Measurement of Soft Tissue Deformation to Improve the Accuracy of a Body-Mounted Motion Sensor

Transcription

1 Measurement of Soft Tissue Deformation to Improve the Accuracy of a Body-Mounted Motion Sensor Tao Liu liu.tao@kochi-tech.ac.jp oshio Inoue Kyoko Shibata Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology, 185 Miyanokuchi, Tosayamada-cho, Kochi , Japan Skin deformation caused by muscle motion is a common source of error for body-mounted sensors. A new method of measuring joint angles using a combination of two-axial accelerometers and reaction force sensors is presented. In this study, the effect of soft tissue deformation was minimized using a new reaction force sensor that is bound onto the body segment. The force sensor was designed using a pressure-sensitive electric conductive rubber. A Fourier transform of the total pressure forces induced by the body-mounted motion sensor modules was implemented to analyze the frequency property of soft tissue deformation on the human body surface. We processed the data of two-axial accelerations measured by the accelerometers using the measurements of soft tissue deformation including the total pressure force and twodirectional coordinates of the center of pressure. An experimental study with ten subjects was implemented to verify the new sensor system proposed for estimating the joint angle of the knee. The effectiveness of this system is illustrated by the experimental results using an optical motion analysis system as a reference. If we use the accelerometers alone, the root mean square (RMS) difference and the coefficient of multiple correlation (CMC) over all the subjects walking at each of the three speeds (slow, average, and fast) are deg and , deg and , and deg and , respectively. If we compensate for soft tissue deformation using the surface pressure measurements, the RMS difference and the CMC in each of the three conditions are deg and , deg and , and deg and , respectively. Measurement results of the developed sensor system showed high correlation with results from two alternative methods including an optical motion analysis system and the goniometer system in walking analysis experiments. The results support the effectiveness of the proposed method in the measurement of the flexion and extension angle of the knee. The compensation for soft tissue deformation using the surface pressure measurements improved the accuracy of the body-mounted sensor in the experiments. DOI: / Keywords: muscle motion, reaction force sensor, accelerometer, knee 1 Introduction In human dynamic analysis based on measurements of orientations of body segments, body-mounted motion, and force sensors Manuscript received June 18, 008; final manuscript received July 30, 009; published online August 8, 009. Review conducted by Vijay Goel. Paper presented at the IEEE /ASME International Conference on Advanced Intelligent Mechatronics AIM008. have gradually been considered more and more for clinical applications. This is due not only to the tremendous increase in research in this area but also to the large number of companies that recently started investing aggressively in the development of wearable products for the applications 1. Some cheaper and more comfortable body-mounted sensor systems with multisensor combinations like force-sensitive resistors, inclinometers, goniometers, gyroscopes, and accelerometers were proposed to implement the measurements of motions and forces for the human analysis. A quantitative analysis of human motion was investigated by Bonato 1 and by using the assembled multiaxis accelerometer sensors, a measurement system was made for the estimation of three-dimensional 3D position and orientation of a body segment. In another study, Tong and Grant 3 proposed a measurement device using two gyroscopes, one placed on the thigh and the other on the shank, which could estimate the rotation angle of the knee during walking. Pappas et al. 4 utilized a detection system consisting of three force-sensitive resistors, which could measure the force loads on a shoe insole, and a gyroscope, which could measure the rotational velocity of the foot. There is growing interest in developing commercial products using body-mounted sensor systems; for example, the MTx solutions furnished by sens sens, Enschede, The Netherlands 5, which produced a motion sensor based on three angular rate sensors, three accelerometers, and three magnetic sensors, which can reconstruct the angular displacement by means of a dedicated algorithm. The above techniques have been focusing on estimation of the orientations or joint angles using different inertial sensors but they share the drawback that the inertial sensors of gyroscopes and accelerometers suffer from fluctuating offset induced by temperature change or small changes in the structure mechanical wear. Luinge 6 proposed a Kalman filter that fuses triaxial accelerometer and triaxial gyroscope signals for ambulatory recording of the human body segment orientation. However, when accelerometers are used in clinical applications a