Using Accelerometers for Physical Actions ... - Semantic Scholar

ORIGINAL RESEARCH

Using Accelerometers for Physical Actions Recognition by a Neural Fuzzy Network 1

2

Shing-Hong Liu, Ph.D., and Yuan-Jen Chang, Ph.D. 1

Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan. 2 Department of Management Information Systems, Central Taiwan University of Science and Technology, Taichung, Taiwan.

Abstract Triaxial accelerometers were employed to monitor human actions under various conditions. This study aimed to determine an optimum classification scheme and sensor placement positions for recognizing different types of physical action. Three triaxial accelerometers were placed on the chest, waist, and thigh, and their abilities to recognize the three actions of walking, sitting down, and falling were determined. The features of the resultant triaxial signals from each accelerometer were extracted by an autoregression (AR) model. A self-constructing neural fuzzy inference network (SONFIN) was used to recognize the three actions. The performance of the SONFIN was assessed based on statistical parameters, sensitivity, specificity, and total classification accuracy. The results show that the SONFIN provided a stability total classification accuracy of 96.3% and 88.7% for the training and testing data, when the parameters of the 60-order AR model were used as the input feature vector, and the accelerometer was placed anywhere on the abdomen. Seven elderly individuals performing the three basic actions had 80.4% confirmation for the testing data. Key words: accelerometer, physical action, SONFIN, health monitoring

Introduction

T

he assessment of physical actions in the elderly living in the community is an important method for monitoring their health status and quality of life. Falling is a very high risk factor in the daily living of the elderly, especially in those

D OI: 1 0. 1 089/ tmj. 2009. 0032

who live independently, and it often causes serious injuries such as bleeding, fracture, and damage to the central nervous system.1–3 The late delivery of emergency treatment for such injuries can result in disability, paralysis, and even death. Therefore, many methods for monitoring falling by the elderly have been investigated, including video systems, acoustic systems, and transducer detection. A video system typically captures the shape of human movement as a feature vector, with contour following and chain coding used to record the locations of contour points. An algorithm then determines whether a fall has occurred based on variations in the contours.4,5 An acoustic system typically detects a fall condition by analyzing audio signals. In general, this method is not very reliable, and is generally only used to augment other methods. A transducer-based method typically uses sensors such as a gyroscope, accelerometer, or strain gauge embedded within clothes to monitor actions in real time, with the occurrence of daily physical activities determined based on pattern-recognition techniques or a triggering threshold.6–8 The use of accelerometers in these methods has been shown to be an objective and reliable tool for assessing physical actions.9–12 The threshold method is normally used to distinguish falling conditions. The accelerometer is placed on the chest or thigh. However, if an elderly subject happens to fall down, he or she will either stand up again and walk, or not stand up again due to the presence of a serious injury. Distinguishing these two conditions is recognized as being very important for healthcare. When an elderly subject can stand up and walk after falling, this indicates self-control in which help is not required. In contrast, help is needed when the subject cannot stand up after falling. Moreover, falling and sitting down on a chair or bed both represent the body moving down. When sitting down a chair or a bed, the next activity may be to watch television or sleep. The accelerometer could not sense any activities for these states. These states of recumbency are very like falling down but obviously do not cause any injury. Therefore, distinguishing falling and sitting down is very important. According the above descriptions, more

© MARY ANN LI E BE RT, I NC. • VOL. 15 NO. 9 • NOVEMBER 2009 TELEMEDICINE and e-HEALTH 867

LIU AND CHANG

Figure 1 shows the detection system. The subjects wore three three-axial accelerometers on the chest, waist, and thigh. Two microcontroller units (MCUs), one master and one slave, were used to sample the signals of the three accelerometers and transfer the data with the serial communication to the Bluetooth module. A notebook (NB) received the data via Bluetooth. The interface was written in LabView software to display and record the signals. The raw independent signals from an accelerometer were converted into polar coordinates, and the feature vector was extracted. The three actions were then recognized with a SONFIN algorithm. Two MCUs were used since the TI MSP430 F1611 device (Texas Instruments, Ithaca, NY) used has only eight A/D channels and a 12-bit A/D converter, which was insufficient for three three-axial accelerometers. Therefore, two MCUs were imbedded in the system (one master and one slave), with data transfer performed using the serial peripheral interface bus model. The sampling rate of the master MCU was 250 Hz. One trigger signal of the master MCU was connected to the interrupt port of the slave MCU for synchronous sampling. The MCU used the serial communication port (baud rate: 115,200) to connect to the Bluetooth module (MB-C04, SMART Design Group, Taiwan). Each accelerometer (KXM52-L20, Dallas, TX) had a range of ±2 g with threeaxis output signals in rectangular coordinates. The power supply to the measurement system was a 4.5-V lithium battery. The sample data to the A/D converter of the MCU were separated into low and high bytes. The master MCU transmitted 20 bytes to the Bluetooth module when a sampling period finished: 18 bytes

