IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 2, MARCH 2013

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Triaxial Accelerometer-Based Fall Detection Method Using a Self-Constructing Cascade-AdaBoost-SVM Classifier Wen-Chang Cheng and Ding-Mao Jhan

Abstract—In this paper, we propose a cascade-AdaBoostsupport vector machine (SVM) classifier to complete the triaxial accelerometer-based fall detection method. The method uses the acceleration signals of daily activities of volunteers from a database and calculates feature values. By taking the feature values of a sliding window as an input vector, the cascade-AdaBoost-SVM algorithm can self-construct based on training vectors, and the AdaBoost algorithm of each layer can automatically select several optimal weak classifiers to form a strong classifier, which accelerates effectively the processing speed in the testing phase, requiring only selected features rather than all features. In addition, the algorithm can automatically determine whether to replace the AdaBoost classifier by support vector machine. We used the UCI database for the experiment, in which the triaxial accelerometers are, respectively, worn around the left and right ankles, and on the chest as well as the waist. The results are compared to those of the neural network, support vector machine, and the cascadeAdaBoost classifier. The experimental results show that the triaxial accelerometers around the chest and waist produce optimal results, and our proposed method has the highest accuracy rate and detection rate as well as the lowest false alarm rate. Index Terms—Feature selection, signal magnitude area (SMA), signal magnitude vector (SMV), sliding window, weak classifier.

I. INTRODUCTION UE to the advancement in medical technology, people’s life expectancy increases, which results in the aging structure trend of the population. In Taiwan, the percentage of the elderly population above 65 out of the total population increased from 10.4% in 2008 to 10.7% in 2010; it is estimated that the aging population will account for 20% of the total population in 2025 [1], [2]; these figures show Taiwan has entered the aging society era. Thus, there is this inevitable trend of responding to the aging phenomenon with the development of a health care mechanism for the elderly [3], [4]. The survey shows that onethird to a half of the old people over 65 years old fell down once, and about a half of them fell down once more. Hence, it can be

D

Manuscript received March 21, 2012; revised August 18, 2012; accepted December 21, 2012. Date of publication January 4, 2013; date of current version March 8, 2013. This work was supported in part by the National Science Council of Taiwan under Grant NSC 99-2632-E-324-001-MY3. The authors are with the Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JBHI.2012.2237034

said that one of every four elderly people over 65 years fell down more than once in the past year. The falls among the elderly people cause injuries to them, from slight injuries like skin congestion to severe injuries such as fractures or joint sprains, or even stroke or death caused by cerebral hemorrhage [5]–[7]. Therefore, falls among the elderly people cost a lot of contribution from home care and social health insurance, which increases the burden to society [8], [9]. It can be known from the above that today’s pressing research issue is to investigate the causes for falls among the elderly people and the methods of detecting the falls [10], [11], so as to avoid social cost increase caused by accidental falls. Thus, fall detection is an important part of health care for the elderly people. The fall detection can be roughly divided into three methods currently used, namely, environmental sensing [12], [13], image sensing [14], [15], and the wearable sensor [16]–[40]. As for the environmental sensing method, sensors such as infrared sensors, pressure sensors, or sound sensors are placed in the environment to detect if someone falls. This method has the advantages of a low price and the convenience it provides for users as they have no need of wearing any device. However, the system can only function in the environment where it is installed. As for the image sensing method, camera(s) are installed in the environment to detect if some of the pedestrians in the image fall using the image processing method. This method does not need users to wear any device, but its images are vulnerable to light and noise interference resulting in low accuracy and high arithmetic difficulty. In addition, this method might violate the user’s privacy, and the system can only be applied to the installation environment. In recent years, many scholars have proposed the detection method of wearing sensors, which captures the signal features and detects falls by making use of information obtained by such sensors as triaxial accelerometer or gyroscope sensor. It has the advantages of low cost and no influence from and limitations on the environment. Therefore, we take the third method in this paper. In related research, such method usually consists of three phases [10], [11]. In the first phase, the data obtained from the accelerometer are a series of time-related signals. Most researches use a single sensor, while some researches apply multiple sensors [24], [26], [28], [35], [38], [39] which relatively increase the cost and the discomfort, so we focus on the single sensor in this paper. The second phase discusses how to obtain useful feature vectors from these data. Commonly used features currently include the acceleration values of X-, Y-, and Z-axis of the triaxial accelerometer, signal magnitude vector (SMV), signal magnitude area (SMA), angle of acceleration, etc. Most

