Falling Monitoring System Based On Multi Axial Accelerometer*

Wentao Liu and Yang Luo Junze YanDepartment of Mechanical Design Harbin Electric Machinery Company Limited

Harbin Institute of Technology Harbin, Heilongjiang Province, ChinaHarbin, Heilongjiang Province, China yanjunze@sina.com

liu.wentao@163.com

Chunjing Tao and Lifang MaR&D department

National Research Center for Rehabilitation Technical Aids Beijing, China

Abstract - A portable & wearable falling monitoring system is proposed in this paper, which is based on microcontroller and triaxial accelerometer, and used for real-time monitoring of human body motion state. Its purpose is to make real-time judgments during the falling, and enable airbags or other protective devices before the body impacts with the ground, preventing or reducing the damage of falling to the human body. This paper proposes a multi-condition comprehensive evaluation algorithm based on the value of acceleration SVM, by analyzing acceleration curve of the processes of the body falling and various daily activities. In the algorithm, the weightlessness condition, the time condition, the speed condition, and the angle condition etc are considered in human body motion state analysis, then the judgment is made whether a falling is happening by these conditions. Experiments show that the method can effectively identify the course of the falling with a low miss report rate and false report rate, meanwhile it can provides the appropriate time margin, and should be a feasible and practical way.

Index Terms - Falling detection, three-axis Digital Accelerometer, SVM, Multi-condition comprehensive evaluation algorithm

I. INTRODUCTION

Falling is more prominent phenomenon in the elderly population. Compared with the harm to young people, injury of falling is much greater in the elderly [1]. 1/3 of people over the age of 65 happen to falling once or more than once every year, and with the increase of age, the probability of falling is also increasing. Probability of population aged 80 or older who will falling in a year even reaches 50% [2-4]. In the United States, falling has become the sixth leading cause of death in the elderly over 70 years old, and every year the medical expenses resulting from a falling has reached more than 20 billion U.S. dollars [5-7]. In the UK fallings are the most leading cause of death after injury of people over 75 years old. And according to statistics in the UK, falling is the first cause of injury death in 65 years or older [8,

9].Therefore, research and prevention of fallings is extremely necessary, and how to reduce the harm caused by fallings has become a research focus.

This paper aims to develop a wearable early warning system of falling based on three-axis accelerometer, using three-axis accelerometer for data collection, MCU for data processing, and detect algorithm based on acceleration threshold as the algorithm of early warning system. This system can send an alarm signal before fully falling, and can guarantee a certain time margin, so that airbags or other protective devices can have enough time to respond.

II. MOVEMENT AND BEHAVIOR ANALYSIS OF THE PROCESS OF HUMAN FALLING

From the mechanical point of view, body balance situation depends on two factors, one is the position of the gravity center of the body, and the other is the area of support surface. Once the body gravity force line exceeds the scope of the support surface, the body will be in a state of imbalance, which is shown in Fig. 1(c) & (d). Therefore, it can be said, falling is an unstable state resulted from body gravity line out of the support surface.

Fig. 1 The process of the body falling

Only the gravity and the ground supporting force are acted on the human body during the process of falling. Apparently, the ground support force of to body decreases with the body dumping and then increases when the human

**This work is supported by Chinese national science and technology support project Grant # 2012BAI33B04, and Chinese National Natural Science Foundation Grant # 51275101.

body colliding with the ground. Variation of the force acting on the ground is shown in Fig. 2:

Fig. 2 The force curve in the process of falling

Falling can be divided into four stages in Fig. 2:

i) Stand phase: The person is a standing state, and the force F on the ground keeps balance with the gravity G.

ii) Weightless phase: Weight loss is that the apparent weight of the object is less than the actual weight. It also refers to the ground force less than the gravity force of the body. At this stage, the body falls down as the gravity line out of the support surface, and the pressure of support surface gradually reduced.

iii) Collision phase: The body comes into contact with the ground. Due to the inertia, the pressure of body from ground sharply increases, resulting in a higher peak.

iv) Still phase: The applied force of ground to the body becomes G.

