The notebook computer was used to display and store the experimental data. The Data acquisition schematic for this experiment is shown in Figure 2. Laptop ...
Proceedings of the 5" World Congress on Intelligent Control and Automation, June 15-19.2004, Hangzhou, P.R. China
A Wireless Motion Sensing System Using ADXL MEMS Accelerometers for Sports Science Applications Davey T. W. FONG, Joe C. Y. WONG, Alan H. F. LAM, Raymond H.W. LAM and Wen J. LI Centre for Micro and Nan0 Systems, The Chinese University of Hong Kong, Hong Kong SAR Absfracf- An acceleration-based wireless motion Sensing System (WMOSS) was developed for sports science applications. It is able to provide real-time information on human motions other than the conventional vision-based motion sensing systems. In this paper, we will present the working principles and system inkegrakion issues of the device. The initial calibration, signal processing and 3D self-calibration functions for the device algorithm were developed to optimize the system performance. Experiments were also conducted to analyze human motions using the WMOSS. The results from these experiments are also discussed in this paper.
motion. Imagine that there is an object which vibrates with very low amplitude but an extremely high frequency. Currently, it is difficult for the displacement-oriented systems to precisely detect such vibration. Furthermore, if excessive power consumption is exerted on the moving object over time, the object may be damaged or fatigued in its mechanical strncture. Existing vision-based systems would not be able to detect power consumptions due to small vibrations. The power consumption factor is also an important issue for an athlete. When an athlete is practising or is in competition, the athlete has to exert forces to maintain his ultimate movement. In other words, his bones, tendons and ligaments are sustaining the reaction forces of his movement. Such reaction forces are very large sometimes and may fatigue the athlete's tissues. When the damaging factors accumulate, the athlete will get injured or even shorten his sportsman career.
Index Terms - wireless motion sensing system, motion sensing and sports science.
M
I. INTRODUCTION
otion is a general term describing the act or process of changing position and place of a dynamic object. Since motion is heavily involved in every sport, it is one of the most important interests of sports science. For instance, motion sensation [l], motion capturing [2]-[3], and motion analysis [4] have long been hot topics io the research and development of sports science. However, equipment applying these technologies is either large in size, expensive or not user friendly enough. It is obviously not easy for the general public to use or afford these advanced and expensive equipment. Therefore, the use of high technology in sports science is still not popular.
Our low-power xireless m t i o n sensing system (WMOSS) is designed to alleviate the above problems. The WMOSS is a sensing device for wireless acceleration measurement with high sensitivity. Each sensing module of our system uses two low-cost and micro accelerometers to detect acceleration. The WMOSS is small and handy, and can he easily carried around.
In addition, the system can be easily installed, because a user only needs to cany the sensing module and run software to receive transmission data. By mounting the WMOSS on a moving object, such as the joint of a human arm,a seat inside a car, or a human leg during walking, some characteristics of the movements can be investigated. For instance, the natural frequency of the vibration for a given motion can be detected. Moreover, the sustaining mechanical power can he estimated. The force acting location of interest can also be determined by Newton's laws mechanics. Therefore, our portable WMOSS can provide extra useful information than the displacement-oriented motion sensors.
Not only the existing equipment has many disadvantages, but these systems also have some limitations. At present, vision-based motion sensing systems are widely used for motion analyses. These techniques have remarkable performances on measuring the displacements and the paths of motions. However, they are weak in measuring the vibration and the power consumption of a given This work is a joint research project between the Centre for Micro and Nan0 Systems and the Custom Computing Laboratoly (Department of Computer Science and Engineering) of The Chinese University of Hong Kong. The project is funded by the Centre for Micro and Nan0 Systems and the Hong Kong Innovation and Technology Commission (ITSII 85/01) *
By mounting several WMOSS on different locations on an athlete's body, localized vibrations can be directly detected. This may help to determine which motion is potentially harmGI to athletes.
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the WMOSS as shown in Figure 2. The microprocessor recursively extracts data from different axes of accelerations. It counts the duty cycles of the sensing signals and converts them to acceleration information. Since the signal outputs of the accelerometers are in the format of Pulse Width Modulation (PWM), the microprocessor is needed to digitalize those signals, sort them in the predefined sequence and pass the data sets to the wireless transmitter (Linx TXM-433-LC).
11. SYSTEM DESCRIPTION The basic idea of WMOSS is to measure the accelerations of human motions. By recording the accelerations, the forces, vibrations, and directions of motions can he obtained. Afterwards, the velocity and displacement information can also be obtained by integration, provided that the M O S S can provide enough sensitivity. The WMOSS could help analyze a person's motion with various criteria, e.g., motion speed, smoothness or stableness. The analyses could potentially show athletes' weak points or unconventional movements. In other words, it could probably help them to improve their performance.
