A Novel Device for Measuring Fluctuations in Stride Intervals of Human Gait

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1 Bulletin of the Osaka Medical College 48 1, , Original Article A Novel Device for Measuring Fluctuations in Stride Intervals of Human Gait Manabu MIYAMOTO and Masahiko SHINDO 1 First Department of Physiology, Osaka Medical College, Takatsuki, Osaka, , Japan Key Words : Fluctuation, Stride intervals, 1/f, RS232C, Self-correlation ABSTRACT Fluctuations in stride-to-stride intervals have been already pointed out. However, further studies of this issue have not progressed in comparison with that of heartbeat because of difficulties both in measuring and in analyzing bilateral walking rhythms. These data had been usually recorded on an analog recorder. Subsequently, such an ambulatory recording machine sometimes influences original fluctuations. We developed a novel device for direct measurement of stride intervals of human gait to investigate gait disturbance caused by many central nervous system disorders. Our system used digital processing with an RS232C terminal. The flat and flexible tape switches are taped to both heels. A threshold of the force required for switching is adjustable. This system is not so expensive and is portable. The measurement is possible even at great distance from a subject via using a radio. Protocols for data collection can be changed. Our spectrum analysis showed long-range correlation. The slopes with respect to power spectrum and frequency in log-log plot are (n=5) for right foot and (n=5) for left foot. These results suggest that stride intervals showed self-correlation, and the analysis of the synchronous mechanism for each pace of the right and left cycles is expected to develop. The accuracy of the sampling time was also examined. The measurement at a sampling rate of 10msec is recommended. INTRODUCTION Since fractal fluctuations in heartbeat intervals have been reported (GOLDBERGER et al, 1990), it has been widely recognized that examples of fractal are abundant in nature. Under healthy conditions, functional oscillations, such as respiratory rhythm and electroencephalogram in addition to heartbeat intervals, also show the presence of long-range self-similar correlations. For example, the heartbeat interval at any time depended on that at previous time, and this dependence decayed in a scale-free (fractal-like) power-low fashion. However, the fractal dynamics disappeared in the patients with heart diseases. (AKSELROD et al, 1981). Following this finding, the spectrum analysis of heartbeats advanced greatly and was applied in a clinical setting. On the other hand, fluctuations were discovered in human gait as well. The gaits of healthy young adults have been reported to be scale-free with long-range correlations. The breakdown of longrange correlations occurred during metronomicallypaced walking was investigated by Hausdorff (HAUSDORFF et al, 2001). He also reported the effects of advanced age and Huntington's disease, a 19