In order to analyze the changes of acceleration and other parameters more intuitively and more accurately in the process of falling, this paper establishes a three-dimensional human body model in simulation software LifeMOD for simulating the process of human body’s falling. Spine joints and upper extremity joints of model is set with a fixed joint types. Lower extremity joint group has a great impact on falling, and need to be set. Stiffness coefficient of hip, knee, ankle and other lower limbs joints are set 17.152N/mm, damping coefficient is set 1.715N • s / mm. The parameter of muscle is set to default value. The material of ground is set as wood. The direction of gravitational acceleration is vertical and its value is 9.8m/s2. In the model the contact force between the human body and the ground and the friction force between them are added in order to prevent the human body penetrating the ground. The process of simulation is shown in Fig. 3. Simulation time is 1.7s, and the data sampling frequency is 80Hz.

After the simulation and analysis of the reference point which nears the human body’s hip joint, the curve of the vertical acceleration is shown in Figure 4.

The curve shows changes of the acceleration’s absolute value in vertical direction. The changes of other two directions are quite small relative to the vertical direction. As

shown in Fig. 2, the time of falling is about 1.1s. During the period of 0~1.1s, the force of ground to the human body reduced gradually. So under the effect of gravity, the human body’s acceleration along the direction of gravity was increased gradually. The value of the acceleration was gradually close to 0.9g. After contacting with the ground, a great peak value of the acceleration was created due to the enormous impact within a short period of time, it proves that falling has huge destructiveness.

Fig. 3 The simulation process of the human body’s falling

Fig. 4 The acceleration curve of the human body’s hip joint

During the period of acceleration’s changing from 0 to 0.9g, the supporting force of the ground was always less than G (human body’s gravity). The human body was in weightlessness. To do early warning for falling, the trend of falling must be judged during the phase of weightlessness.

III. CONSTITUE OF THE HUMAN FALLING MONITORING SYSTEM

The body falling monitoring system developed in this paper is constituted of a central processing unit MCU, a triaxial acceleration sensor, a memory module, a serial communication module, and input and output ports modules. The block diagram is shown in Fig. 5.

TI's MSP430F149 is used as MCU; ADXL345 three-axis accelerometer is chosen as sensor with ± 2g, ± 4g, ± 8g and ± 16g four selectable ranges, and direct output 16-bit digital signal; AT24C256 is selected as memory, which is a type of memory chip with capacity E2PROM 256k for storing dynamic acceleration data; this paper adopts SP3232EEN

serial communications chip to establish RS232 serial communication port for external communications with the MCP, download the program and transfer the recorded data. In addition， this paper sets photo coupler devices as I / O port, and it can control buzzer, airbags and other alarmed or protective external devices. Studies have shown that the acceleration component of the torso portion generally does not exceed 6g during daily activities, and also the frequency range is between 0-20Hz[10]. So the sampling frequency of the system is set 80Hz, and the range of the acceleration sensor is 16g. The dimension of falling monitoring system is 80mm x 60mm x 25mm, so it is easy to carry.

Fig. 5 The system hardware block diagram of body falling monitoring system

IV. DATA COLLECTION OF FALLING AND DAILY ACTIVITIES

In order to study difference of acceleration curve in the process of the body falling and other daily activities ADL (Activities of Daily Living), this paper uses the human falling monitoring system to collect acceleration data of falling and other activities of daily ADL. The falling monitoring system is put on the human waist during the experiment (Fig. 6), and the test of falling is shown in Fig. 7.

Fig. 6 Worn position of detection device

Fig. 7 Testing process diagram of falling

Experimental data are shown in Fig.8—Fig.11, where signal vector magnitude SVM (Signal Vector Magnitude) of the acceleration with three direction is

(1)

Fig. 8 is a change in acceleration during a falling, where a), b), c), and d) are acceleration curves of back falling, forward falling, left and right falling respectively. Fig. 9a) and 9b) are acceleration curves of walk and run. Fig. 10a) and Fig. 10b) are acceleration curves of squatting and sitting. Fig. 11a) and 11b) are acceleration curves of upstairs and downstairs. The unit of each Fig. is gravity g.