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 2, MARCH 2013

activity classification methods use windowing techniques to divide the sensor signal into smaller time segments (windows). Three different windowing techniques have been used in activity monitoring, sliding windows, event-defined windows, and activity-defined windows [29]. With the sliding window method, the signal is divided into windows of fixed length with no interwindow gaps. It does not require preprocessing of the sensor signal and is therefore ideally suited to real-time applications. Due to the simplicity of its application, most activity classification studies have employed this approach. Therefore, we adopt this method. The third phase discusses on how to determine whether the fall actually occurs/has occurred. Generally, it can be divided roughly into two determination methods based on either threshold values (including decision trees) [16]–[28] or classifier [29]–[40]; the threshold value determination is simple but is easily disturbed by noise. Furthermore, it depends on the wearing position and person. Therefore, some scholars propose to improve the accuracy by the use of classifier, such as the artificial neural networks [30], [31], fuzzy logic [32], [33], support vector machine (SVM) [34]–[37], and combining classifiers [38]–[40]. However, these methods still have a low detection rate and a high false alarm rate. To increase the detection rate and reduce the false alarm rate, this study used a cascade-AdaBoost-SVM classifier that combines AdaBoost classifier and SVM [41], [42] to classify the fall detections of triaxial accelerometers’ feature vectors. The AdaBoost classifier is a linear combination of multiple classifiers [43]–[45], in which each classifier only classifies one dimension of the input feature vectors and each classifier is known as a weak classifier. When the AdaBoost classifier is constructed, the algorithm will calculate the weight value of the weak classifier after it is newly added and at the same time, readjust the weight value of each training vector and pass that to the next weak classifier. By adding a new weak classifier, the accuracy of the classifier is gradually increased; therefore, the optimal feature can be selected automatically from the input vectors of the sliding window. Besides, in order to reduce the false alarm rate of the AdaBoost, scholars [46], [47] have proposed the cascadeAdaBoost algorithm to construct cascade-AdaBoost classifier. In spite of this, the cascade-AdaBoost classifier can use fewer weak classifiers of an AdaBoost classifier in the previous layer but more weak classifiers of the AdaBoost classifier in the rear layer, but this can easily lead to the overfitting. Thus, we propose a new cascade-AdaBoost-SVM classifier that combines the AdaBoost classifier and SVM [41], [42]. When the number of weak classifiers of the AdaBoost classifier in the cascadeAdaBoost classifier of the rear layer exceeds the default number, SVM [48], [49] automatically replaces the AdaBoost classifier of the rear layer. In addition, we also improve the AdaBoost algorithm to construct the classifier with a high detection rate. Finally, in order to validate the effect of our proposed method, we use the Localization Data for Person Activity database of UCI in the experiment [50]. The experimental results show that our proposed method effectively enhances the accuracy and also achieves the highest detection rate and the lowest false alarm rate.

The remaining structure of the paper is as follows: Section II will introduce the use of the feature vector, Section III will explain the cascade-AdaBoost-SVM classifier, and Section IV will introduce the experiment and results. Section V sets forth the conclusion. II. FEATURE EXTRACTION It can be found from the observation of triaxial accelerometer’s values that the acceleration changes significantly when a fall occurs. Therefore, the change of acceleration intensity value can be used to determine whether a fall occurs. The commonly used features currently are SMV, SMA, the angle of acceleration, etc. [29]. Since the angle of acceleration is related to the wearing posture, it is not considered in this paper. Instead, we take the acceleration values of X-, Y -, and Z-axis of the triaxial accelerometer, SMV and SMA as the feature vectors of fall detection, in which the SMV and SMA signals are calculated by using the original triaxial acceleration values, and SMV is defined as the intensity of triaxial acceleration value, which is shown as SMV[n] =