complex calibration procedure is impractical and can cause misuse. In our research, we integrated a soft tissue deformation measurement with a developed body-mounted motion sensor 7 to estimate joint angles of the human lower limb. The goal of the study was to reduce the effects of noise due to body soft tissue deformation on motion sensors like accelerometers and to improve the accuracy of joint angle measurements. Methods and Materials In this section, a new design of the soft tissue deformation measurement device is proposed. Moreover, a body-mounted motion sensor and its algorithm for estimating the joint angle of the knee are introduced. Based on the new sensor system, the experimental study on the measurement of soft tissue deformation for improving accuracy of the body-mounted motion sensor system is described..1 Reaction Force Sensor for Measuring Soft Tissue Deformation. We proposed a new method to analyze soft tissue deformation from measurements of pressure distribution between the body-mounted sensor and the human body. A human body reaction force sensor constructed of four pressure-sensing cells can measure soft tissue deformation during movement. As shown in Fig. 1, an elastic strap was used to fix the reaction force sensor on the lower limb in which four contact points of the sensor can measure pressure force distribution induced by soft tissue deformation. The sensing cells in the reaction force sensor were designed using the pressure-sensitive electric conductive rubber PSECR, which has been used for measuring force distribution. The PSECR has been developed for the sheet-switch of the electronic circuits and has a unique property in that it conducts electric current only Journal of Medical Devices Copyright 009 by ASME SEPTEMBER 009, Vol. 3 /

2 Pectinate circuits Muscle Contact point of pressure sensor PSECR Base-board for motion sensor Elastic support trap Fig. 1 Soft tissue deformation measurement Sensing point (Contact point of pressure sensor) Fig. 3 Prototype of reaction force sensor when compressed and acts as an insulator when the pressure is released. The material properties of the PSECR are given in Table 1. Four pectinate circuits were made to construct one sensing matrix in the reaction force sensor. The interface of the sensor was designed to measure reaction force distribution on its base plate by four-positional pressure force inputs Fig.. The total pressure force F and the coordinates of the center of pressure COP including x COP and y COP were calculated using the measurements of the four pressure-sensing cells as follows: F = F 1 + F + F 3 + F 4 1 M x = F 1 + F F 3 F 4 L/ M y = F 1 + F 4 F F 3 L/ x COP = M y /F y COP = M x /F 5 where F i is the measured pressure force of each support point i =1,,3,and4 and M x and M y are the two-directional moments Table 1 Material properties of PSECR Color gray-black Tensile strength 1.86 MPa Elongation at break 0% 100% modulus 0.86 MPa Tear resistance 7 kn/m Hardness in durometer on the sensor plate. As shown in Fig. 3, a prototype of the reaction force sensor was developed and we mounted a motion sensor module designed with two multiaxial accelerometers on it to measure angular displacement of the knee.. Motion sensor module. As shown in Fig. 4, a motion sensor module was designed by integrating two accelerometers MM-860, made by Freescale Semiconductor, Tokyo, Japan. The sensor can work under low energy consumption 800 A at 5V, which includes signal conditioning modules and a power regulator so it is appropriate for ambulatory measurements. The two accelerometers were attached on a base board plate to measure the two-directional accelerations along the tangent direction of the x-axis and the sagittal direction of the y-axis when mounted on the lower limbs. In this motion sensor system, the two-axial acceleration information from the two accelerometers was fused to estimate the angular displacement of the attached segment. As shown in Fig. 5, the motion sensor system includes two sensor modules composed of two accelerometers and a reaction force sensor. The two sensor modules were mounted on the thigh and shank, respectively, and a data sampling logger was attached to the waist. Each sensor module including two accelerometers only performs measurements of acceleration in a segment-fixed reference frame during walking. As shown in Fig. 6, two local coordinates were defined for the sensor modules mounted on the thigh and shank. Considering the D model of a segment s motion, vector R a,r b indicates the distance between the sensor module and the knee joint, while the D local coordinate systems x t y t and x s y s represent the orientations of the sensor modules with re- Flexible rubber PSECR Small pectinate electrode 10mm D L F1 F Top view Z Z F4 L=0mm Fig. Reaction force sensor for measuring soft tissue deformation F3 Side view Reaction force sensor Accelerometer Fig. 4 Prototype of motion sensor module / Vol. 3, SEPTEMBER 009 Transactions of the ASME