2 20 M. MIYAMOTO, M. SHINDO neurodegenerative disorder of the central nervous system on stride interval correlations. Further research on the phenomena on stride interval correlations has not progressed in comparison with that of heartbeat very much, because of difficulties in measuring stride intervals. Moreover, left and right rhythms complicate measurements. Various methods of measuring stride intervals have been attempted previously. Stride interval was defined as the time between the heel strike of one foot and the next heel strike of the same foot. To measure the stride interval, usually force-sensitive switching devices are taped beneath both heels. Electric changes evoked by contact of heels are usually recorded in the magnetic tape in analog. Then, the recorded gait signals were digitized with an AD converter, and the intervals between heel strikes were computed. Because the noise may affect the results, the analogue-type recording machine was recommended to be able to take data at a rate faster than 416Hz (HAUSDORFF, 1992). It had been impossible for direct digital-data processing. However, with the development of computer, it became possible to record stride intervals precisely and to digitize data directly. In this study, we developed a novel measurement device by assembling the latest parts and made the new software for analysis, and examined its performance. METHOD 1 Devices attached to a subject. The tape switch (151-BBW, Tape Switch Japan Company) was used as the sensor to capture the times of landing of heels. The size of the tape part was adjusted to 14.3mm 620mm 1.4mm. They were covered by paraffin film (PARAFILM, American National Can Company) and attached to the right and left heels using a 2.5 cm-wide adhesive tape (3M Company) as shown in Figure 1. Several 15 cm-long stripes of the tape were used to fix the tape switch on each foot as been partially superposed upon each other. We confirmed that there was not drift of the sensor in walk. Each lead of the switch was connected to the wireless communication device with leads of 1mlong. We used the wireless network adaptor (WNA-RS, I-ODATA Corporation), which was a 2.4GHz spectrum diffusion-type: Direct Sequence Spread Spectrum (DS-SS). Serial communication at a maximum communication speed of 115.2kbps is possible by radio. The terminals of the right tape switch were connected to 6-pin and the ground, and those of the left tape to 8-pin and the ground with the Recommend Standard number 232 C (RS232C) standard. Then, the setup of the wireless network adaptor on the subject is set in the terminal adapter (TA) mode; therefore, RS232C was connected in the cross-wiring mode. This adapter driven by alkali dry battery in series (6.4V) was fixed the subject on the lumbar region with the band and pocket.(figure 2) 2 Hardware in the data-receiving end. (Data incorporation end) Another wireless network adaptor of WNA-RS was used as the wireless communication hardware on the data-receiving end. A data incorporation computer is a Notebook conformer (IBM ThinkPad X22, IBM Japan; CPU: Pentium III, 533MHz). A Universal Serial Bus (USB) Serial adapter (URS-03, PLANECS COMMUNICATIONS CORPO- Fig. 1 Installation of the tape switches to both sole. Fig. 2 Apparatus for measuring stride-to-stride intervals in a human gait. Data-sending devices were composed of the switches and the wireless network adaptor. Data-receiving notebook computer was equipped with the other wireless network adaptor and USB Serial adaptor. 20 Bulletin of the Osaka Medical College 48 1, , 2002

3 A Novel Device for Measuring Fluctuations in Stride Intervals of Human Gait 21 RATION) connects between WNA-RS on the datareceiving end and the USB terminal. The other end of the D-sub 9 terminal was connected to the RS232C terminal (D-sub 9 pin-female) of the wireless network adapter on the receiving side. This adapter was set in the personal computer (PC) mode. (Figure 2) The program for data incorporation was developed using Microsoft Visual Basic 6.0 using timer function. The communication conditions of this RS232C were bit rate, 9600bps; parity, N; data length, 8; stop bit, 1. The graphical user interface was adopted in the program for sampling. A sampling rate (a generation time of the timer function) could be set in the text box. This program was compiled and used as execute file. Its counter increased by 1 for every sampling, and the total number of this counter was substituted for time. The counter numbers at moments when the switching device became on (1) from off (0) were recorded on each side. Data were stored in using the ASCII format when a directory and a file name for storage was specified in the text box. The storage format consisted of the counter value of the left switch, tab break, counter value of the right switch and return as terminator. 3 Study of the accuracy of sampling rate. The accuracy of a sampling rate in this device was also studied. The intervals of function calls for timer can be changed by the context in the text box. The program started in the setup condition. At the moment when 1000 counts were reached, the total elapsed time was obtained using the calling system time function. breaks. Samples consisted of 128 points from the start of walkings, which were analyzed using a mathematical software (Mathcad, Mathsoft Corporation). The command of "pspectrum" as a built-in function in Mathcad was used to calculate the power spectrum of these data with the assistance of discrete FFT. The pspectrum (x, n, r, w) indicates as the follows, x is a signal vector, n is an integer, and 1 n length(x) is a segment of the input signal. r (0 r 1) is the partial overlap between segments, and w is the index that specifies the window handling function. Here, n=3, r=0.1 and the Hanning window handling was used. 4-1 Representative stride interval time courses Stride interval time course graph and their power spectra, were obtained for a sampling rate of 100msec, 50msec and 10msec and self-correlation functions were calculated. The appropriate sampling time and the fluctuation pattern of stride-to-stride intervals was determined. 4-2 Estimation of number of power The exponent of power with respect to the power spectrum and frequency was then estimated in a log-log plot in the case of the sampling time at 10msec. Table 1 Sampling time and total time taken for 1000 counts. 4 Measurement of stride-to-stride time intervals in volunteers Five healthy men without any history of neuromusculoskeletal disorders especially no aliment aberration of lower extremities such as genu varum or genu valgum and no medical history of sprains, participated in this study. The mean age was 43 years old (range 34-52yr). The mean height of the subjects was 1.68 m and the mean weight was 62.2 kg. The subjects were instructed to walk continuously on a level ground at a usual self-determined rate. Switching devices are attached to both of their heels. When they walked naturally wearing an ordinary pair of shoes, bilateral stride-to-stride intervals were simultaneously measured. The number of heel strikes counters for both sides was recorded in the text file with "tab" Bulletin of the Osaka Medical College 48 1, ,