I
The WMOSS consists of a motion sensing module, a wireless receiving module, and an interface program. The motion sensing module is able to detect acceleration, vibration, and force, and send data wirelessly. After the interface program collected all the sensor data, it can store and display those data instantaneously with some post-processing functions, such as some statistics and signal processing functions such as filtering (the details of a filter used in noise elimination will he described in section 3.1).
I Motion Sensing
Module
Wireless Receiving Module
1
----7
z
Accelerometer 3-
I
Interface Program L--------------J
Figure 2: Data acquisition schematic for the WMOSS.
Accelerometei
Afterwards, the wireless receiver (Linx RXM-433-LC) in the receiving module (Figure 3) receives and decodes all the signals. The interface IC chip MAX232 in the receiving module passes the acceleration data to a computer through the serial port (RS232). Using the interface program (Figure 4), all acceleration signals can be recorded for further analysis. Furthermore, the program can plot those signals in real-time, with some signal processing functions such as the Butterworth low-pass filtering. Some instantaneous statistical statistic parameters such as maximum, minimum, mean and standard deviation within a certain duration can also be calculated in the program.
Transmitter .
.
Figure 1: Photo ofthe circuihy in the WMOSS A WMOSS as shown in Figure 1 is able to detect the acceleration of a reference point on a human body. The module mainly includes two dual-axis MEMS accelerometers, a microprocessor and a wireless transmitter. The two accelerometers (manufactured by Analog Devices Inc.) are placed orthogonally in the module. The accelerometer A is placed on the base of the circuihy horizontally to measure the accelerations in x and y directions, while the accelerometer B is placed at the side vertically to detect the accelerations in y and z directions. Therefore, they are capable of detecting the accelerations in Cartesian space. The accelerometers are a microprocessor (AT90S8515 connected to manufactured by ATMEL Corporation), which is used for signal encoding and conversion. The microprocessor was programmed to control the data acquisition flow of
I
i
Fimue 3: Photo ofthe wirele ss . program for the WMOSS. receiving module. I
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Table 1: Specification data of the WMOSS Sensing Element Accelerationrange(lg= 9.81ms') Resoiution Bandwidth
Since the frequency of human motion is below 20Hz, the cutoff frequency we chose is f, = 20Hz.
. i2E 5mg
3.2 Inifial OffsefCalibration and Results For the WMOSS, there are intemal offsets in the sensing elements, so it is necessary to calibrate .them before use. The Butterworth low-pass filter as discussed in the previous section was used first to eliminate high frequency noise. Then, an offset-calibration procedure was applied to adjust the sensor offset as described below.
5000 Hz
0-70°C 0.6mA
"
supply current
Wireless Trahsmission Baud rate Transmission frequency
2400kbos 433Mfir
CPU Speed grade Clock rate Power consumption at 3V, 25°C
4 MHz 7.3728MHz Active: 3mA Idle mode: ImA
in the WMOSS there are 3 acceleration signals for 3 axes of detection. Different accelerometers have different bias values, and sometimes they are significant. However, for each axis, the offset value is consistent, hence, we can eliminate the offsets by subtracting the bias value for each sensor. After this process, the calibrated accelerometers have insignificant offsets as show in Figure 5(b). Hence the accuracy of the sensor can be improved significantly by the initial offset calibration.
Overall system
Dab sampling resolution Operating voltage Operating power
-40 data setdsec -3v 17.4 mW
-
The specification data of the WMOSS is summarized in Table 1. It includes some technical data about the sensors, the transmission, the microprocessor and the overall system. The sampling frequency of the WMOSS is about 40-data sets per second, which is larger than the reaction rate of human [ 5 ] (less than 20Hz of data sets). A data set is composed of a header byte and the acceleration values for all axes.
111. PERFORMANCE ENHANCEMENT The accelerometers we used are sensitive to acceleration changes. . However, some noise may exist during motion measurements, which may affect the accuracy .of the measurements. in order to eliminate the noise, the Butterworth low-pass filter was employed to filter out the noise at excessively high frequencies. The transfer function of the first order Butterworth low-pass filter is
where f, is the cutoff frequency in Hz and sampling frequency in Hz.
f,
3.3 Inclinaiion Angle Measurement Using WMOSS
The WMOSS is able to measure the orientation of mechanical structures. in order to evaluate the performance of the inclination angle measurement, an experiment for the tilt angle accuracy was done. To measufe the inclination angles of the M O S S , the sensors were mounted on a protractor to provide the reference angle marks. The experimental result is shown in Figure 6(a) and the detection error for each angle of measurement is shown in Figure 6(b).
is the
By applying the bilinear transformation on equation (I), the digitized expression of the output in time domain can be exmessed as
According to the results shown in Figure 6(b), the mean error and the standard deviation are -0.4551' and 0.7141", respectively. Moreover, it indicates the peak-to-peak error of the angle measurement is about 3" which is 0.83% error of the overall measurement range (360').