Bluetooth

chest waist thigh

Feature Extraction





868 TELEMEDICINE and e-HEALTH NOVEMBER 200 9

Methods

Fig. 1. Our proposed detection system.

Recognition



activities of motion behavior have to be recognized for home care. The threshold method does not have enough capability to process this problem. Some studies have considered using neural or fuzzy recognizers for different objects. A backpropagation neural network (BPNN) uses the Fourier descriptor of the object’s contour as the feature vector for classification.13,14 Juang et al. showed that the self-constructing neural fuzzy inference network (SONFIN) recognizes moving objects with a shorter training time and higher recognition rate than the BPNN or the radial basis function network.15,16 Zhang et al. used the one-class support vector machine algorithm to extract features of the motion signal, and the kernel Fisher discriminant and the k-nearest-neighbor algorithm for classifying falling.11 The present study considered three problems: (1) the optimal position for the accelerometer; (2) recognition of the basic three actions of walking, sitting down, and falling; and (3) identification of an inexpensive portable telemetry device that is suitable for use in medical applications. Three accelerometers were placed on the chest, waist, and thigh to measure the motion trajectories related to the investigated actions. The signals were transmitted to the monitoring system using Bluetooth technology, which has been widely embedded in cell phones, personal digital assistants, and notebook computers. Thus, these devices could use the Internet to transmit data or alarm signals to family members.17 Moreover, some home-care medical devices, such as blood pressure monitors, electrocardiographs, and pulse oximeters, also contain embedded Bluetooth modules for outputting measured data. We used a SONFIN algorithm to recognize the three actions of walking, sitting down, and falling. This network can be self-learning by constructing the structure and parameters of the neural network for the habitual motions of an individual person.16 The acceleration trajectory of each action was considered as an autoregression (AR) model. A linear predictor method was used to estimate the model’s parameters as the feature vector. Our designed measurement system was applied to 10 volunteers performing the three actions of walking, sitting down, and falling. We gathered about 20 samples for each activity to complete our experiments. The results of the statistical parameters of the SONFIN show that the three actions could be recognized, achieving 88.7% accuracy when the accelerometer was placed anywhere between the chest and the waist, and when the dimension of the feature vector is 60. Using a BPNN rather than the SONFIN to recognize the three actions under the same conditions produced a markedly lower recognition rate. Finally, seven elderly individuals were asked to perform the three actions of walking, sitting, and climbing stairs. The system recognized appropriate activity in 80.4%.

Actions

PHYSICAL ACTIONS RECOGNITION BY A SONFIN

were for the X-, Y-, and Z-axis data of the three accelerometers; and the other 2 bytes were the distinguishing codes placed at the front of the 18-byte data segment. Because, when a Bluetooth was interrupted to stop the communication, some data were still stored in the buffer of the Bluetooth model, and when the Bluetooth transmitted data again, these data would appear before the new data; the two distinguishing codes were used to separate these data. This implementation allowed us to reconstruct the actual sampled data. The resultant signal from a triaxial accelerometer is derived by converting from rectangular coordinates into polar coordinates: s = √ x 2 + y 2 + z 2,

(1)

where x, y, z are the raw triaxial data of accelerometer. When stationary, the resultant signal, s, from the accelerometer was a constant +1 g. In this study, the window size of the signal was 4 seconds. Because the resultant signal of the action is considered as a stationary signal in one action cycle, it can be represented by a single-output AR model. The transfer function is defined as follows: H(z) = 1 + a1z-1 + … + apz-p ,

work realizes a fuzzy model of the following form: Rule j: If x1 is A1j and … and xn is Anj n

Then yj is w0j + ∑ wijxj , i=1

where xi is the input variable, yj is the output variable, Aij is a fuzzy n

∑ wijxi is the traditional Takagi-Sugeno-Kang (TSK) set, and w0j + i=1 model. The five layers are described below. Layer 1: No computation is performed in this layer. Each node in this layer, which corresponds to one input variable, only transmits input values to the next layer directly: ui(1) = xi .