Fig. 8 Acceleration curve of falling

Fig. 9 Acceleration curve of walking and jogging

Fig. 10 Acceleration curve of sitting and squat

Fig. 11 Acceleration curve of upstairs and downstairs

V. JUDGED ALGORITHM OF FALLING

Through analysis and comparison of test data, this paper proposes integrated multiple conditions judgment algorithm based on acceleration value SVM, which considers the following conditions:

Condition 1： Weightlessness condition

Expression ：Wherein： is the value of the acceleration SVM,

is the weight loss determination threshold value.

Description: in an ideal state, SVM value equals to the gravitational g when the human body is standing or static, that is (the unit is g); when human falls, due to the effect of gravity, the center of gravity is out of control, the show weight will be less than actual weight, that is , so it can be judged as a condition falls. When the condition is true, it indicates that the body is in a state of weightlessness, the system can go to the next step for further judgment. Taking into account the effect of random vibration and other factors, threshold can set lower, such as 0.95-0.98.

Condition 2：Time Condition

Expression：

Wherein: is the continuous weightlessness time, is the continuous weightlessness time threshold.

Description ： It can be seen from Fig. 9 to Fig. 11，weight loss is not a special condition for falling, there

are also weightless situation in other daily activities, such as walking, running. Weightlessness time in normal daily activities is much shorter than time in falling. Thus the duration of weightlessness is a valid condition to determine whether the state is falling. When the weightlessness duration exceeds a critical value, it can be thought in the falling state.

According to the experiments, the obvious weightlessness was lasting about 0.4s~0.8s during the process of falling. The weightlessness time during walking was about 0.3s, during running was 0.2s, during going upstairs or downstairs was 0.3s, during sitting down or squatting down was 0.5s. The threshold of window time was set as TC=0.35s. It is considered as the suspect of falling if the time of weightlessness is more than 0.35s. The appropriate threshold setting can filter out most of non-falling states in theory.

Condition 3：Speed Condition

Expression：

Wherein： is the moving speed of the center of gravity in the vertical direction, is the moving speed threshold of the center of gravity in the vertical direction.

Description: Only by conditions 1 and 2, it sometimes cannot make a correctly judge. Because in terms of people squat or sit down at a slower rate, its acceleration curve SVM also displays a longer period of weightlessness, and the system easily makes a mistaken judge for a falling. So it requires to introduce the rate conditions. The body has a higher velocity during a falling in the vertical direction, but squat and sitting has a slower speed. Since the kinetic energy of human body is proportional to the square of the velocity, so the size of the speed of falling also expresses the severity of harm to human body, and is a very important criterion. For some processes of falling, it may not reach the threshold condition, thus it means harm to human body may be small. Since the starting vertical velocity is zero, so the speed can be calculated using the cumulative acceleration:

(2)

Condition 4：Direction Condition

Expression：Wherein： is the change angle of the acceleration

vector in falling, is the change angle threshold of the acceleration vector.

Description: The system can distinguish falling from most of ADL event through conditions 1, 2, 3, but there are still some ADL will be mistaken for a falling, such as jumping down from a height, whose SVM curve is similar to

the process of falling, then it considers to introduce the direction condition. For some typical falling process, torso has a greater angle change. The angle can be shown by the change angle of acceleration vector. is calculated as follows:

(3)

As can be seen from angle curves during falling in Fig. 12, the process of falling can be divided into four phases:

Phase i: Standing state, the inclination between body and ground is 0.

Phase ii: Dumping state, the angle between the body and the vertical direction gradually increases.

Phase iii: Touchdown phase, due to the output of X-axis also has a great impact, the angle of the output has a greater shock, the angle losses reference value at this time.

Phase iv: quiescent phase after falling, the body torso parallel to the horizontal direction, and the angle with the vertical is 90.

Fig. 12 Changes in the direction of the acceleration vector in the process of falling

The process of multiple conditions judgment method based on acceleration SVM is shown in Fig. 13.

Fig. 13 Flowchart of falling monitoring algorithm

VI. ESTABLISHMENT, TESTING AND ANALYSIS OF EXPERIMENT MEASUREMENT SYSTEM

1) The success rate analysis of detection

In order to reduce the risk of the experiment, the experimental subjects were 27 years old male and 26 years old female. M1 was set to fall forward, knees bending and touching the ground first. M2 to fall backward, buttocks touching the ground first. M3 to fall leftward, knees bending. M4 to fall rightward, knees bending. Each of these modes was measured 10 times. Knees touching the ground first is tested in order to simulate the real falling situation. Situations of falling detection under various modes are shown in Table I.