4 22 M. MIYAMOTO, M. SHINDO RESULTS For the count up to 1000, the total time was measured for sampling times of 100msec, 90msec, 80msec, 70msec, 60msec, 50msec, 40msec, 30msec, 20msec, 10msec, 9msec, 8msec, 7msec, 6msec, 5msec, 4msec, 3msec, 2msec and 1msec. Results of these trial measurements are shown in Table 1. Sampling times showed accurate count times for a total time of more than 10msec. Even when the sampling times became shorter than 10msec, the total count times in the trial was the same as 10sec. (Table 1) The individual time courses of stride intervals for both feet in 3 cases out of the five subjects were shown in Figure 3. Their power spectra were also plotted in Figure 4. The logarithm of power was shown in the y axis and the logarithm of frequency was shown in the x axis. The slope was estimated by the least square fitting method. The slopes were (n=5) for the right foot and (n=5) for the left foot. That is, since the stride fluctuated showing the character of 1/f noise, the logarithm power was inversely proportional to the logarithm frequency with the slopes of near 1. Furthermore, these self-correlation functions were characteristic and coincide each another as shown in Figure 4. Figures 5-a and b showed the stride interval time courses taken at a sampling time of 50msec and their power spectra. Furthermore, Figures 5-c and d showed the time courses at a sampling time of 100msec; fluctuations could be detected even at this sampling time. However, it was recommended to analyze at a sampling time of less than 50msec, in particular, at especially 10msec. Fig. 3 Time courses of stride-to-stride intervals of actual gaits. Each scale for the right or left foot was indicated on each side of the figures. DISCUSSION Various phenomena with fluctuations in the living body have been reported in recent years (GOLDBERGER et al, 1990). Fluctuation in a gait cycle was already discovered (HAUSDORFF et al, 1995). Moreover, there are left and right rhythms in a walk, and the importance of research on the mutual relationship of left-right fluctuations was also pointed out. However, these studies have not progressed as much as compared with those of heartbeat because of difficulty in many factors for measuring the stride-to-stride period in locomotion. The conventional method of measuring the times taken for infinite steps is insufficient. In order to measure fluctuations during walking, we must measure the stride-to-stride intervals directly. In this paper, we developed a novel device for direct measurement of stride intervals of human gait to investigate gait disturbance caused by many central nervous system disorders. Various devices for measuring walking cycles have been developed previously. However, the force plate is only applicable to measurement of a few steps possible. On the other hand, both the built-in floor type (CROUSE et al, 1987) and the measuring-walkway (GARDNER et al, 1975) are expensive. Adjustment of the threshold force required for switching is difficult in an insole-type device (GIFFORD et al, 1987). Foot switch devices 22 Bulletin of the Osaka Medical College 48 1, , 2002

