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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 16, NO. 3, JUNE 2008
Automatic Synchronization of Functional Electrical Stimulation and Robotic Assisted Treadmill Training Mark E. Dohring, Member, IEEE, and Janis J. Daly
Index Terms—Automation, functional electrical stimulation (FES), gait, neuromuscular stimulation, robots.
can be a provider of passive movement in the swing phase limb, and passive movement is not a desirable motor learning strategy. A second disadvantage could be that the Lokomat produced abnormal muscle activation timings in healthy controls [6]. In contrast, FES-IM can provide normal muscle activation timings. In fact, the combination of Lokomat and FES-IM promises a number of advantages including early practice of close to normal swing phase movement patterns accompanied by electrically induced muscle contractions at the proper timing for swing and stance phases of gait. We investigated manually initiating the FES-IM gait stance phase in synchrony with the Lokomat heel strike. This has proven feasible, but not ideal, since it is subject to human error. Therefore, the purpose of this research was to test the feasibility and reliability of automatically synchronizing FES gait pattern onset with the Lokomat gait robot heel strike.
I. INTRODUCTION
II. METHODS
Abstract—This work presents a means to automatically synchronize two promising gait training technologies to address gait deficits in stroke survivors: functional electrical stimulation using intramuscular electrodes (FES-IM) and the Lokomat robotic gait orthosis. A system of hardware and software was developed to achieve the automatic synchronization. A series of bench tests were performed to verify the feasibility and reliability of automatic synchronization. The bench tests showed that automatic synchronization of FES-IM to the Lokomat gait cycle was feasible and reliable. Automatic synchronization was more consistent than manually triggered stimulation (10-fold smaller standard deviation of latency), and produced no early or missed stimulations across 634 strides. Automatic synchronization had greater accuracy than manually triggered stimulation, producing stimulation timed to an accuracy of 2.5% of one gait cycle duration (heel strike ). to heel strike
= 100
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ORLDWIDE, 15 million people per year have a stroke. Five million per year are permanently disabled. Up to half of all stroke survivors do not regain functional independence. At six months poststroke, 30% are unable to walk without another person’s physical assistance. Conventional gait training does not restore normal gait in many stroke survivors. There are two promising, new gait-training methods. First, gait robot assist is promising in that it provides passive swing phase robotics assist, as well as weight support and robotic assistance during stance phase [1], [2]. Second, functional electrical stimulation (FES) with intramuscular (IM) electrodes (FES-IM) is promising in that it is clinically and statistically significantly advantageous in gait restoration compared with a comparable comprehensive gait-training program without FES [3], [4] . Each has disadvantages that are compensated by the other. One disadvantage of the FES-IM gait system is that it did not produce an absolutely normal swing phase limb flexion pattern for all subjects [5]. One disadvantage of the Lokomat is that it
Manuscript received July 12, 2007; revised December 12, 2007; accepted December 30, 2007. This work was supported by the Department of Veterans Affairs, Office of Rehabilitation Research and Development under Grant B4036I and Grant B5080S. M. E. Dohring is with the Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH 44106 USA (e-mail: markdohring@ieee. org). J. J. Daly is with the Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH 44106 USA and with the Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH 44106 USA (e-mail:
[email protected]). Digital Object Identifier 10.1109/TNSRE.2008.920081
In testing the feasibility, reliability, and consistency of automatic synchronization of FES-IM and the gait robot, the principle questions guiding the testing were: 1) feasibility of automatic synchronization, i.e., that latency must be short enough to ensure that the stance muscles are activated prior to loading more than 50% of body weight onto the stance limb. Otherwise, stance phase knee control practice would not be practiced during limb loading. According to published data for healthy adults [7], 50% of body weight is accepted after 5% of the gait cycle. Therefore, the acceptable latency must be less than or equal to 5% of the gait; 2) reliability of stimulus activation for each step, i.e., will stimulation occur at each and every step of the gait robot; and 3) consistency of stimulation activation onset, i.e., will the variance of stimulation start times be acceptable (at least as good as manual stimulation). A. FES-IM Technology Stimulation patterns were developed which activated eight, targeted muscles in the proper sequence for the gait pattern [3]. The stimulation gait patterns were handcrafted using an iterative process beginning with templates and customized for individual patient gait deficits. In the current research, a typical patient stimulation pattern was selected for synchronizing with the gait robot. The Lokomat produced a pulse at each right heel strike that was used to synchronize the FES to the robot. This pulse was routed to the Universal External Control Unit (UECU), which has external input/output lines that can be used in the software to trigger stimulation.
