Michelle Cullen, Matthew Keller, Eli Brantingham, John Bartholomew, Sarah Levine, Michael Guthrie
Abstract
Although running is associated with positive health benefits, many runners experience running-induced injuries. Research demonstrates that a rear-foot strike pattern is a common factor amongst these injuries and may increase one’s likelihood of sustaining another in the future. Current technology to assess foot strike pattern and running form in a clinical setting lacks the instantaneous feedback needed to alert a runner to his or her poor mechanics. Partner physical therapists desire a device that can be easily implemented into a gait retraining program to provide instantaneous feedback to the runner about his or her foot striking pattern in order to treat and prevent running-induced injuries. The proposed design is intended for use in a clinical setting and uses force sensors placed directly on the bottom of the runner’s foot, a compact wiring cord, and a small electronics box to transmit data to an iPad application.
Introduction/Background
Running is an enjoyable physical activity in which several million Americans choose to participate in. However, running induced injuries are highly prevalent, and two-thirds of all reported running injuries occur at the lower leg and knee (1). A rear-foot striking pattern is thought to be the cause of an increased impact force at the hip and knee, and may be reduced by retraining the runner to adopt either a forefoot or mid-foot striking pattern (2). Current technology used in gait retraining is limited to videotaping runners and a post-run analysis by the physical therapist. Unfortunately, there is currently a lack of instantaneous feedback provided to the runner during their run because of limitations of present technology. However, research has demonstrated that concurrent feedback is effective when learning motor activities similar to gait retraining (1). Therefore, it is desirable to have a compact device with the ability to detect poor running form and provide feedback to correct it.
Figure 1: Current gait retraining technique. The runner is videotaped using an iPad application and analysis is done following the running session. The video is analyzed over several frames to determine the type of foot striking pattern exhibited. The physical therapist will then use a metronome in an attempt to change the runner’s cadence to correct a rear foot striking pattern.
Problem Statement
The purpose of this project is to develop a device to detect a rear-foot striking running gait and provide the runner with instantaneous external feedback to correct mechanics that predispose an individual to increased risk for injury. The device is only intended for use in a clinical setting and will be used by a physical therapist.
Design and Development
The final device consists of four main features.
Force Sensor
Two force sensors are used in this design. Both sensors are applied to the bottom on the foot using Leukotape (available at most physical therapy clinics). The sensors are applied by the physical therapist when the patient is seated with his/her leg resting on a bench; one sensor is taped to the inferior portion of the calcaneus (‘heel sensor’) and the other to the forefoot (‘toe sensor’). The sensors are 1’’ in diameter and roughly the thickness of a piece of paper and do not inhibit the runner’s form or interfere with existing orthotics. The sensors can be easily disinfected and reused with alcohol wipes.
Figure 2: Force sensor applied to the inferior portion of the calcaneus by a physical therapist (left). The sensor location is highlighted by the red circle.
Electronics Box
The electronics box houses a battery and RFduino microcontroller. The electronics box is a custom design and was created using a 3D printer located at The Ohio State University. The device is powered by a 3V battery and the microcontroller transfers real-time data to an iPad using Bluetooth technology. The electronics box is lightweight and does not alter inherent running form.
Figure 3: CAD model of the battery (top left) RFduino microcontroller (top right) and side view of the electronics box (bottom). The electonrics box is a custom design and inexpensive to produce. Electronics box modeled on running shoe (right).
Compact Wiring
The device uses a single cord, which contains all electronic components and is covered by a protective sleeve to avoid any risk of electrical shock. The cord connects to the electronics box (clipped to the shoe laces) and the sensors inside the runner’s shoe. The cord is slim and does not inhibit the runner’s form. The cord runs into the shoe along the lateral side of the malleolus to avoid any potential tension on the cord potentially caused by pronated running gait.
iPad Compatible
The device connects with an iPad application, TechBasic. The microcontroller wirelessly transmits data in real-time. The number of heel strikes detected is then displayed on the TechBasic interface as feedback to the runner.
Table 1: Final device components (described above).
Evaluation
The final device was evaluated in order to assess its clinical relevance, reliable data transfer, consistency across patients, and ability to distinguish a rear-foot striking pattern from a more anterior foot striking pattern. A 5-question survey was administered to a team of expert physical therapists from The Ohio State University’s Endurance Medicine Program to assess whether the device seemed viable for its proposed use of gait retraining. The device met the needs of the team and was deemed to offer several advantages over current practice. Additionally, reliable data transfer was confirmed by transferring data wirelessly and through a computer connection simultaneously. All team members posed as patients in order to test the efficacy of the device. Testing demonstrated the device’s ability to reliably distinguish a rear-foot striking pattern from a more anterior striking pattern.
Figure 4: Preliminary sample data from the sensor. In the first half of the trial (left) the patient is running with an intentional rear foot striking pattern, and the heel sensor displays a higher magnitude of impact than the toe sensor. In the second half of the trial (right), the patient is running with an intentional fore foot striking pattern, and the toe sensor displays a higher magnitude of impact.
Figure 5: Preliminary data of device comparison with current practice using iPad video application: Golf Swing App. The paneled framed (top) were time-matched with the device output (bottom).
Discussion and Conclusions
In conclusion, the device has met several of the outlined project objectives. Specifically, it meets all of the following:
- Wireless integration with popular software
- Adjustable/flexible to fit a range of shoe sizes for both men and women
- Procedure duration of less than 30 minutes
- Combined weight of less than 0.5 lbs.
- Less than ¼ inch thickness under the runner’s foot
- Accommodates existing insole or orthotic
- Data can be saved and retrieved later for further analysis
- Device provides instantaneous feedback (visual)
We have developed and tested a device that meets projects objectives and shows promise for integration into clinical gait retraining programs.
While this project was originally developed for use in a running gait retraining setting, the technology developed can easily be applied to other health conditions for gait retraining. It can easily be used with individuals with cerebral palsy, hemiplegia, spinal cord injury, and other gait altering conditions in order to improve ambulation. The device could be useful to encourage heel strikes in these populations in order to normalize walking gait mechanics and increase efficiency, using the heel strike detection as positive rather than negative feedback.
Acknowledgements
The student group would like to thank our advisors, Drs. Sandra Metzler and Carmen DiGiovine for their outstanding and continuous support throughout the project. This project went through several design iterations, which would not have been possible without your support. We’d also like to thank physical therapists Kelly Mueller and Rick Neitzelt for your clinical input and constructive criticism of the design and data analysis over the course of this project. Lastly, we would like to thank Dr. David Lee for your assistance with developing the capability to wirelessly transfer data.
References
1. Miller, A., Willy, R.W., Retraining fixes faulty gait in injured runners. Journal of Foot and Ankle Research, 2013: p. 1-11.
2. Goss, D.L. and M.T. Gross, A review of mechanics and injury trends among various running styles. US Army Med Dep J, 2012: p. 62-71.