TABLE ITHE STATISTICAL RESULTS OF SUCCESSFUL FALLING DETECTION

M1 M2 M3 M4male 9/10 10/10 10/10 9/10

female 8/10 10/10 9/10 9/10

As shown in the table, the algorithm has considerable sensitivity on fallings with the highest prediction accuracy of mode M1. M3, M4 all have a certain degree of omissions. It proves that the different degrees of knees bending have a certain influence on the algorithm. Actually, even if there are different degrees of knees bending, the detection success rate of the system is still relatively high. It can be detected if directly kneeling on the mat during the process of falling forward. This fully shows that the algorithm has high sensitivity of falling.

2) Time margin analysis

The purpose of this system is to send alarm signals after early warning of falling, and then to drive airbags and other protection devices to intervene falling, instead of processing after falling has happened. So it should be guaranteed to leave enough time to inflate airbags before torsos of the human body touches the ground. This period of time is called time margin, which is the difference between the total duration of falling process and the continuous weightlessness time threshold Tc.

Use Matlab simulation to examine the time margin of this algorithm. Fig. 14 is the simulation result of falling forward, in which, the first peak is produced by the collision of knees touching the ground, the second peak occured when body touching the ground. As it can be seen from the Fig. 14, there is about 0.25s between the knee touching the ground and the body touching the ground. The system has been able to generate an alarm signal before the knee touch the ground, so for the forward fall, the algorithm can ensure adequate time margin.

Fig. 14 Analysis of falling forward time margin

Fig. 15 shows the time margin analysis of falling backward. It can be seen that the time from the moment of sending alarm signal to the peak is 0.2s ~ 0.3s. This period of time for the reactor is very abundant. It should be noted that, for falling backward, buttocks generally touches the ground first, and then the torsos. Due to the system placing on the waist of the human body, and relatively close to the buttocks, only one peak was measured.

Fig. 15 Analysis of falling backward time margin

By the above analysis of time margin, it can be seen that falling warning algorithm with three steps based on the threshold of SVM can meet response time requirements of airbags, and ensure a sufficient time margin, so it is an effective detection algorithm.

3) False report rate analysis

An important design principles of the present system is to guarantee the enough success rate at the absence of false report. Table II shows the false report statistics of the experiments. In experiments walking time was 30 minutes, running time was 20 minutes, the number of upstairs and downstairs is 20 times, sit and squat 30 times, lift 10 times, jump 10 times without speed condition enable (group 1), and jump 10 times with speed condition enable (group 2).

It can be seen from the above statistics of false reports, in their daily activities, false report rate of jumping from a height place is relatively higher, and it can be improved by using speed condition in algorithm. Considered that such jumping movements generally seldom happen for the elderly, so the false positives for jumping are acceptable.

In summary, this system is reliable in performance with low level of false positives, high degree of sensitivity. It can guarantee a certain time margin that meets the requirements of design.

VII. CONCLUSION

This paper is based on the characteristic that older age groups are relatively easy to fall, committing to the development of an early warning device of falling for the elderly. First, three-dimensional modeling and simulation software is used to simulate the process of human body’s falling, and obtain variation datum and curves of acceleration and inclination during the falling. A warning algorithm based on the acceleration threshold of SVM is proposed. Matlab is used to simulate the algorithm for a preliminary analysis, which demonstrated the feasibility and validity of the algorithm. Finally, a falling detection test environment based on the three-axis acceleration sensor was established. And the hardware and software systems with early warning detection algorithm was designed based on the acceleration threshold. The Experiment results show that the device can effectively detect fallings, and have a low level of false positives under daily activity mode.

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TABLEⅡTHE STATISTICAL RESULTS OF FALSE POSITIVES UNDER DAILY ACTIVITY MODE

walk run go upstairs go downstairs sit squat elevator jump 1 jump 2male 0 0 0 0 0 0 0 7/10 3/10

female 0 0 0 0 0 0 0 8/10 2/10