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Hence, the inclination angle measurement of the WMOSS is suitable for the human motions in low frequency range. Furthermore, it is accurate enough for the angle estimation in the 3D self-calibration technique to improve WMOSS accuracy, which will be discussed in section 4.
World coordinate frame
Device coordinate frame
’
(4
(b)
Figure 7 : The figure shows the gravitational acceleration vector in (a) the device coordinate frame and (b) the world coordinate frame. The algorithm requires only the rotations of x and y-axis with angles a and ./, respectively. Since 2 - is orthogonal to the x and y axes, it does not affect the position vectors along x and,y axes. In order to declare the reference directions for the world x .and y Bxes, the angle of rotation along z-axis y is set to he zero. Then the transformation equation becomes
Figure 6: Experimental result and error of the inclination angle measurement for the WMOSS sensors,
IV. 3D SELF-CALIBRATION FOR INITIAL CONDITIONS DETECTION A misalignment of a measurement relative to an object to be measured may exist due to the initial orientation of the sensor. In other words, when the WMOSS is mounted on a moving object, the z-axis of the device, for instance, cannot he made filly parallel to the gravity direction in every measurement. This will make the estimation of the signal directions become difficult.
where [xdo ydo z d o y is the initial acceleration vector detected in the device coordinate frame and [x, yo z , , r is the initial acceleration vector in the
Moreover, if the initial orientation of the WMOSS is restricted for every experiment, the measurement process will he much inconvenient. So it is necessary for the initial orientation he calibrated after a M O S S has been mounted on a moving object.
world coordination frame. By substituting [x, we get
y o z o r =[O
0
sins=+ , c o s a = P Z
The 3D self-calibration is an algorithm to fix the initial orientation misalignment problem. (Our team published a paper about 2D self-calibration in [ 5 ] . ) In other words, it is used to resolve the sensor signals from the device coordinate frame to the world coordinate frame. To perform the 3D self-calihration algorithm, the WMOSS should be kept in the steady state during certain duration before measurement. That is, it is not allowed to have any displacement, velocity, and acceleration change. In the steady state, the accelerometers detect only the gravitational acceleration (9.81ms.’). Consider that there is the gravitational acceleration vector g detected in the device coordinate frame as shown in Figure 7(a), the world coordinate frame acceleration can be expressed by the rotations of detected signals along the x, y and then z axes. After the transformation, the acceleration value on z-axis is defined to be - g as shown in Figure 7(b).
g
g
-gy
in (3),
(4)
Hence we can transform the sensed signals to the world coordinate frame by
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where
[x,
y,
zdlT is the acceleration
detected during measurement and [x
y
z]'
vector
Laptop with MSD Interface
-
is the
corresponding calibrated acceleration vector in the world coordinate frame. An experiment was performed to show the significance of the 3D self-calibration algorithm. In this experiment, the WMOSS was mounted on a person's fingertip. The person moved his fingertip along a circular path that was perpendicular to the gravity. The raw acceleration signals and the 3D self-calibrated (s-c) acceleration values are shown in Figure 8. In order to show the experimental result clearly, only the signals in x-y plane are plotted in Figure 9. A clear circular path (blue) of the calibrated signals can be seen in the figure. It shows that the 3D self-calibration.algorithm can successfully and automatically eliminate the Signal offsets and the scaling errors due to the misalignment ofthe device orientation.
Figure 8: The measured time-sequence acceleration data and the corresponding 3D self-calibrated data in x, y and z directions.
v.
Wireless Receiving Module
__Motion Sensing Module
-Running
Machine
Figure IO: Experimental setup for the human motion detection.
4.2. Experimental Results Two experimental subjects were selected to perform the same mnning speed experiment (-4k"hr) for 2 minutes. The WMOSS interface (shown in Figure 4) was used to help us to analyze the experimental data using PSD analysis. The experimental results for the hand motion of two experimental subjects are shown in Figure I I and Figure 12. The acceleration signals in xyz directions for first 10 seconds and the PSD in frequency domain are shown. In Figure 11, the acceleration of the hand of the experimental subject, (we refer to him as subject A from here on). and the PSD are shown. The peak-to-peak accelerations in z and y directions are much higher than the x direction. Since Subject A moved his hand on the zy-plane, the movement of hand in x direction is much less then other two directions. Furthermore, Subject A moved his hand horizontally during the running experiment. It tums out that the acceleration in the y direction was a bit higher than the z-direction.
Figure 9: Comparison between a 3D self.calihrated acceleration path and its raw acceleration path in the x.y plane.