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B. Quality Control Bench Testing To test the latency between the desired stimulation onset and the actual stimulation onset of the newly developed UECU application software, the system was bench tested while connected to the Lokomat during a 30-min test run. This was strictly bench testing, and so there was no subject in the Lokomat during testing. 1) Analysis: Data from two 30-min test trials were recorded and analyzed to determine how quickly the UECU could respond to the Lokomat right heel strike gait event. We recorded the time instants of the trigger pulses from the Lokomat, , when the UECU recognized the trigger signal, , and when the UECU delivered the first stim. Our analysis of the outcome was ulation pulse, referenced to the Lokomat trigger pulses at the beginning of stance phase of the right leg. Equations (1) and (2) show the method of calculation of two latencies: the trigger recognition latency, i.e., the time from the right heel strike trigger pulse from the Lokomat to the recogni, and the first pulse tion of the trigger by the UECU, latency, i.e., the time from the right heel strike pulse to the delivery of the first stimulation pulse,
1
Fig. 1. Shows the trigger recognition latency, t , and the first pulse latency, Dtpulse, for two test runs using automatic synchronization of FES to the Lokomat gait cycle. For example, during walk trial 1 (solid line), for 118 of the ranged from 20 to 25 ms, and for 130 of the strides, the lastrides, t tency ranged from 25 to 30 ms.
1
TABLE I FIRST PULSE LATENCY
(1) (2) 2) Comparison to Manual Initiation of Stimulation: Manual synchronization of FES-IM and the Lokomat was tested by two clinicians. Each clinician initiated stimulation manually during a 30-min test run as he/she would during an actual training session using FES-IM and the Lokomat simultaneously. The la, was calculated for each clinician run, for comtency, parison to automatic stimulation. III. RESULTS AND DISCUSSION Automatic synchronization of FES gait patterns and the Lokomat was feasible and more accurate and repeatable than manually delivered electrical stimulation patterns during Lokomat use. A. Latency 1) Trigger Recognition Latency: Fig. 1 shows a plot of the distribution of the trigger recognition latency on the left. The mean trigger recognition latencies were 30.4 and 30.2 ms, respectively, for the two test runs. The minimum latencies for trigger recognition were 17.5 and 17.8 ms, respectively. The maximum trigger recognition latencies were 43.3 and 43.1 ms, respectively. The latency was uniformly distributed over a narrow range of 25.8 ms. The narrow range of this latency and the short duration (30.3 ms) contribute to the feasibility of automatically synchronized FES-IM and Lokomat gait training. There was no accumulation of delay over the 30-min runs. 2) First Pulse Latency: Fig. 1 also shows a plot of the distribution of the first pulse latencies for the two test trials. Table I shows first pulse latencies for automatic stimulation. Total latency had means of 68.7 and 69.4 ms across strides for the two trials, respectively, and ranges of from 40.3 to 97.1 ms and
from 41.8 and 97.5 ms, respectively. The time for the Lokomat gait cycle was 2.8 s; therefore, the latency of 68 ms for signal transmission was only 2.5% of the entire gait cycle. The short contributes to the feasibility of combined duration of FES-IM and Lokomat gait training. Since stance phase extended to 60% of the gait cycle, almost all of stance phase and all of swing practice could be performed with combined FES-IM and Lokomat. The latency met the criterion of being less than 5% of the gait cycle. Fig. 2 shows a comparison of automatic and manual stimulation, according to trigger recognition. Automated stimulation had a latency that was less variable than manual stimulation (10-fold smaller standard deviation; Table II). Table II shows trigger recognition latency for automatic and manual stimulation. Note that both clinicians sometimes initiated stimulation early. Clinician 1 was early with stimulation 92.0% of the time, which was the majority of steps, and clinician 2 was early 22.8% of the time. This means that they initiated stimulation prior to the Lokomat gait event of right heel strike. In addition, clinician 1 was later than automatic triggering for 4.7% of the steps, and clinician 2 was later than automatic triggering for 64.4% of the steps. B. FES Delivery Reliability In two trials of 634 steps each, the UECU initiated the stimulation pattern for each step without missing any and without any
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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 16, NO. 3, JUNE 2008
Fig. 2. Shows a comparison of automatic vs. manual synchronization. Note that the latency for the automatic test runs occupies a very narrow time window (17.5–43.5 ms) compared to the latencies of the manual test runs ( 330:3 to 287.4 ms).
0
TABLE II MANUAL AND AUTOMATIC TRIGGER RECOGNITION LATENCY
Fig. 3. Shows desired FES-IM stimulation pattern (solid lines) and actual pulses (points) delivered for one gait cycle plotted in relation to the joint angles at the hip and knee joints of the Lokomat. The delivered correspond to the desired pattern to within 2.5% of the gait cycle. The displayed gait cycle is from right heel strike to subsequent right heel strike.