3 Data logger Battery (9V) Sensor module on thigh 16-channel digital switch Sensor module on shank Micro-computer (H8 3964) Wireless module (ZIG-100B) Fig. 7 Hardware system of data sampling spect to the body segments. We aligned the y-direction along the line connecting the segment s proximal and distal joints and let the x-direction be the anterior-posterior direction. For the simplified model, the human body segments were considered rigid. We analyzed the motion of the sensor module fixed on a body segment by dividing the motion into the linear motion of the segment s rotation point and the angular motion of the sensor module around the rotation point. Therefore, to estimate the joint angle, the first step was to calculate two-directional accelerations of the knee joint in each local coordinate system using measurements of the sensor modules fixed on the body segments; the calculation equations are given as follows: Fig. 6 Fig. 5 Body-mounted sensor system. a K x,a K y s = a A1 x,a A1 y s R a + D s, R a + D s a K x,a K y s = a A x,a A y s R a s, R a s a K x,a K y t = a B1 x,a B1 y t R b t, R b t Sensor module B Sensor module A A 1 A D R b B x s B 1 y s y t R a x t Local coordinate of Sensor module on the thigh D Knee joint Local coordinate of Sensor moduleontheshank Local coordinate systems of the sensor modules a K x,a K y t = a B x,a B y t R b + D t, R b + D t 9 where a A1 x,a A1 y s, a A x,a A y s, a B1 x,a B1 y t, and a B x,a B y t are outputs of the accelerometers obtained from the two sensor modules fixed on the body segments of the shank and thigh, respectively, a K x,a K y t and a K x,a K y s are the knee joint accelerometers, which were calculated from measurements at the two local coordinates on the attached segments, and s and t are defined as angular velocities of the shank and thigh relative to the axis that is perpendicular to the x y plane. The next step was to calculate the joint angle between the two segments using the two sensor modules fixed on each segment. Since one point should physically have a unique acceleration, the two calculated accelerations at the local coordinates at the same rotation joint should be equal. a K x,a K y t = R a K x,a K y s 10 where R is the axis rotation matrix of the calculated joint accelerations that relate the two connected local coordinates..3 Data Sampling and Signal Processing. As shown in Fig. 7, an integrated hardware system was developed and incorporated into the sensor system for the data sampling and signal processing. The pressure forces applied to the flexible interface of the reaction force sensor were converted into resistance changes in the sensing cells by the PSECR. Then the resistance changes were converted into voltage signals by conditioning modules and were amplified by amplifier modules. The conditioned voltage signals from the reaction force sensor and accelerometers were fed into a personal computer after A/D conversion 10 bits using a microcomputer H Eight cells for pressure-sensing in the two reaction force sensors were used to measure soft tissue deformation, and eight acceleration outputs were used to estimate the joint angle, so there were in total 16 channels of voltage signals. A pair of wireless modules ZIG-100B was used for data communication between the microcomputer and a personal computer and to connect the RS3 ports instead of the common serial communication cable. A data processing program designed using MATLAB software The Mathworks, Natick, MA was utilized to calculate the joint angle by fusing the measurements of soft tissue deformation and accelerations. A Fourier transform of the total pressure forces induced by the body-mounted motion sensor modules was used to analyze the frequency property of the human body surface soft tissue deformation. We designed a one-order band pass filter to reduce the effect of soft tissue deformation on the measurements of accelerometers. The Fourier transform results were used to determine band values of the band pass filter for accelerometers. Moreover, the disturbance acceleration errors caused by soft tissue deformations d x COP /dt and d y COP /dt were subtracted from the two-axial accelerations measured using accelerometers in each Journal of Medical Devices SEPTEMBER 009, Vol. 3 /

4 Accelerometers Band filter + - Joint angle FFT discrete Fourier transform Total pressure force Cop d x cop & dt d y cop dt Reaction force sensor (Muscle motion) Fig. 8 Data processing for the accelerometers mounted on human body. local coordinate system. Figure 8 gives the architecture of the accelerometer data processing method using the measurements of soft tissue deformation including the total pressure force and twodirectional coordinates of the COP..4 Experimental Methods. To validate the sensor system performance, we compared the measurement results of the developed sensor system with measurements by a commercial optical motion analysis system, Hi-DCam NAC image technology, Japan and a joint angle measurement system, GonioMeter SG150 Biopac Systems, U.K.. The optical motion analysis system tracked and measured 3D trajectories of retroreflective markers placed on the subject s body surface. The