x[n]2 + y[n]2 + z[n]2

(1)

where x[n], y[n], and z[n] are the acceleration values of X-, Y -, and Z-axis, respectively, at the sampling time n. In addition, since the fall is a process of time, the intensity change at a single detection moment is often misjudged by the noise. Thus, another feature used to detect falls is the integration of the triaxial acceleration intensity values, which is known as the SMA. And the sampling signal at the discrete time can be replaced by the sum; thus, SMA[n] can be defined as 1 SMA[n] = N

n −N +1  i=n

|x[i]| +

n −N +1 i=n

|y[i]| +

n −N +1

 |z[i]|

i=n

(2) where x[n], y[n], and z[n] are the acceleration values of X-, Y -, and Z-axis, respectively, at the sampling time n. Differing from those methods are some methods that only consider the signals at a single sampling time as the input vector; we take the features of multiple consecutive sampling time points (sliding window) as a single input feature vector f = [xn −N +1 , . . . , xn ]T , where xn = [x[n], y[n], z[n], SMV[n], SMA[n]]. The following sections will describe our proposed cascade-AdaBoost-SVM classifier. III. SELF-CONSTRUCTING CASCADE-ADABOOST-SVM FOR FALL DETECTION In this section, we introduce the self-constructing cascadeAdaBoost-SVM classifier that combines the AdaBoost classifier [43]–[45] and SVM [48], [49]. First, this section introduces the AdaBoost classifier and cascade-AdaBoost classifier [46], [47], and then proceeds with the introduction of our self-constructing cascade-AdaBoost-SVM classifier and algorithm [41], [42].

CHENG AND JHAN: TRIAXIAL ACCELEROMETER-BASED FALL DETECTION METHOD

Fig. 1.

AdaBoost architecture diagram.

Fig. 3.

Fig. 2.

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Diagram of the classification performance evaluation.

A. Cascade-AdaBoost Classifier The AdaBoost classifier was originally proposed by Freund and Schapire [43], [44]. The AdaBoost classifier is a parallel classifier combined with many linear weak classifiers. Each weak classifier only focuses on the classification of one dimension in the input feature vector. During the entire training process after the goal is given to the classifier, the algorithm is able to self-adaptively increase the number of weak classifiers so as to improve the overall accuracy rate of the classification and focus on key features. After a weak classifier is added, the algorithm uses the minimum error to calculate the weight value of this weak classifier and readjust the weight value of every training example, and then pass the value to the next newly added weak classifier. Based on the newly added weak classifier, the effect of the overall parallel classifier is improved. The architecture diagram of the AdaBoost classifier is shown in Fig. 1, wherein f represents the input feature vector, ht (f ), t = 1, . . . , T , represents the number of weak classifiers, and βt , t = 1, . . . , T , represents the corresponding weight value of each weak classifier. The final result of the strong classifier H(f) is expressed in the following equation:   T  βt ht (f ) . (3) H(f ) = sign t=1

Since the AdaBoost classifier is a strong classifier composed of many weak classifiers, the selection of effective weak classifiers is important. Fig. 2 shows the case of two classifications for one dimension. Assuming that both positive and negative examples have Gaussian probability distribution features, the solid line covered by the Gaussian function is the probability distribution of positive examples and the dotted line covered by the Gaussian function is the probability distribution of negative examples. Both Gaussian functions are partially overlapped. The

AdaBoost algorithm.

weak classifiers obtained by the minimum mean square error method can get the best accuracy rate, as shown in the classifier location of the critical value marked by the “Th2” line in Fig. 2. But this classification leads to a wrong classification of some positive examples. In case of a high detection rate requirement, the weak classifiers must be able to correctly classify all positive examples and maintain the error classification result of the negative examples only, as shown in the classifier location of the critical value marked by the “Th3” line in Fig. 2. In this case, two types of classification examples can have a completely accurate detection rate and high false alarm rate. Since we have focused on the high detection rate in this paper, the simplest method to achieve a high detection rate is to change the initial weight value setting method of each training example assuming that the AdaBoost algorithm would not be changed. If the training set contains p positive examples and q negative examples and the weight value ωp of a positive example is equal to the weight value ωn of all negative examples given that no positive example can be falsely classified, the relationship can be expressed in the following equation: ωp = qωn pωp + qωn = 1.