D. Limitations Gait speed of the Lokomat was constrained at 1.5 km/h for this study. Future work could include testing the integration of FES-IM and Lokomat at different gait speeds. To use our system at different gait speeds, we would use the capability of adjusting the overall pattern timing that was built into our application software. Since this capability was already used to adjust pattern timing for our tests, it should pose no problem for synchronizing to other gait speeds. IV. CONCLUSION
extraneous initiations. In two trials of 636 steps each, two clinicians missed 2 and 9 stimulation initiations, respectively. Automatic stimulation was 100% reliable. Manual stimulation was 99.69% and 98.58% reliable for two runs with two different clinicians, respectively. These levels of reliability are sufficient for the clinical use of FES with the Lokomat. The occasional missed initiation of stimulation would not adversely affect training. C. FES Delivery Integrity Fig. 3 shows the muscle stimulation activations for all eight channels [Fig. 3(a)] plotted against the joint angles at the hip and knee of the right leg [Fig. 3(b)]. The solid lines [Fig. 3(a)] represent the programmed stimulation pattern and the dots represent actual stimulation pulses generated by the UECU during one stride. Time 0 is right heel strike, as defined by the Lokomat trigger pulse. This research developed a new sophistication in technology capability for gait training. An 8-channel FES system was automatically synchronized with a gait robot. The clinical significance is that actual muscle activations can be FES-induced in concert with the robotics-induced passive movement of the lower limbs, resulting in active muscle contractions contributing to close-to-normal coordinated movement components of gait. To our knowledge there is no other currently available method to provide this type of gait practice with these practice advantages.
Within the bench testing that was conducted, the integration of automatically triggered FES-IM with the gait robot was feasible, accurate, and reliable. REFERENCES [1] A. Mayr, M. Kofler, E. Quirbach, H. Matzak, K. Frohlich, and L. Saltuari, “Prospective, blinded, randomized crossover study of gait rehabilitation in stroke patients using the lokomat gait orthosis,” Neurorehabil. Neural Repair, vol. 21, no. 4, pp. 307–314, Jul.–Aug. 2007. [2] B. Husemann, F. Muller, C. Krewer, S. Heller, and E. Koenig, “Effects of locomotion training with assistance of a robot-driven gait orthosis in hemiparetic patients after stroke: A randomized controlled pilot study,” Stroke, vol. 38, no. 2, pp. 349–354, Feb. 2007. [3] J. J. Daly, K. Roenigk, J. Holcomb, J. M. Rogers, K. Butler, J. Gansen, J. McCabe, E. Fredrickson, E. B. Marsolais, and R. L. Ruff, “A randomized controlled trial of functional neuromuscular stimulation in chronic stroke subjects,” Stroke, vol. 37, no. 1, pp. 172–178, Jan. 2006. [4] J. J. Daly, K. Sng, K. Roenigk, E. Fredrickson, and M. E. Dohring, “Intra-limb coordination deficit in stroke survivors and response to treatment,” Gait Posture, vol. 25, pp. 412–418, Mar. 2006. [5] J. J. Daly and R. L. Ruff, “Feasibility of combining multi-channel functional neuromuscular stimulation with weight-supported treadmill training,” J. Neurol. Sci., vol. 255, pp. 105–115, Oct. 2004. [6] J. M. Hidler and A. E. Wall, “Alterations in muscle activation patterns during robotic-assisted walking,” Clin. Biomech., vol. 20, no. 2, pp. 184–193, Feb. 2005. [7] D. A. Winter, The Biomechanics and Motor Control of Human Gait: Normal, Elderly, and Pathological. Waterloo, ON, Canada: Waterloo Biomechanics, 1991.
DOHRING AND DALY: AUTOMATIC SYNCHRONIZATION OF FUNCTIONAL ELECTRICAL STIMULATION
Mark E Dohring received the Ph.D. degree from Case Western Reserve University, Cleveland, OH, in 2002. He worked as a post-doc at Case Western Reserve University until taking a research position at the Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Cleveland, OH, in 2004 as a Research Biomedical Engineer. His research interests include the application of novel technologies, including FES and robotics to the problem of motor relearning to improve gait and upper limb function in stroke survivors.
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Janis J. Daly received the B.S. degree in biology from Oberlin College, Oberlin, OH, the M.S. degree in physical therapy from Case Western Reserve University (CWRU), Cleveland, OH, and the Ph.D. degree in psychology from University of Akron, Akron, OH. Currently, she holds the following appointments: Associate Professor, Department of Neurology, CWRU School of Medicine; Associate Director, FES Center of Excellence; Research Career Scientist, and Director, Cognitive and Motor Learning Research Program, LS Cleveland V.A. Medical Center, for which she leads a team of interdisciplinary members who study cognitive control and function, and develop innovative motor learning methods and technology applications that improve gait and upper limb function for stroke survivors.