cameras with a sampling frequency of 100 Hz were used to track marker motions to an accuracy of 1 mm. Since the motion measured using surface mounted optical markers is also fraught with soft tissue deformation of the body, we also used the GonioMeter system, constructed by resistive strain gauges, to measure the joint angle of the knee as another reference measurement. To align the body-mounted sensors, we divided the process of attachment to the leg into three steps: First, to restrain the distance of the two sensor modules, we fixed the modules to a frame, which could slide along a vertical pole. Second, the height of the frame was adjusted along the vertical pole to align the center of the two modules with the knee joint and the sensing plane of the two sensor modules with the frontal-medial aspect of the shank and thigh. Last, four ropes with magic tapes at the end were used to attach the sensor modules to the body to keep the two sensor modules in the same plane vertical to the ground, and then we removed the modules from the frame see Fig. 9. An experimental study was implemented to verify the new sensor system proposed to reduce the effects of soft tissue deformation on the estimation of the joint angle of the knee. Ten healthy subjects one female, nine males, aged yrs mean SD, height of 1.71 m 0.05 m, and mass of Pole Sensor module Frame GonioMeter Reflective mark First step Second step Last step Fig. 9 Sensor alignment Rope Fig kg 16. kg participated in the study. They performed flat walking trials when wearing the developed sensors and their usual shoes. The signals from the three measurement systems were simultaneously sampled at 100 samples/s. In order to examine the effects of soft tissue deformations on the joint angle estimation algorithm, each subject performed one walking trial at three speeds including slow, average, and fast. The soft tissue probably moves the most during the initial contact phase 8, so the knee angles calculated using the proposed method and the two alternative methods at ground contact initial contact phase for each of the three walking speeds were examined in particular. Based on the measurements of the segment s angle using the optical motion analysis system, we can detect the initial contact phase because the maximum angular displacement between the shank and the vertical axis gravity direction appears during this phase. 3 Results Cop accelerations obtained from two sensor modules. The coordinates of the COP for each sensor module were calculated from the measurements of pressure force distribution. As shown in Fig. 10, two-directional accelerations of COP were calculated from the double differential of two components of the COP displacements in a walking experiment and we noted that the accelerations increased with the different human motions that cause soft tissue deformation. The measured body reaction forces see Fig. 11 and their Fourier transform results for the shank and thigh during walking can be used for analyzing the effect of soft tissue deformation on the measurements of the body-mounted sensors. The band pass filter values of 5 0 Hz were determined from the Fourier transform results of a walking trial. The joint angles measured using the optical motion analysis system and the goniometer are most similar to the angles calculated using the accelerometer data that compensate the for noise due to soft tissue deformation Fig. 1. For the quantitative comparison between the sensor system and the two alternative methods, the RMS differences and the CMC were used to compare the closeness in amplitude. According to the comparison results from the measurement of the optical motion analysis system, if we use the accelerometers alone, the mean RMS difference and CMC for all ten subjects walking at each of the three speeds slow, average, and fast are deg and , deg and , and deg and , respectively. If we compensate for soft tissue deformation using the surface pressure measurements, the mean RMS and CMC in each of the three conditions are deg and , deg and , and deg and , respectively. Since the motion measured using sur / Vol. 3, SEPTEMBER 009 Transactions of the ASME

5 Fig. 11 The pressure force response and the Fourier transform face mounted optical markers may also be fraught with soft tissue deformation, we compared the results of the developed sensor with the measurements of a GonioMeter constructed with resistive strain gauges. If we do not consider compensation of soft tissue deformation, the mean RMS and CMC for the seven subjects walking at each of the three speeds are deg and , deg and , and 6..8 deg and , respectively. If we use the proposed compensation method, the mean RMS and CMC are deg and , deg and , and deg and , respectively. We noted that the RMS difference increases with walking velocity for both measurement devices and that the RMS difference between the sensor system and goniometer is smaller than that between the sensor and the optical system for each walking speed. Moreover, the agreement is always better when we use the soft tissue deformation