(4)

By solving this simultaneous equation, we can get the weight values ωp = 1/(p + 1) and ωn = 1/q(p + 1); the result can be used to replace the initial setting of the weight value of training examples in the AdaBoost algorithm, shown in Fig. 3. Fig. 3 shows the modified algorithm of the AdaBoost classifier. Assume that a training set {fi , yi }, i = 1, . . . , m is given, in which fi ∈ Rn , yi ∈ {1, −1}. First, the initial weight values of all training examples are initialized. The weight values of the positive example and negative example would be set as 1/(p + 1) and 1/q(p + 1), respectively. Next, the selection of T weak classifiers is cycled. When carrying out the tth cycle, the weak classifiers hj (f ), j = 1, . . . , n with minimum error for each dimension are selected among those weak classifiers. The weak classifier with the lowest error is selected from these weak classifiers and regarded as the weak classifier ht of the tth cycle, and the corresponding weight value βt of this weak

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Fig. 4.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 2, MARCH 2013

Classifier’s cascade architecture diagram.

classifier ht (f ) is calculated. The weight value ωi (t + 1), i = 1, . . . , m of each training example is then readjusted to carry out prior classification of examples with classification errors in t cycles while executing the (t + 1)th cycle. Finally, the sum of all T weak classifiers and corresponding weight values β are calculated, and the sign function is obtained to get the strong classifier H(f). However, the AdaBoost classifier still has a high false alarm rate. To improve this condition, Viola and Jones [46], [47] have further proposed a cascade-AdaBoost classifier to reduce the false alarm rate. In the cascade-AdaBoost classifier architecture shown in Fig. 4, each classifier is an AdaBoost classifier. If the input feature vector is determined as a negative example when passing the first AdaBoost classifier, it would be removed from the training set without entering the next AdaBoost classifier. Therefore, the number of examples would be decreased along with the increase in layers, in order to remove negative examples quickly. If the input feature vector is determined as a positive example, then the training example would enter the next layer’s AdaBoost classifier for further classification until it reaches the last layer. Therefore, classification through the cascade classifier still maintains a high detection rate with very low false alarm rate. The main purpose of the cascade classifier is to reduce the false alarm rate by using the cascade combination of many classifiers. Assume there are L layers in this cascade classifier and the detection rate and false alarm rate of each layer are di and fi , respectively, then the detection rate and the false alarm rate of the whole cascade classifier can be defined as D = (di )L and F = (fi )L , respectively. For example, there are ten layers in this cascade classifier. If the detection rate di and the false alarm rate fi are set as 0.99 and 0.3, respectively, in all layers, then the whole detection rate D and the whole false alarm rate F will be (0.99)10 > 0.9 and (0.3)10 < 6e−6, respectively. It can be observed that the cascade classifier can reduce the overall false alarm rate as well as the overall detection rate; therefore, classifiers in each layer must maintain the highest detection rate while reducing the false alarm rate. B. SVMs SVMs were first proposed by Vapnik [48] and have been successfully applied to many classification issues [48], [49]. We hope to find a hyperplane f (x), which makes category “−1” of y fall into the range of f (x) < 0 and category “+1” of y fall into the range of f (x) > 0. Therefore, we can distinguish the categories according to the sign of f (x). The hyperplane equation of f (x) can be expressed as f (x) = wT x + b

(5)

where w is the normal vector of this hyperplane, x is the input vector, −b/w is the distance from the origin perpendicular to the hyperplane. If w is a unit vector, this distance is −b. Thus, w and b are the parameters we search for. SVM solution is based on maximum margin and minimum square error. So, the objective function can be defined as follows: m  1 αi (yi − f (xi ))2 (6) E(w, b) = w2 − 2 i=1 where α = {α1 , . . . , αm } is the coefficient of Lagrange and αi > 0, i = 1, 2, . . . , m. Maximizing (6), the following equation can be obtained: m m   w= αi yi xi and αi yi = 0. (7) i=1

i=1

Substitute the aforementioned equation into (6). The original objective function is converted into a dual objective function, and the objective function can be redefined as Q (α) =

m  i=1

1  αi αj yi yj K (xi , xj ). 2 i=1 j =1 m

αi −

m

It also satisfies m  αi yi = 0; 0 ≤ αi ≤ C, ∀i = 1, 2, . . . , m

(8)

