Design of a Compact System Using a MEMS ... - IEEE Xplore

Design of a Compact System Using a MEMS Accelerometer to Measure Body Posture and Ambulation Kara E. Bliley, Daniel J. Schwab, David R. Holmes III, Paul H. Kane, James A. Levine, Erik S. Daniel, and Barry K. Gilbert Department of Physiology and Biomedical Engineering Mayo Clinic College of Medicine, Rochester, MN 55905 [email protected]

Abstract Interest in studying human posture, movement, and physical activity is growing due in part to the increasing prevalence of obesity. Accelerometers are commonly used in motion analysis systems to enable researchers to conduct studies outside of the traditional laboratory environment; however the available systems tend to be bulky and unsuitable for long-term studies. Therefore, a need exists for a physically robust, yet compact motion analysis system that can be easily worn for an extended time period without disrupting the person’s range of motion. Here we describe our on-going efforts to develop a robust, compact system that can measure body posture and movement using a tri-axial accelerometer, and then store this data on a secure digital memory card. This device can be easily configured to collect accelerometer data for specific applications in human motion analysis. In the future, this device will be used to study physical activity in free-living individuals.

I. INTRODUCTION Interest in studying human posture, movement, and physical activity is growing due in part to the increasing prevalence of obesity. Accelerometers are commonly used in motion analysis systems, and much research is being conducted to make these systems more portable for use on free-living individuals (1-5). These systems enable researchers to conduct studies outside of the traditional laboratory environment. For a thorough review of additional physical activity monitoring technologies using accelerometers, please see Chen and Bassett 2005 (6). Furthermore, there exists a need for a

portable, physically robust, compact motion analysis system that can be easily worn for an extended period without disrupting the person’s range of motion. Here we describe our on-going efforts to develop a robust, compact system to measure and store body posture and movement data.

II. METHODS Previously we described the design of a motion analysis logger unit using a microcontroller and MEMS accelerometer, which collects and stores acceleration data (1). The device was comprised of a MEMS accelerometer (KXM52-1050, Kionix Inc., Ithaca, NY), a low power microcontroller (Texas Instruments MSP430F1232) (Texas Instruments, Dallas, TX), and a mini secure digital memory card (SanDisk, Sunnyvale, CA). The device was powered by a single coin cell battery (CR2032, Panasonic, Seacaucus, NJ), and the components were housed inside of a plastic case. The device was demonstrated to be comparable to a commercially available accelerometer and data logger system (Crossbow Technology Inc., San Jose, CA) at distinguishing human subject posture and motion, and was an improvement over the commercial system in that it was much smaller in size and did not have any external wires. Although this firstgeneration design was a significant improvement over existing technology, it still was not considered to be robust enough for extended use and not appropriate for mass production. In order to make the Mayo-developed firstgeneration unit better suited for studies on freeliving individuals in potentially rugged environments, it was necessary to integrate all of the components on a single small circuit board and encase these components properly to protect

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them from the external environment. For this second-generation design, several changes were made to the components in order to give this new system increased functionality and greater flexibility with respect to data collection for specific research applications. Before scaling the design down to the desired size, an “evaluation board” was assembled for use during software development. For testing purposes, the evaluation board is laid out to allow easy access to all pins on the board (Figure 1).

Figure 1. A prototype evaluation board of the system was designed for use during software development and testing. (21418) The main components are a tri-axial MEMS accelerometer (KXM52-1050, Kionix Inc., Ithaca, NY), a low-power microcontroller (MSP430F149, Texas Instruments, Dallas, TX), and a mini secure digital memory card (SanDisk, Sunnyvale, CA). A clock chip (MAX6902, Maxim Integrated Products, Sunnyvale, CA) with a separate power supply has also been added on this secondgeneration design (not shown in figure). We have created two slightly different versions of the system, one that employs a pair of 23 mm (CR2330) coin cell batteries connected in series for those applications in which the smallest possible size is important; and a second slightly larger version using a pair of 30 mm (CR3032) coin cells that will support much longer recording durations at the cost of slightly larger size. The smaller of the final designs is sized to fit into the case shown in Figure 2; the larger case has a similar appearance. The case was designed to protect the contents from dirt, moisture, and physical abuse. The buttons have been recessed to prevent accidental button pushes. At this time,

the case has been designed so that the memory card is not readily accessible, in that the case needs to be opened in order to expose the memory card. This packaging approach was adopted to prevent the wearer or other unauthorized individual from accidentally ejecting the memory card during data collection. Also, at this time, we expect that the individuals conducting the research will be the only ones who need to upload and download data from the cards. The case may be redesigned in the future to allow for easier access to the card while still providing some protection. In addition, the final version of the system may also be used by children, which influenced the appearance of our case design as well.

Figure 2. The outer case was designed to protect the device from dirt, moisture, and physical abuse. It measures 63.5 mm by 41.91 mm by 7.874 mm. (21182) Our first-generation design required that the accelerometer data acquisition program be loaded on the microcontroller using a JTAG programming interface by means of a computer. For the second-generation design, we have written an application programming interface (API) so that application-specific data acquisition programs can be uploaded to the microcontroller from a secure digital memory card inserted into the unit, eliminating the need for a computer. One advantage of our API is that files read from and written to the secure digital memory card are compatible with the FAT32 (file allocation table) file system, enabling the researcher or clinician to analyze the data collected by the unit using a number of common PC-based software programs. In addition, the user is not required to interact with the underlying software in this most recent design because application-specific software is uploaded automatically upon insertion of a


memory card. Although the unit described here uses a three-axis accelerometer for collecting data, the unit is configurable for specific applications and its design could be modified for other types of peripherals, such as pressure or temperature sensors.