algorithm that we developed rather than using only the accelerometers see Fig. 13. Moreover, the means of the knee angles calculated using the proposed method and the two alternative methods at ground contacts in a walking trial for each of the three walking speeds are summarized in Fig Discussion and Conclusions To resolve the problem of soft tissue deformation, a common source of error in body-mounted devices, we proposed a new Fig. 1 Comparison between the body-mounted sensor system and the two alternative methods using the optical motion analysis system and the joint angle sensor GonioMeter Fig. 13 The RMS difference and CMC of the knee angle measurements of ten subjects we used two reference measurement systems, the Hi-Dcam system and the goniometer system, to verify the body-mounted sensor system: a the RMS difference and b the CMC Journal of Medical Devices SEPTEMBER 009, Vol. 3 /

6 Fig. 14 The knee angles calculated using the proposed method and the two alternative methods at ground contacts during walking trials for each of the three walking speeds method including sensor alignment and measurement of the response of the human body s surface soft tissue. The results support the effectiveness of the proposed method in the measurement of the flexion and extension angle of the knee and the compensation for soft tissue deformation using the surface pressure measurements improved the accuracy of the body-mounted sensor in the experiments. As shown in Fig. 13, the RMS difference obtained from the soft tissue measuring algorithm is smaller than the comparison results obtained from accelerometers alone and the CMC is large when the soft tissue measuring algorithm is utilized. Therefore, the multiple tests on multiple subjects on the developed sensor system with the soft tissue measuring algorithm showed a high correlation with the results obtained from the two alternative methods in the multistep walking validation experiments. Moreover, in the statistical analysis of the comparison results of the three walking conditions, we obtained almost the same RMS and CMC so the proposed algorithm was robust to the different walking speeds that can cause different body motor motions and soft tissue deformations. We particularly examined the angles calculated using each of the three methods at ground contact for each of the three walking speeds. The differences between the angles obtained with the three methods after compensating for soft tissue deformation were small for all the three conditions. The remaining difference may be due to the assumption that the wearable sensors were flat on the body surfaces and aligned with joints at both ends when measuring the body segments orientations. The errors may be even lower when positions of the lower limb s joint centers the ankle, knee, and hip are accurately estimated, because the error in indentifying joint center positions is about 5 mm, which can cause a maximum offset of 7 deg in the segment orientation estimation. We can accurately align the sensor system with the goniometer using the sensor alignment mechanism so the RMS differences with the goniometer are smaller than in the optical motion analysis that is sensitive to the marker position on the body segment see Fig. 14. The larger difference between the estimated angles using the joint angle sensor GonioMeter in the low speed and fast speed walking conditions indicates that the goniometer may not be suited to high-speed measurement of human motion, because of the larger vibration of the large strain transducer in the goniometer during high-speed human motion. In this paper, two-axis acceleration data from 3D accelerometers in the plane of interest were used to estimate uniaxial angular displacement of the knee. Although multiaxial measurement is more powerful, in many cases a simple uniaxial measurement may be effective as well, giving useful information on pathologies related to the knee, for example, hemiplegia induced by stroke. The reported level of accuracy joint angle estimation error of about 4 deg should be good enough to be able to make these diagnoses. The 3D information from triaxial accelerometers will be necessary for analyzing the 3D orientation of the body segment, and the proposed compensation method for reducing the effects of soft tissue deformation can also be applied to measure other 3D joint angles such as in the ankle by attaching body-mounted sensors on the shank and foot. As for kinematic analysis of some special subjects such as obese patients, in the future we will apply the new method of sensor alignment and compensation for soft tissue deformation to realize high precision measurements. Moreover, the sensor system will be tested on many types of human motion analyses such as jumping and stair-climbing and a comprehensive test of its impact resistance property will surely be helpful in promoting this system to clinical applications. Acknowledgment We are grateful to the anonymous reviewers for their important comments and suggestions. 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