedical Engineering, College of Health Science, Korea University, Seoul, Korea. His current research interests include active microelectrodes, new electronic materials, and electronic devices for biomedical engineering. Yong Gyu Lim received the M.S and Ph.D. degrees in biomedical engineering from Seoul National University, Seoul, Korea, in 1990 and 2006, respectively. From 1995 to 2001, he was a Senior Researcher with the Samsung Advanced Institute of Technology, where he was involved in the development of MRI system. He is currently a Professor with the Department of Oriental Biomedical Engineering, College of Health Science, Sangji University, Wonju, Korea. His current research interests include biomedical signal processing, nonintrusive biomedical instrumentation, and MRI system. Kwang Suk Park (M’78–SM’09) received the B.S., M.S., and Ph.D. degrees in electronics engineering from Seoul National University, Seoul, Korea, in 1980, 1982, and 1985, respectively. In 1985, he joined the Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, as a Founding Staff Member, where he is currently a Professor and the Director of Advanced Biometric Research Center. His current research interests include biological signal measurement and processing for the diagnosis. His current research interests include nonintrusive measurements of biological signals for ubiquitous healthcare. Dr. Park is a member of the Korean Society of Medical and Biological Engineering and has was the Secretary General of the World Congress on Medical Physics and Biomedical Engineering that was held in Seoul in 2006. He is also a Senior Member of the IEEE Engineering in Medicine and Biology Society. Since 2005, has been an Associated Editor for the IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE. He has also Chaired or Cochaired the Annual International Conference on u-Healthcare during past nine years.