III. RESULTS AND DISCUSSION Our previous first-generation design was used in bench and human subject testing. After testing our device at known orientation angles over the range of 0 to 360 degrees, we developed calibration equations for converting the raw data from the device into data with meaningful units of gravitational force (g) and then to angle (degrees). The devices were then attached to human subjects in our studies and used to detect various postures, namely lying down, sitting, and standing (Figure 3). Bench testing of our second generation design is now underway. When an axis of the accelerometer is aligned with gravity, it will return a value of either +/- 1 g depending on the orientation of the axis. Accordingly, when an axis of the accelerometer is perpendicular to the gravity vector, it will return a value of 0 g. Initial results collected by taking accelerometer measurements while rotating the device over the range of 0 to 360 degrees in both the x-y and y-z planes of the accelerometer are shown in Figure 4. Although we have not completed power consumption testing, preliminary tests and calculations suggest that the average current consumption is 0.965 mA when the system is actively sampling accelerometer data and writing it to the memory card. At present, the sampling rate is 10 Hz and data is written to the card once every 1.6 seconds. Total sampling time is 32 ms (2% duty cycle) during which time the system draws a current of 2.4 mA. A write operation lasts up to 14 ms (0.88% duty cycle) during which time the system draws a current up to 80 mA. The system is in standby mode for the remaining 97.22% of the time, drawing 0.167 mA of current. Based on these calculations, the smaller unit can collect data continuously for 9 days, and the larger unit for 20 days, before the batteries are depleted. These values are dependent both on sampling rate and how often data is written to the card. Data sample rates vary by intended use. Writes to the memory card are determined by how many samples can be collected and stored in a single 512-byte block. Part of this is dependent

Figure 3. Posture detection for (A) lying down, (B) sitting, and (C) standing using 2 of our first-generation devices, one placed on the lateral torso and one on the thigh. Detected angle of orientation versus subject number. Error bars are SD for repeat measures. Thresholds used for classifying the posture are marked with dashed lines. (21296) on the timestamp information attached to the data, whether the researcher needs the full timestamp for each sample or only needs it each time the data is saved. The format of the data also impacts how often a write must occur. Data samples are


currently written to the card in ASCII format, but we will also develop a version in which the data is written in binary format, which will significantly reduce how often data will be written and therefore significantly increase the battery life. Although we currently use a memory card with a storage capacity of 128 MB, we will evaluate the use of memory cards with greater storage capacity as they are increasingly available at lower costs.

ergonomics studies on lifting, evaluating athletes, evaluating arm movement in wheelchair users, or preventing back strain. In the future, we plan to use this device to study physical activity in freeliving individuals, under approved clinical protocols.

V. ACKNOWLEDGEMENTS The authors wish to thank S. Richardson, E. Doherty, D. Jensen, and T. Funk for assistance in preparation of figures; D. Bastian and S. Dierauer for assistance with text preparation; and K. Olson and D. Post for assistance with circuit board layout and other CAD support.

VI. REFERENCES [1] D.R. Bassett Jr., B.E. Ainsworth, A.M. Swartz, S.J. Strath, W.L. O’Brien, and G.A. King, “Validity of four motion sensors in measuring moderate intensity physical activity,” in Med Sci Sports Exerc, 2000, pp.S471-480. [2] G.M. Lyons, K.M. Culhane, D. Hilton, P.A. Grace, and D. Lyons, “A description of an accelerometerbased mobility monitoring technique,” in Med Eng Phys, 2005, pp. 497-504. [3] G. Plasqui, A.M. Joosen, A.D. Kester, A.H. Goris, and K.R. Westerterp, “Measuring free-living energy expenditure and physical activity with triaxial accelerometry,” in Obes Res, 2005, pp. 1363-1369.

Figure 4. A sample of calibration data collected using our evaluation prototype board. The data was collected while subjecting each axis of the accelerometer to accelerations in the range of +/- 1 g. The board was repeatedly rotated 360 degrees in the (a) x-y and (b) y-z planes of the accelerometer. (21424)

IV. CONCLUSION Unlike existing technology, the new device is smaller, physically robust, and especially user friendly. It can easily be configured to collect accelerometer data for specific applications in human motion analysis and may be modified for applications involving other peripherals. Specifically, this device could be used in

[4] K. Zhang, F.X. Pi-Sunyer, and C.N. Boozer, “Improving energy expenditure estimation for physical activity,” in Med Sci Sports Exerc, 2004, pp. 883-889. [5] K. Zhang, P. Werner, M. Sun, F.X. Pi-Sunyer, and C.N. Boozer, “Measurement of human daily physical activity,” in Obes Res, 2003, pp. 33-40. [6] K.Y. Chen and D.R. Bassett Jr., “The technology of accelerometry-based activity monitors: current and future,” in Med Sci Sports Exerc, 2005, pp. S490-500. [7] K.E. Bliley, D.R. Holmes III, P.H. Kane, R.C. Foster, J.A. Levine, E.S. Daniel, and B.K. Gilbert, "A miniaturized low power personal motion analysis logger utilizing MEMS accelerometers and low power microcontroller," in 3rd IEEE/EMBS Special Topic Conference on Microtechnology in Medicine and Biology, 2005, pp. 92-93.