Layer 2: For fuzzy set Aij, a Gaussian membership function is used to calculate the degree uij(2) that input variable xj belongs to the ith fuzzy set. Its mathematical function is defined as follows:

(2)

where p is the order of the AR model. The Yule-Walker equations were used to predict the parameters of the AR model. Assume that a predictor is a linear combination of the past samples:

(7)

y

Layer 5 (Output nodes)

p

xˆ[n] = –

∑ akx[n–k].

(3)

1

The prediction parameters {a1, a2, …, ap} are chosen to minimize the power of the prediction error e[n]: v = E[e[n]2] = E[x[n] – xˆ[n]2].

(4)

Layer 4 (Consequent nodes)

… n

w01 +

k = 1,2,…,p,

(5)

w02 +

i1 i

i-1

n

Σw x

w03 +

i2 i

i=1

Σw x

i3 i

i=1

… Layer 3 (Rule nodes)

The power of e[n] was minimized using the orthogonal principle:

E[x*[n–k](x[n] – xˆ[n])] = 0

n

Σw x

…

Layer 2 (Membership function nodes)

…

p

vmin = rxx[0] +

∑ akrxx[–k].

(6)

k=1

Therefore, the parameters of the AR model, ak, could be calculated when vmin was equal to σ2. The SONFIN is a general connectionist model of a fuzzy inference system, and its structure is shown in Figure 2.16 This five-layer net-

Layer 1 (Input nodes)

x1

x2

Fig. 2. Structure of the self-constructing neural fuzzy inference network.

© M ARY ANN LI E BE RT, I NC. • VOL. 15 NO. 9 • NOVEMBER 2009 TELEMEDICINE and e-HEALTH 869

LIU AND CHANG

– [ui(1) – mij ]2 uij(2) = exp(————————— ———— ), σij2

(8)

where mij and σij are the center and width of the membership function, respectively. This function is implemented by each node. Layer 3: A node in this layer represents one fuzzy logic rule and performs precondition matching of a rule. Here we use the following product operation for each Layer-3 node:

u(3) = j

∏ uij(2) .

(9)

i

Layer 4: Nodes in this layer are called consequent nodes. Each node is linked to Layer-3 output, and the linear association of the weight in this layer is n

u(4) = u(3) (w0j + j j

∑ wijxi) .

(10)

i=1

Layer 5: Each node in this layer corresponds to one output variable. The node integrates all the actions recommended by Layer 5 and acts as a defuzzifier with p

p

n

∑u(4) ∑u(3) (w0j + ∑ wijxi) j j j=1 j=1 i=1 y = u(5) = —p——— = ———————p——————————— . (3) (3) ∑uj ∑uj j=1

(11)

j=1

The number of network outputs is equal to the number of classes to be recognized (three in this study). The desired outputs, d, were (1, –1, –1), (–1, 1, –1), and (–1, –1, 1). Two types of training (structure and parameter training) were used concurrently to construct the SONFIN. Initially there were no rules in the SONFIN, with all rules being constructed by online structure training. For structure training, a predefined threshold, H, was used as a criterion for the generation of fuzzy rules. When the maximum uj(3) was below H for every rule, a new rule would be generated. Therefore, more rules were generated for a larger value of H. The initial width of each generated Gaussian fuzzy set was decided by a predefined constant σ. H was defined as 10–5, and σ was defined as 0.2. Training occurred for 1,000 iterations. In parameter training, the objective was to minimize the error 3

verror =

∑ (di – yi)2 .

(12)

i=1

The consequent part parameters were tuned by the recursive least-squares algorithm. The fuzzy-set parameters were tuned by a


