Validity and Reliability of mTrigger Biofeedback

By |2023-07-18T11:15:31-04:00July 23rd, 2023|Latest Articles|

Data collected from EMG biofeedback (EMG-BFB) is one of the most common variables measured in research. However, until recently, the value of biofeedback information has been limited to a laboratory setting. To cover this void, the mTrigger device was developed to provide a valid and reliable clinic and home use EMG biofeedback unit. Done in conjunction with the University of Delaware, this feasibility study aimed to first repurpose the mTrigger biofeedback device for gait research and second establish the validity and reliability of the mTrigger device in addressing gait disorders.

In stroke populations, EMG-BFB has been shown to improve gait speed, step length, muscle strength, balance, reduce assistive device use, and so much more. With the stroke population predicted to increase by 25% by 2030, the potential for the use of EMG-BFB in stroke populations is enormous.

Repurposing mTrigger for Gait Research

To repurpose the mTrigger device for gait research, four new features were added, some of which you may be familiar with.

First, auditory and haptic (vibration) feedback was added. Visual feedback was already a part of the mTrigger device; however, the visual system is so strongly used to control and stabilize gait that visual feedback is not always optimal when training gait. Therefore, the addition of auditory andhaptic feedback allowed participants the ability to select their mode of feedback. Just like you can do with the clinic device today. 

Second, success rate tracking was added to help promote motor learning. Both knowledge of performance (feedback during the practice – How well did you move?) and knowledge of results (feedback after the performance – Did you achieve the goal?) are important for motor learning in stroke populations. Therefore, a knowledge of results feature was added to the mTrigger app that took the whole trial into account. This is what you see under the “TRACK” screen.

Third, a cloud upload feature was added to allow participants to upload their data to the cloud.

Finally, calibration was added to provide detection and display of mean peak muscle activity. This is often the most frustrating feature for clinicians, so let’s highlight why it’s important. Factors such as electrode placement/configuration, skin impedance (hair, lotion, oils, etc), skin temperature, anatomy, motion artifact, and cross talk (signals from muscle groups not being monitored that contaminate the signal being collected) all contribute to EMG signals being highly variable. Since some of these variables cannot be controlled (especially in a clinical unit), calibration is necessary to achieve accurate results.

Testing mTrigger for Drift and Lag

First, the mTrigger device had to be tested for signal drift and temporal lag to determine if it would be appropriate for capturing stride data during gait.

To test for signal drift, participants sat in a chair with their knees flexed to 90 degrees with no movement. A single channel of electrodes was placed over the medial gastroc and data was collected for 5 minutes. A linear regression was performed to find the line of best fit through the resting mTrigger data. A 2-tailed sample t-test was then used to determine whether the beta value was significantly different from zero. The results were not significantly different from zero (p=0.99), indicating there was no significant signal drift.


Testing for temporal lag was performed to determine if the mTrigger system could detect successive contractions and relaxations of the plantar flexors during the push off phase of gait at different gait speeds. Normal human gait speed is 50-65 steps per minute. A range of 45-90 steps per minute was tested to encompass faster and slower muscle contractions/relaxations. To test this, participants performed 30sec of a rapid heel raise exercise at four different frequencies (45, 60, 75, and 90 bpm using a metronome) on the mTrigger device and the laboratory grade DELSYS system. The number of EMG peaks detected by each system was counted. A Pearson’s correlation was then used to compare the number of peak contractions detected by the mTrigger and the DELSYS systems. The number of peaks detected by both systems matched for all participants. Pearson’s correlation coefficient between signals for detecting a heel raise was r = 1.0, n = 31, and p >0.0001, indicating the temporal lag of the mTrigger device will not result in a missed step.

Validity and Reliability of the mTrigger 

Finally, the validity and reliability of the mTrigger device for detecting plantar flexor muscle activity during the push off phase of gait at typical older adult walking speeds and in individuals with stroke compared to the DELSYS system could be tested.

Thirty-two participants (17 females and 15 males) were recruited for this study. Participants ranged in age from 18 – 70 years. Participants were able to walk without an assistive device and had no recent musculoskeletal injury. Subjects with gait disorders, osteoarthritis, or any neurological disorder affecting gait were excluded.