EXPERIMENTS ON DETECTION OF HUMAN MOTIONS
As mentioned, WMOSS can be used in sports science In this section, an experiment was applications. performed using 2 experimental subjects. The motion of their hands was measured during running. The aim of this experiment is to find the physical characteristics of human motion during the running exercise. The results are shown below. 4.1. Experimental Setup In this experiment, the subjects held the WMOSS on their hands in order to measure the hand motion during running. The experimental setup is shown in Figure 10. The 3D signals in xyz directions are transmitted to the computer wirelessly. The notebook computer was used to display and store the experimental data. The Data acquisition schematic for this experiment is shown in Figure 2.
Figure 11: Experimental results for the hand motion of experimental Subject A during running at 4km/hr speed for 2 minutes.
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The PSD graph in Figure 11 indicates the resonance frequencies of the hand motion during the running experiment. As shown, the first and second mode resonances (-1.2Hz and -2.4Hz) are the highest peaks in the PSD. Also, the acceleration PSD in the y direction is higher than the z direction and the acceleration PSD level in the x direction is the lowest. On the other hand, the peak-to-peak accelerations in z direction are much higher than the y and x directions for a different experimental subject (Subject B) as shown in Figure 12. Ac~elerationinXyZdireCtloN
-“ 7 1Hand motion
VI. CONCLUSION The acceleration-based wireless motion sensing =stem (WMOSS) was developed successfully and was employed to detect motion characteristics such as resonance frequencies, acceleration, and motion direction of human subjects. Also, a real-time interface called “WMOSS interface” was created to store, display and analyze signals in real-time. Methods such as’ the initial calibration and signal low-pass filtering were applied to reduce the errors in the sensed signals significantly. The experiments showed that the WMOSS is capable of real-time orientation detection of various human motions. With a 3D self-calihration algorithm, the WMOSS was made to self-modify its axes orientations with respect gravity such that the signal error due to inclination angle can be eliminated. Experiments were also conducted to show the characteristics of the human hand motion during running exercise, including the resonance frequencies of the motion and the PSD analysis in fiequency domain. With further development, several WMOSS can he built to form a wireless motion sensing network and allow the motion analysis between various joints of a human body, and allow scientists to analyze complex human motions in much more detail in the future.
Figure 12: Experimental results for the band motion of experimental subject B during running in 4kmihr speed for 2 minutes.
REFERENCES Alan H. F. Lam, Wen J. Li, Yunhui Liu, and Ning Xi, “MIDS: Micro Input Devices System Using MEMS Sensors”, Proceedings of 2002 IEEE/RSI International Conference on Intelligent Robots and Systems, Switzerland, October 2002, pp.1184-1189. S. Yonemoto, A. Matsumoto, D. Arita, and R. -I. Taniguchi, “A real-time motion capture system with multiple camera fusion”, Proceedings of I999 International Conference on Image Analysis and Processing, 27-29 Sept. 1999, pp.600-605. Jihong Lee and lnsoo Ha, “Sensor Fusion and for Motion Captures using Calibration Accelerometers”, Proceedings of the 2002 IEEE/RSI International Conference on Intelligent Robots and Systems, Detroit, Michigan, May 1999, pp. 1954-1959. S. Yonemoto, D. Arita, and R. Taniguchi, “Real-time human motion analysis and IK-based human fieure control”. Proceedings o f 2000 Workshop on Human Motion, 7-8 Dec. 2000 pp. 149-154. Alan H. F. Lam, Raymond H. W. Lam, Wen J. Li, Martin Y . Y . Leung, and Yunhui Liu, “Motion sensing for robot hands using MIDS”, Proceedings of 2003 IEEE International Conference on Robotics andiiutomation, September 2003.
These results inferred that Subject B moved his hand along the z-axis more than the movements in the x and y directions. This means that Subject B moved his hand vertically in the z direction much more than Subject A. Thus, the result of the peak-to-peak acceleration in z direction for this subject is much higher than the x and y directions. The resonance frequencies of the hand motion of Subject B during running are shown in the PSD graph in Figure 12. The acceleration PSD level in z direction is much higher than the x and y direction. Also, same as for subject A, the result indicates that subject B has first and second mode resonances at -1.2Hz and -2.4Hz, respectively. This may be due to the fact that both two subjects have done the experiment of the same running speed, i.e., the moving frequencies of the two subjects are similar, thus, the resonance frequencies of their hand motions are similar. From the above experimental results, it is clear that the acceleration and the PSD information can reflect the physically characteristics of the human hand movement during a running exercise. In addition, as shown by the experimental results, by checking the dominating acceleration direction, an athlete’s running style can be recorded, checked and improved. The above findings show that the WMOSS is potentially a powerful tool for motion sensing in sports science.
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