gradient-descent algorithm, where the training rates of the consequent and fuzzy-set parts were 0.1 and 0.05, respectively. The details of the training algorithm can be found elsewhere.16 In order to compare the performance of the different classifiers for the same classification problems, we implemented the BPNN that also used the training rule of back-propagation to adjust the network parameters. The BPNN used in this study had only one hidden layer, with twice as many nodes as there were input nodes. The activation function in the hidden and output layers was the sigmoidal function. The desired output was (1, –1, –1), (–1, 1, –1) and (–1, –1, 1). The training rate of the BPNN was set to be 0.1, and the BPNN was trained for 1,000 iterations. Experiments were conducted in which the 10 healthy volunteers with no mobility limitations (5 females, 5 males; age: 21.3 ± 1.1 years, mean ± standard deviation) performed the three standard actions of walking, sitting down, and falling. Each action was performed 20 times; 10 data sets were selected at random to train the parameters of the classification algorithms, with the other 10 data sets used to evaluate the performance of the classification algorithm. Each subject was directed through the procedure by an investigator who identified the time of the onset and offset of each segment using a stopwatch. The three accelerometers were placed at chest, waist, and thigh with the elastic belts, respectively. The chest position was at the xiphoid process, the waist position at the navel, and the thigh position is the middle of the femur. In the sitting-down action, the subject stood for about 30 seconds then sat down an office chair and remained sitting for about 30 seconds. In the walking action, the subject stood for about 10 seconds, walked along a flat, straight corridor for about 1 minute, and remained standing for about 10 seconds. The subject listened to music in order to decrease the focus on the actual walking. In the falling action, the subject wore a blindfold, and after standing for about 10 seconds was led by the investigator toward an airbed before stumbling over it. The distance between the standing position and the airbed was changed for each experiment. The subject did not move on the airbed for about 30 seconds after stumbling or lying on the airbed.

Results Figure 3 shows the raw and resultant signals for the accelerometers placed on the chest for the walking, sitting-down, and falling actions. In the walking action, the z-axis signal changed repetitively, whereas the x- and y-axis signals did not change. In the sitting-down action, the change was always largest in the z-axis signal, but the x-axis signal also changed depending on the motion type of the individual


subject. In the falling action, the changes in the z- and x-axis signals were larger than those in the sitting signals. The training of the SONFIN as a recognizer employs a feature vector comprising the parameters of the AR model. The order of the model, p, determines the complexity and accuracy of the model. If the model order is too low, it will not accurately represent the characteristic of the resultant signal for each action, whereas a higher order increases the size of the recognizer, thereby increasing the training time. Therefore, model validation is required to verify that the identified model can adequately perform action classification based on the total classification accuracy. The classification performances for feature vectors of different dimensions and different accelerometer positions were examined

based on sensitivity and specificity values. Sensitivity is the ability to recognize correct actions, which is defined as Sensitivity = 100% · TP/(TP + FN) ,

(13)

and the specificity is the ability to recognize false actions, which is defined as Specificity = 100% · TN/(TN + FP) ,

(14)

Voltage (V) Voltage (V) Voltage (V)

Voltage (V)

Voltage (V)

Voltage (V)

where TP and TN are the numbers of correct (true-positive) and incorrect (true-negative) measurements of events, respectively, and FP and FN are the numbers of correct (false-positive) and incorrect (false-negative) measurements of nonevents. Tables 1, 2, and 3 list the classification Three independent signals of accelerometer RSS signal of accelerometer performances of the SONFIN for the 4 5 10 subjects in the training for sensors 3 4 placed on the chest, waist, and thigh, 2 3 respectively. Figure 4 shows the total 1 classification accuracy for AR models 2 0 with different orders, and indicates that 1 0 1 2 3 4 0 1 2 3 4 the recognition rate of SONFIN reached an ability to recognize the three actions Time (sec) Time (sec) (A) when the order of the AR model was 60. 4 Moreover, the values of the statistical 5 3 parameters (sensitivity, specificity, and 4 total classification accuracy) were almost 2 3 same for SONFINs with accelerometers 1 2 placed on the chest and waist. Moreover, 0 the falling action was detected with the 1 0 1 2 3 4 0 1 2 3 4 highest accuracy. The test performances Time (sec) Time (sec) for chest, waist, and thigh accelerometer (B) positions are listed in Tables 4, 5, and 6, 4 5 respectively. The effects of the order of 3 4 the AR model and accelerometer position were the same as those in the training 2 3 results. 1 2 The parameters of 60 order of the AR 0 1 model of the resultant signal were used as 0 1 2 3 4 0 1 2 3 4 the training and testing data for a BPNN. Time (sec) Time (sec) (C) The values of the statistical parameters of the classifiers for the training and testing data are listed in Tables 7 and 8. Fig. 3. Raw (left column) and resultant (right column) signals from the accelerometer placed on the abdomen obtained when sitting down (A), walking (B), and falling (C). Finally, 7 elderly subjects who did not