The skin over the participants’ medial gastroc was cleaned with alcohol and shaved if needed. Two mTrigger electrodes were placed on the right medial gastroc muscle belly. The DELSYS electrodes were placed on the same right medial gastroc, just medial to, but not touching the mTrigger electrodes. 

Three formulated hypotheses were tested.

Hypothesis #1

It was hypothesized that the mTrigger device would be able to measure EMG bursts of the gastroc muscle during gait just as the laboratory grade DELSYS system could during four different gait speeds.

  • 0.3m/s = Severe gait impairment speed
  • 0.6m/s = Limited community ambulation
  • 0.9m/s = Full community ambulation
  • 1.2m/s = Normal walking speed

Participants walked for 5 minutes on the treadmill prior to data collection in order to stabilize their gait. Then data was collected for 2 min at each of the four walking speeds, in random order. A single measure intraclass correlation with mixed effects was performed to determine reliability between the two systems. There was a good level of agreement between the number of peaks detected by each system at 0.3m/s (ICC=0.846). There was an excellent level of agreement at 0.6m/s, 0.9m/s, and 1.2m/s. (ICC=0.939, 0.99, and 0.976 respectively).

Hypothesis #2
It was hypothesized that the mTrigger device would be able to measure the same number of EMG bursts during a follow up trial at the same speed. After the initial trial, electrodes were removed and attached again before a second trial. The data from the two separate testing sessions was compared using Pearson’s correlation coefficients. There was a very high positive correlation between the number of peaks detected in mTrigger session 1 and mTrigger session 2 at walking speeds of 0.6m/s (CC=0.971), 0.9m/s (CC=0.979), and 1.2m/s (CC= 0.964). There was a high positive correlation at a speed of 0.3m/s (CC= 0.890).

Hypothesis #3
It was hypothesized that overground walking at a set speed with a metronome would measure the same number of EMG bursts of activity as treadmill walking at that same speed. Participants walked continuously along a 10m pathway matching their steps to a preset metronome pace. The number of EMG peaks during the walking trial was compared between mTrigger and DELSYS systems. A single measure intraclass correlation with mixed effects was performed demonstrating a good level agreement between the number of peaks detected by the mTrigger and DELSYS systems at walking speeds of 0.3m/s (ICC=0.883 ), 0.6m/s (ICC=0.804 ), and 1.2m/s (ICC=0.792), and a moderate agreement at a speed of 0.9m/s (ICC=0.702)

Furthermore, the reliability of the mTrigger during overground walking was also established by looking at the data between two separate testing session using Pearson’s correlation coefficients. This showed a moderate to high correlation between the number of peaks mTrigger detected in session 1 compared to the number of peaks mTrigger detected in session 2. Indicating a high positive correlation at speeds of 0.3m/s (CC=0.756), 0.6m/s (CC=0.817), and 0.9m/s (CC=0.856). And a moderate positive correlation at normal walking speed of 1.2m/s (CC=0.690).


The mTrigger device was able to correctly measure the same number of EMG peaks of muscle activity during walking as compared to the laboratory grade DELSY system, demonstrating the validity of the mTrigger device. A good to excellent intraclass correlation between the two systems was demonstrated for treadmill walking (0.990>ICC.0.846) and a moderate to good ICC was shown for overground walking (0.883 > ICC < 0.702). The mTrigger system was also able to capture maximum plantar flexion muscle contractions at rates similar to and higher than those typically seen in older adults or individuals with stroke. This indicates the mTrigger device is reliable for providing real time biofeedback during gait applications. Furthermore, it points to the ability of mTrigger biofeedback to accurately capture muscle activation during additional exercise training at a variety of speeds and intensities. This is important for the number of exercise, clinical, and home based applications the mTrigger device is utilized for. 


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  1. Koiler R, Bakhshipour E, Glutting J, Lalime A, Kofa D, Getchell N. Repurposing an EMG Biofeedback Device for Gait Rehabilitation: Development, Validity and Reliability. International Journal of Environmental Research and Public Health 2021, Vol 18, Page 6460. 2021;18(12):6460. doi:10.3390/IJERPH18126460

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