(9)

i=1

where α = {α1 , . . . , αm } is the coefficient of Lagrange, K(xi , xj ) is a kernel function, m is the number of training examples, and C is an adjustable positive parameter. For separable linear problems, C value is infinite. On the contrary, for nonseparable linear problems, C value is a positive integer. This equation is a quadratic equation, so it can be solved by using the optimization algorithm. By substituting the obtained value of α = {α1 , . . . , αm } into (7), the w vector is obtained. By substituting w into (5), b value can be obtained. K(xi , xj ) is a kernel function of positive number. In this paper, we use Gaussian function as the kernel function, which is defined as follows:   − x − xi 2 (10) K(x, xi ) = exp σ2 where σ acts as a variance parameter. C. Self-Constructing Cascade-AdaBoost-SVM Classifier We found that AdaBoost classifiers in front layers could reach preset targets with less weak classifiers but those in rear layers need more weak classifiers because the training set would remove some negative examples when passing through each layer of the AdaBoost classifier; with the increase of layers, the examples of the remaining training set become less and similar, so more difficult negative examples are used for training in latter layers, and more weak learners were usually chosen to satisfy the goals in the latter layers. To solve this problem, we proposed a self-constructing cascade classifier combining AdaBoost classifier with SVM, called cascade-AdaBoost-SVM classifier [41], [42], and modified the training algorithm of

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erwise, training of the AdaBoost classifier at this layer will be completed. Then, determine whether the overall false alarm rate of the present cascade-AdaBoost-SVM classifier can satisfy the condition of the external loop; if it can, train the AdaBoost classifier of the next layer with the rest of the negative examples and all the positive examples; otherwise, terminate the training of the cascade-AdaBoost-SVM classifier. In addition, the SVM training of the cascade-AdaBoost-SVM is different from the previous training which focuses on a higher accuracy rate. Our proposed cascade classifier is based on the premise of a high detection rate to further reduce the false alarm rate. By considering the accuracy rate, a lower false alarm rate could be obtained but might lead to a wrong classification of some positive training examples, causing a detection rate reduction of the SVM classification result; this further reduces the detection rate of the overall cascade classifier. In order to maintain a high detection rate of the overall cascade classifier, the related parameters selected in the SVM training would be capable of maintaining a higher detection rate rather than a higher accuracy rate. Therefore, the cascade-AdaBoost-SVM classifier can use classifiers with less layers but achieve lower overall false alarm rate than that of the cascade-AdaBoost classifier. Fig. 5.

Training algorithm for building a cascade AdaBoost-SVM classifier.

the cascade-AdaBoost classifier proposed by Viola and Jones to make it suitable for constructing a cascade-AdaBoost-SVM classifier; at the very start, the algorithm set the lowest detection rate, the highest false alarm rate, and the maximum number of weak classifiers of each layer of the AdaBoost classifier; when the AdaBoost classifier of each layer could not achieve the preset performance under the predetermined maximum number of weak classifiers, substitute this AdaBoost classifier with SVM and perform SVM training based on the feature dimensions selected by the AdaBoost classifier without calculating all dimensions. The proposed cascade classifier can increase AdaBoost classifier or SVM adaptively. Fig. 5 shows the construction algorithm of the cascadeAdaBoost-SVM classifier; during the construction of the cascade classifier, first set the detection rate d, the false alarm rate f , the maximum number of weak classifiers nth , and the target Ftarget of the overall false alarm rate of classifiers. Suppose that the sets of positive examples and negative examples are called P and N, respectively; the construction algorithm of the cascade classifier is mainly composed of two loops; the internal loop mainly uses the aforementioned AdaBoost algorithm to train the AdaBoost classifier; each time a weak classifier is added, the present AdaBoost classifier will be reappraised to see if it has satisfied the condition; if it has, continue adding weak classifiers until conditions of the inner loop are not satisfied; otherwise, it determines whether the number ni of weak classifiers is greater than the maximum value nth ; if it is, substitute AdaBoost classifier of this layer with SVM and train SVM with the dimension of input vector selected by this AdaBoost classifier without calculating all dimensions of input vector; in this way, the training of SVM can be completed more effectively and quickly; oth-