© MARY ANN LI E BE RT, I NC. • VOL. 15 NO. 9 • NOVEMBER 2009 TELEMEDICINE and e-HEALTH 871

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Table 1. Statistical Parameters of the Self-Constructing Neural Fuzzy Inference Network for the Training Data with the Accelerometer Placed on the Chest ORDER OF AR

TOTAL CLASSIFICATION ACCURACY

ACTIVITIES

SENSITIVITY

SPECIFICITY

20

Sitting Walking Falling

0.46 0.48 0.63

0.815 0.73 0.74

0.523

40


0.56 0.74 0.95

0.975 0.78 0.87

0.75


0.88 0.97 1

0.995 0.94 0.999

0.95


0.9 0.97 1

0.995 0.95 0.99

0.957


0.95 0.97 1

0.995 0.975 0.99

0.973

60

80

100

use any auxiliary mechanism for actions performed the three standard actions of walking, sitting, and climbing stairs. Table 9 shows the subjects’ basic information. According the above results, the subject wore an accelerometer at the waist. We also recorded 20 actions and selected 10 data sets at random to train the parameters of the classification algorithms, with the other 10 data sets used to evaluate the performance of

Table 3. Statistical Parameters of the Self-Constructing Neural Fuzzy Inference Network for the Training Data with the Accelerometer Placed on the Thigh ORDER OF AR

ACTIVITIES

SENSITIVITY

SPECIFICITY

20


0.47 0.48 0.73

0.865 0.735 0.74

0.556

40


0.77 0.71 0.88

0.94 0.885 0.855

0.787

60


0.83 0.87 0.95

0.975 0.915 0.934

0.883

80


0.84 0.89 0.96

0.98 0.92 0.945

0.896

100


0.85 0.90 0.96

0.98 0.925 0.95

0.903

AR, autoregression.

Table 2. Statistical Parameters of the Self-Constructing Neural Fuzzy Inference Network for the Training Data with the Accelerometer Placed on the Waist

ACTIVITIES

SENSITIVITY

SPECIFICITY

20


0.49 0.62 0.6

0.80 0.745 0.81

0.57

40


0.85 0.81 0.87

0.935 0.925 0.905

0.843

60


0.95 0.96 0.98

0.95 0.975 0.98

0.963

80


0.96 0.97 0.98

0.985 0.98 0.99

0.97


0.96 0.97 0.98

0.985 0.98 0.99

0.97

100

AR, autoregression.


AR, autoregression.

1.0 0.9

Accuracy

ORDER OF AR



0.8 0.7 0.6 0.5 0.4 20

40

60

80

100

Order of AR model

Fig. 4. Total classification accuracy for autoregression (AR) models with orders of 20, 40, 60, 80, and 100 with the accelerometer placed on the chest (solid line), abdomen (dotted line), and thigh (dashed line).


Table 4. Statistical Parameters of the Self-Constructing Neural Fuzzy Inference Network for the Testing Data with the Accelerometer Placed on the Chest ORDER OF AR


the classification algorithm. The input feature vector was the parameter of 60 order of the AR model. The values of the statistical parameters of the classifiers for the training and testing data are listed in Table 10.

Discussion and Conclusions

ACTIVITIES

SENSITIVITY

SPECIFICITY

20


0.42 0.5 0.56

0.78 0.71 0.75

0.493

40


0.67 0.65 0.89

0.945 0.835 0.825

0.737

Table 6. Statistical Parameters of the Self-Constructing Neural Fuzzy Inference Network for the Testing Data with the Accelerometer Placed on the Thigh

60


0.82 0.87 0.90

0.95 0.91 0.935

0.863

ORDER OF AR

80


0.82 0.87 0.90

0.95 0.91 0.935

100


0.82 0.88 0.90

0.95 0.91 0.94


ACTIVITIES

SENSITIVITY

SPECIFICITY

20


0.45 0.57 0.57

0.785 0.725 0.785

0.53

40


0.78 0.77 0.83

0.865 0.89 0.865

0.78

60


0.87 0.88 0.91

0.955 0.935 0.94

0.887


0.88 0.88 0.92

0.96 0.94 0.94

0.893


0.88 0.90 0.92

0.96 0.94 0.945

0.896

80

100

AR, autoregression.