5 A Novel Device for Measuring Fluctuations in Stride Intervals of Human Gait 23 Fig. 4 Power spectrum analysis (Relationship between logarithm of power and logarithm of frequency) and Self-correlation function are more convenient but data intakes are usually in analog. (ALEXANDERl et al, 1990; LIGGINS Abet al, 1991; MINNS RJ et al, 1982; ROSS JD et al, 1987; WINTER DA et al, 1972) Intermediate recording into a magnetic tape with an analog is not satisfactorily performed, and the recording machine sometimes affects the data. On the other hand, our measure system for stride intervals by digital type with application of the RS232C standard can digitalize time intervals directly at the time of sampling. A maximum resolution at a sampling time of 10msec can be attained. Each basic command is executed in synchrony with the clock of a computer in the order of 500MHz. Many factors such as interruptions may stagger the sampling time, but a shift in time is negligible in the order of millisecond. We can measure time intervals precisely using our device without any mechanical fluctuations. Furthermore, the followings are its features. The flat and flexible tape switches are used and attached to both heels. The threshold force required for switching on the device can be adjusted by choosing materials (2.2N in this cases). This system is not so expensive because its components are available in the market, and it is portable and its use is not restricted both by place and by condition. A person can obtain measurements at a distance via a radio. There is no problem with the subject wearing an ordinary pair of shoes. The subject is not restricted by wiring. Protocols for measurement can be changed when the program of the computer is changed. Signal processing can be carried out using the same computer as the receiver. However, there were some defects of this device. Didn't the place of the switch change in walk? Was fixation and fixative intensity of 3M tape sufficient? In anyway it was necessary to confirm that the places of the switches did not slip off after walk. Bulletin of the Osaka Medical College 48 1, ,

6 24 M. MIYAMOTO, M. SHINDO Fig. 5 a Time courses of stride intervals at a sampling time of 50msec b Power spectrum analysis c Time courses of stride intervals at a sampling time of 100msec d Power spectrum analysis Each scale axis for each foot was set on each side. Notice that their 0 line levels are different in right and left stride intervals. According to our unpublished data of simultaneous accelerometry, "on" of the switches were synchronized very well with the acceleration curve in walking direction. Spectrum analyses of fluctuations in stride intervals were performed. They had the characteristic of 1 / f fluctuation as shown by the linear relationship between the logarithmic power spectrum and logarithmic frequency. The slopes were (n=5) for the right side and (n=5) for the left side. It was suggested that stride intervals show chaos, and the analysis of the synchronous mechanism for both rhythms of the right and left is expected to develop. The accuracy of sampling time was also examined. The measurement at a sampling rate of 10msec is recommended. REFERENCES AKSELROD S, GORDON D, UBEL FA, SHANNON DC, BERGER AC and COHEN RJ: Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 213, 220, 1981 ALEXANDER I, CHAO EY and JOHNSON KA: The assessment of dynamic foot-to-ground contact forces and plantar pressure distribution: a review of the revolution of current techniques and clinical applications. Foot Ankle 11, , 1990 CROUSE J, WALL JC and MARBLE AE: Measurement of the temporal and spatial parameters of gait using a microcomputer based system. J. Biomed. Engng 9, 64-68, 1987 GARDNER GM and MURRAY MP: A method of measuring the duration of foot-floor contact during walking. Phys. Ther. 11, , 1975 GIFFORD G and HUGHES JA: Gait analysis system in clinical practice. J. Biomed. Engng 5, , 1983 GOLDBERGER AL, RIGNEY DR and WEST BJ: Chaos and fractals in human physiology. Scientific American 262(2) 42-51, Bulletin of the Osaka Medical College 48 1, , 2002

7 A Novel Device for Measuring Fluctuations in Stride Intervals of Human Gait 25 HAUSEDORF JM, PENG C-K, LADIN Z, WEI Y and GOLDBERGER AL: Is walking a random walk? Evidence for long-range correlation in stride interval of human gait. Journal of Applied Physiology 78, 349, 1995 HAUSEDORF JM, ASHKENAZY Y, PENG C-K, IVANOV P, STANLEY E and GOLDBERGER AL: When human walking becomes random walking: fractal analysis and modeling of gait rhythm fluctuations. PHYSICA A 302, , 2001 LIGGINS AB and BOWKER P: A simple low cost footswitch. J. Biomed. Engng 13, 87-88, 1991 MINNS RJ: A conductive rubber footswitch design for gait analysis. J. Biomed. Engng 4, , 1982 ROSS JD and ASHMANN RB: A thin foot switch. J. Biomechanics 20, , 1987 WINTER DA, GREENLAW RK and HOBSON DA: A microswitch shoe for use in locomotion studies. J. Biomechanics, 5, , 1972 Bulletin of the Osaka Medical College 48 1, ,