IV. EXPERIMENTAL RESULTS In this paper, we use the Localization Data for Person Activity dataset of UCI database [38], [50] in the fall detection experiments. The commercially available localization system allows local positioning by tracking a set of reasonably small tags, which are attached to a person. A sampling frequency of around 10 Hz can be achieved with no more than four tags attached to a person simultaneously. They decided to position the tags at the following locations on the body: chest, waist, left ankle, and right ankle. The database includes five volunteers, and each of them is recorded with the triaxial acceleration values of five daily activities, respectively. Therefore, there are 25 records lasting for 3–5 min. In each record, there are three falls, including the first two occur when walking which cause people to lie on the ground (walking–falling–lying), and the last fall occurs by sliding from the chair to the ground (sitting–falling– sitting on the ground). The situations in which the alarm must be raised are tripping (quick fall), fainting (slow fall), and sliding from the chair (sliding slowly from the chair and sitting on the ground). Fig. 6 shows the signal timing diagram on the records of triaxial accelerometer around the waist of the first testee, including the acceleration values of X-, Y -, and Z-axis, and d, SMV, and SMA features, wherein d represents the artificial mark of the position where the fall occurs. Therefore, when d is 1, it indicates the occurrence of fall; otherwise, no fall occurs. It can be found from the diagram that, when fall occurs, the triaxial acceleration values of X-, Y -, and Z-axis change significantly. However, they also change significantly in many cases wherein no fall occurs. In addition, most of the noise is difficult to be identified, so the calculated SMV and SMA features have significant change. From the diagram, it is also known that the method that set a single threshold value for the SMV and SMA features

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TABLE I COMPARISON TABLE OF EXPERIMENTAL RESULTS

Fig. 6. waist.

Signal timing diagram example of daily activity captured from the

will fail. Thus, the self-adaptive boosting classifier that is selfadaptive based on the training examples is used to integrate multiple threshold classifiers (weak classifiers) into an effective strong classifier. Furthermore, in most existing fall detection methods using the classifier, only the triaxial acceleration values of the current moment and relevant parameters are inputted to conduct the classified fall detections in order to simplify the system input. However, from the diagram, it is known that fall is a time process. In this paper, we will not only consider the current triaxial acceleration values, SMV, and SMA features, but also those in the past period. We use the concept of a sliding observation window and integrate the triaxial acceleration values and features at all sampling times covered by the window into a single input vector. In other words, if the window covers the triaxial acceleration values of X-, Y -, and Z-axis, as well as SMV and SMA features of N sampling time points, the input vector is large, but the AdaBoost algorithm can automatically select effective features. Thus, in the final test, only the selected features are required to be inputted. In order to assess the test effect, we define three parameters, namely, accuracy rate (AR), detection rate (DR), and false alarm rate (FAR), and are expressed as follows: AR =

TP + TN × 100% p+q

(11)

DR =

TP × 100% p

(12)

FAR =

FP × 100% q

(13)

wherein p and q represent the number of collections of the positive examples (falls) and negative examples (no-fall), respectively, while TP is true positive which represents the number