SENSITIVITY

SPECIFICITY

20


0.42 0.44 0.68

0.84 0.71 0.72

0.513

40


0.71 0.75 0.77

0.885 0.855 0.875

0.743

60


0.77 0.82 0.88

0.94 0.885 0.91

0.823

80


0.77 0.82 0.90

0.95 0.885 0.91

0.83

100


0.78 0.82 0.90

0.95 0.89 0.91

0.883

0.867

Table 5. Statistical Parameters of the Self-Constructing Neural Fuzzy Inference Network for the Testing Data with the Accelerometer Placed on the Waist


ACTIVITIES

0.863

AR, autoregression.

ORDER OF AR

It is important to be able to detect movements such as walking and postural transitions because they provide valuable information

AR, autoregression.

Table 7. Statistical Parameters of the Backpropagation Neural Network for the Training Data TOTAL CLASSIFICATION ACCURACY

POSITION

ACTIVITIES

SENSITIVITY

SPECIFICITY

Chest


0.96 1 0.98

0.995 0.985 0.99

0.98

Waist


0.95 0.99 0.95

0.975 0.985 0.985

0.963

Thigh


0.96 0.98 0.94

0.99 0.955 0.995

0.96


LIU AND CHANG

Table 8. Statistical Parameters of the Backpropagation Neural Network for the Testing Data TOTAL CLASSIFICATION ACCURACY

POSITION

ACTIVITIES

SENSITIVITY

SPECIFICITY

Chest


0.53 0.47 0.34

0.68 0.72 0.77

0.447

Waist


0.52 0.80 0.32

0.79 0.635 0.895

0.547

Thigh


0.50 0.67 0.28

0.76 0.615 0.85

0.483

Table 9. Elderly’s Basic Information ELDER

SEX

AGE (YEARS)

HIGH (CM)

WEIGHT (KG)

1

Female

70

163

46

2

Female

68

158

50

3

Male

72

177

74

4

Male

86

168

68

5

Male

69

163

60

6

Male

73

168

79

7

Male

70

162

59

Table 10. Statistical Parameters for Elders TOTAL CLASSIFICATION ACCURACY

ACTIVITIES

SENSITIVITY

SPECIFICITY

Training

Sitting Walking Stairstep

0.99 0.96 0.96

1 0.97 0.98

0.967

Testing

Sitting Walking Stairstep

0.90 0.73 0.79

0.94 0.88 0.86

0.804

on the functional ability of a subject when falling has occurred. Moreover, it is necessary to be able to recognize the occurrence of walking after a fall, since this indicates that the subject has recovered from the accident; that is, a help alarm is not needed. In contrast, when no action occurs following a fall, the subject may require help.


Falling and sitting-down actions both involve the subject moving downward due to the force of gravity, and hence they can be difficult to distinguish. Figure 3 shows that the envelopes of signals associated with sitting-down and falling actions were similar, with only their amplitudes differing. This led to the falling event being misrecognized as a sitting-down event when the SONFIN used the parameters of a lower-order AR model as the input feature vector. There was always a subsequent action when the subject stood up after a fall. Walking was usually the most frequent action. The standing up is a transient action between the falling and walking. That is why the standing event was not recognized in this study. We did not consider small movements such as a slight change of posture while sitting down or between sitting and lying. Some studies have investigated differences in types of falling, such as forward, backward, and slipping. However, gravity is a constant, and the signals analyzed in the present study were the resultant signal. For different types of falling, the center of the gravity of the subject moves downward. Therefore, the resultant signals of these types of falling will be almost the same, except skeletal muscles exhibit reflex responses to prevent falling that produce some differences in posture. Moreover, O’Neill proposed that forward falling was most common for the elderly.18 Thus, in our study, experiments were designed to elicit subject stumbling in a non-anticipatory fashion. This could have resulted in decreased skeletal muscle activity. The data obtained during each experiment applied only to a single action, and the rest period was used to determine when the action ended (except for walking, which represents a continuous action). The timescale of human movements ranges from 160 to 190 ms for basic reaction times to several seconds for simple movements such as sit-to-stand transition.8 Extended movements, such as walking, can occur for indefinite periods. Because the duration of the single action was variable, it was not possible to apply a window width that matched the duration of all actions. For example, if the selected window was substantially longer than the length of an action, the window area would be corrupted by signal from the subsequent action, which could cause misclassification owing to an incorrect value for the averaged action in the window. However, shortening the window width increases the proportion of the action within that window, while a window that is narrower than the duration of the action would result in the classifiers not having sufficient information to correctly recognize the action therein, making the system susceptible to false-positive errors. The best value for the window duration was found to be 4 seconds in this study. Because SONFIN includes a TSK model in the fourth layer, the output value of SONFIN could be any value. In this study, we used


Restaurant Signal (Voltage)

the winner method to determine the action. But our recognizing algorithm also could be considered as a scale for the fall risk. Figure 5A shows a sample of falling action. The subject walked and fell down within 4 seconds. The raw data were segmented by a window in which the length was 4 seconds and shifted time was 0.25 second. The segmented data were extracted as the parameters of 60-order AR model to test the SONFIN results. From Figure 5A, we could find that the falling action happened at the third second. Figure 5B shows the continuous results of SONFIN for the three actions. When the subject was walking, the walking output of the SONFIN had the maximum value, but when the subject was falling down, the walking output of the SONFIN descended and the falling output of the SONFIN immediately rose to a maximum value. The window shifted 0.25 second again. Because the window almost left the falling action, the falling output of the SONFIN also descended again. The accelerometer measures the acceleration of the movement, so the optimal position at which to place the sensor is where

1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0

1

2

3

4

5

Time (sec)

(A)

SOFIN Output

1.5 1.0 0.5 0.0 -0.5 -0.1 1.5

2.0

2.5

3.0

3.5

Time (sec)

(B) Fig. 5. The continuous outputs of the self-constructing neural fuzzy inference network (SONFIN) for the falling action, and (A) the resultant signal; (B) the falling output is the solid line, the walking output is the dashed line, and the sitting output is the dotted line.

the force moment is largest, although in this study, we placed accelerometers on the chest, waist, and thigh, and the values of the statistical parameters of the SONFIN indicated that the best results were obtained from the waist position. Tables 1 and 2 and Tables 4 and 5 separately indicate that the total classification accuracies were 0.973 and 0.97, and 0.867 and 0.896, respectively, for the training and testing data. Therefore, we believe that the accelerometer could be placed anywhere between the chest and waist. We also compared the performances of the SONFIN and the BPNN in classifying actions using the parameters of the 60-order AR model as the input feature vector. For the training data, the total classification accuracies of the SONFIN and BPNN were 0.963 and 0.963, respectively, when the accelerometer was placed on the waist. However, for the testing data, the total classification accuracies of the SONFIN and BPNN were only 0.887 and 0.547, respectively. The markedly worse performance of the BPNN compared to the SONFIN could be due to several factors, such as the training algorithms used, the scattered and mixed nature of the features, and the fixed network structure. Table 10 indicates that the total classification accuracies were 0.967 and 0.804, respectively, for the training and testing data. We found that the testing result is lower than the young subject’s result. Two reasons are possible. First, in the elderly person’s experiment, we considered safety. The elderly person did not do the falling action but rather climbed a stair. The paper had indicated that the elderly also easily fell down when climbing.18 But the accelerometer’s signal of the stair action is very like to the signal of the walking action. In Table 10, it is clear that the sensitivity and specificity of the walking action and stair actions were all lower than the sitting action. Second, the elderly subject’s mobility was lower than that of the younger subject. Thus, the consistency and stability of the elderly’s activities are also lower. This problem would decrease the recognizing rate of the SONFIN. We have shown that it is possible for the SONFIN algorithm to distinguish the three investigated actions using a single triaxial accelerometer. The AR model parameters were used as the feature vector of the resultant signal for the three actions. According to the values of the statistical parameters, the classifier accuracy was stable when the input feature vector was based on the parameters of the 60-order AR model. With the sensor placed anywhere between the chest and waist, the SONFIN exhibited an optimum total classification accuracy of 0.963 for the training data and 0.887 for the testing data. Finally, 7 elderly subjects were used to perform the three basic actions. It was recognized that there was 80.4% confirmation for the testing data.


LIU AND CHANG

Acknowledgments This work was supported by the National Science Council, Taiwan, Republic of China, under grant numbers NSC 96-2221-E-324-054MY3.

Disclosure Statement No competing financial interests exist.

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Address correspondence to: Shing-Hong Liu, Ph.D. Department of Computer Science and Information Engineering Chaoyang University of Technology 168 Jifong E. Road Wufong Township Taichung, 41349 Taiwan E-mail: [email protected] Received: March 16, 2009 Accepted: May 11, 2009