of falls detected of examples, TN is true negative which represents the number of no-fall examples detected as a no-fall, and FP is false positive which represents the number of no-fall examples detected as a fall. Therefore, the AR is defined as the percentage of all correctly detected examples divided by the number of total examples, while the DR is defined as the percentage of the correct classification number of positive examples divided by the number of positive examples. Thus, the higher the DR, the higher the percentage of detected positive examples. At last, FAR is defined as the value of false positive number from negative examples divided by the number of negative examples, so the higher the FAR, the higher the false alarm rate, and vice versa. The following experiments adopt random sampling of five-folds to conduct cross validation. Since the total fall number is 75 per position, the fall training examples and testing examples of each experiment of each position have 60 records and 15 records of input vectors at N sampling time points, respectively. However, 100 records of input vectors at N sampling time points are randomly extracted as the no-fall examples from each testee’s daily activity records, which results in 2500 records of no-fall examples preposition. Thus, the numbers of no-fall training examples and testing examples of each experiment of each position have 2000 records and 500 records of input vectors at N sampling time points, respectively. In this experiment, we respectively use BP-NN, SVM, cascade-AdaBoost, and cascade-AdaBoost-SVM for input vectors classification to complete the fall detection, and used the triaxial accelerometers on the left ankle, right ankle, chest, and waist for experiments based on the database. The experimental results in Table I show that when the triaxial accelerometers are worn on the left ankle, chest, and waist, the cascade-AdaBoostSVM classifier has the optimal AR of 95.35%, 98.23%, and 98.48%, the highest DR of 52%, 88%, and 78.67%, and the lowest FAR of 2.53%, 1.27%, and 0.53%, respectively. When the triaxial accelerometer is worn on the right ankle, the SVM classifier has the optimal AR of 93.71%, followed by the cascadeAdaBoost-SVM classifier whose AR 93.52% is close to that of SVM. However, the DR of the cascade-AdaBoost-SVM

CHENG AND JHAN: TRIAXIAL ACCELEROMETER-BASED FALL DETECTION METHOD

TABLE II CLASSIFIER NUMBER AT EACH LAYER OF THE CASCADE-ADABOOST CLASSIFIER

TABLE III LAYER NUMBER OF THE CASCADE-ADABOOST-SVM CLASSIFIER AND THE NUMBER OF CLASSIFIERS AT EACH LAYER (THE RESULTS OF THREE EXPERIMENTS)

classifier is 52%, which is about 25% higher than SVM’s DR of 26.67%, but their FARs is 4.4% and 2.93%, respectively. In other words, among the 15 fall testing examples, the cascade AdaBoost-SVM classifier fails to detect for seven or eight times, while SVM fails to detect for about 11 times. Among 500 no-fall testing examples, the cascade-AdaBoost-SVM classifier gives about 22 times of false alarm, while SVM gives 15 times of false alarm. As for fall detection, under the principle that every fall must be detected, the cascade-AdaBoost-SVM classifier still has very good performance. From the experimental results, the chest and the waist have better performance than that of the ankle. This result is the same as other research works, and the result of the chest is the best, namely AR of 98.23%, DR of 88%, and FAR of 1.27%. Comparing to the optimal DR of 84% and FAR of 1.33% obtained by Kaluza et al. [38] by using the same database, this result is better. Table II shows the number of layers constructed by the cascade-AdaBoost classifier and the number of weak AdaBoost classifiers on each layer. We set the detection rate d = 0.99 and the false alarm rate f = 0.3. For the experimental results of different positions, six layers of AdaBoost classifiers are required. If the body is divided into the upper part subdivided into the waist and the chest and lower part subdivided into the left ankle and the right ankle, it can be seen from Table II that the number of weak classifiers used when the triaxial accelerometer is placed on the lower part is more than that on the upper part. It can be seen that, when the triaxial accelerometer is worn on the body for fall detection, it is better to place it on the upper part

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than the lower part of the body. From Table II, it can be found that the number of weak AdaBoost classifiers in the rear layers is obviously more than that of the previous layers. Table III shows the layer number constructed by the cascadeAdaBoost-SVM classifier and the number of classifiers on each layer. Since parameter nth is the basis of the cascade classifier algorithm and determines whether the AdaBoost classifier is replaced by SVM, the increased number of weak classifiers of a certain AdaBoost classifier layer is multiplied by several times of the increased numbers of previous several layers; then, we set nth as the number of weak classifiers of previous layers or a threshold from observation. For the experiment on the ankle, the maximum allowable number of weak classifiers is limited to 110; thus, the first experiment on the left ankle has three layers, namely 41 classifiers on layer 1, 81 on layer 2, and the rest on layer 3; if the number of classifiers on layer 3 exceeds 110, SVM shall be used to replace it. If the training is completed after the SVM is used, it will be expressed as 41–81–SVM. The second and third experiments have three layers of 29–76–SVM and four layers of 38–70–97—SVM, respectively, which are less than six layers of cascade-AdaBoost, and have lower FAR. Three experiments on the right ankle use three layers of 30– 68–SVM, four layers of 21–56–79–SVM, and three layers of 42–75–SVM, respectively, which shows the same performance as that of the left ankle. As to the chest and waist, the number of weak classifiers at each layer is much lower than that of the ankle. Therefore, we set the maximum allowable number of weak classifiers to 8. From Table III, the results show that the cascade-AdaBoost-SVM classifiers used on the chest and waist need four to seven layers, and the number of weak classifiers at each layer is less than 8. A minimum of 1 and a maximum of 8 weak classifiers can meet the detection rate d and the false alarm rate f which are set for each layer. Three experiments on the chest, respectively, require five layers of 1–1–1–SVM–8, seven layers of 1–1–1–7–7–SVM–8, and four layers of 1–1–8–SVM. Three experiments on the waist require four layers of 1–6–5– SVM, four layers of 1–1–5–SVM, and five layers of 1–8-6–7– SVM, respectively. From the experimental results, if SVM is used to replace the AdaBoost classifier in most experiments, the training results can reach 100%, which makes the training of the next layer unnecessary. But according to the results of the first and second experiments on the chest, some training examples still require further training after the training of SVM. Thus, the SVM is followed by a layer of AdaBoost classifiers. The main reason is that when we use SVM for training examples learning, a high DR rather than AR is mainly considered, and this might lead to the increase of the FAR. Thus, further training is needed. The results show that our proposed cascade-AdaBoost-SVM classifier can conduct the self-construction based on the training examples, and it does not finish by using a layer of SVM at the end of the cascade-AdaBoost classifier. Finally, only a small amount of weak classifier on each layer is used around the chest and waist. In other words, only a few features are selected from the triaxial acceleration values of X-, Y -, and Z-axis, as well as SMA and SMA features extracted from N sampling time points (the size of window) to be testing features, which makes rapid processing possible.

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V. CONCLUSION In this paper, we use the triaxial accelerometers worn on the body from the UCI database and the proposed cascadeAdaBoost-SVM classifier to complete a real-time fall detection method. As for the wearing position of the triaxial accelerometers, we adopt the triaxial accelerometers on the chest, waist, left ankle, and right ankle, respectively, for the experiments based on the database. From the experimental results, it is known that the triaxial accelerometers worn on the waist and chest have the optimal performance. In addition, the input of triaxial accelerometer values of multiple sampling time points and the features are taken into account to facilitate improving the fall detection AR. As to the classifiers used, we have proposed a new cascade classifier combining AdaBoost classifier and SVM. This self-constructing classifier is based on the training set and preset target; therefore, it does not have a fixed combined structure. It is the one which addresses the problems of the original cascadeAdaBoost classifier. Furthermore, we have also modified the initial setting method of the AdaBoost classifier algorithm so that the AdaBoost classifier would be able to focus on the classification detection rate. Based on the experimental results, the cascade-AdaBoost-SVM classifier can use fewer layers and less weak classifiers to achieve optimal performance.

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CHENG AND JHAN: TRIAXIAL ACCELEROMETER-BASED FALL DETECTION METHOD

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Wen-Chang Cheng was born in Nantou, Taiwan, in 1971. He received the B.S. degree in electronics engineering from National Cheng-Kung University, Tainan, Taiwan, in 1997, the M.S. degree in electronics engineering from National Chung Cheng University, Chiayi, Taiwan, in 1999, and the Ph.D. degree in electronics engineering from National Chiao-Tung University, Hsinchu, Taiwan, in 2005. He is currently an Assistant Professor in the Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan. His research interests include intelligence systems, video/image processing, machine learning, artificial intelligence, computer vision, and pattern recognition.

Ding-Mao Jhan, photograph and biography not available at the time of publication.