VALIDATION

Sean K.T. Gaiesky, Lauren Fridman, Tom V. Michie, Paul Blazey, Nicholas Tran, Amy Schneeberg, Christopher Napier

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Abstract: Inertial measurement units (IMUs) represent an exciting opportunity for researchers to broaden our understanding of running-related injuries, and for clinicians to expand their application of running gait analysis. The primary aim of our study was to investigate the 1-week (short-term) and 3-month (long-term) reliability of peak resultant, vertical, and anteroposterior accelerations derived from insole-embedded IMUs. The secondary aim was to assess the reliability of peak acceleration variability and left–right limb symmetry in all directions over the short and long term. A sample of healthy adult rearfoot runners (n = 23; age 41.7 ± 11.2 years) ran at a variety of speeds (2.5 m/s, 3.0 m/s, and 3.5 m/s) on a treadmill in standardised footwear with insole-embedded IMUs in each shoe. Peak accelerations exhibited good to excellent short-term reliability and moderate to excellent long-term reliability in all directions. Peak acceleration variability showed poor to good short- and long-term reliability, whereas the symmetry of peak accelerations demonstrated moderate to excellent and moderate to good short- and long-term reliability, respectively. Our results demonstrate how insole-embedded IMUs represent a viable option for clinicians to measure peak accelerations within the clinic.

Full Paper

Drew Lawson, Nate Morris, Matt Jordan

CSB 2020 (abstract)

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Abstract: The reactive strength index (RSI) can be used to test muscle power and plyometric ability for performance and to monitor athletes after injury (1). Wearable technologies, such as insoles instrumented with inertial measurement unit sensors (IMU), may provide a versatile alternative or assessing RSI compared to laboratory instruments like a force plate (FP). The purpose was to determine the agreement between RSI calculated with IMU insoles and a FP in an athletic population.

Participants familiarized with plyometric training (n=15) were recruited to perform 4 series of 5 consecutive countermovement jumps (total jump count: n=60) on a FP sampling at 1500 Hz while wearing IMU insoles sampling at 416 Hz (Plantiga Technologies Platform). RSI (jump flight time to contraction time ratio) was obtained using a deep learning neural network (DL) from the IMU insole. The RSI outputs from the DL model were compared with an analytical algorithm obtained from the vertical ground reaction force. Agreement was calculated as the percent error between FP RSI and the DL RSI.

The DL RSI model showed high agreement with FP RSI (mean difference: 2.1%; 95% limits of agreement: -7.7%, 11.8%) and supports the use of these IMU insoles for providing an accurate estimate of vertical jump RSI in an athlete population. This may be useful for remote athlete monitoring. As machine learning models, specifically deep learning neural networks as used in the present study, often improve with training on larger data sets, future studies should aim to expand the data set including with non-athlete populations to further increase the system accuracy for measuring the vertical jump RSI.

References:

1. Tenelsen et al., Sports 2019

Nate Morris, Drew Lawson, Matt Jordan

CSB 2020 (abstract)

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Abstract: Wearable technology allows for real-world quantification of kinetic and kinematic variables that previously was only possible in laboratory settings (1). This provides the applied sport scientist with tools to assess movement quality and loading in regular training and competition environments. Additionally, these technologies are a key component assessing improvement of performance variables in the return to sport process following injury (2).

The purpose of this investigation was to determine the accuracy of predicted running speed by smart insoles.

Eleven recreationally active subjects volunteered to perform three treadmill running trials at speeds of 14, 16 and 18 kph while wearing insoles containing IMUs (Plantiga Technologies Platform) sampling at 500 Hz. Subjects were instructed to remain centered on the treadmill while running for 15 seconds at each speed. One minute of rest was given between trials. Insole acceleration data was analyzed using a custom algorithm to determine instantaneous running speed. A 5 second mean running speed from the middle of each trial was used for analysis.

Agreement between insole and treadmill speeds were quantified according to the methods of Bland and Altman (3).

The insoles over estimated treadmill speed in each of the 33 trials conducted (mean, std, 95% random error: 0.15, 0.06, 0.13 m/s). Agreement (bias 95% random error) and coefficient of variation between treadmill and insole speed is presented in Table 1.

The insoles over predicted running speed by 0.15 m/s. Machine learning based algorithm updates may improve accuracy in the future. These data suggest that smart insoles have the potential to accurately and reliably assess running speed. This provides applied sport scientists with non-invasive technology that can accurately quantify movement characteristics in sporting environments.

References:

1. Poitras et al., Sensors 2019

2. Ancillao et al., Sensors 2018

3. Bland & Altman, Stat Methods Med Res 1999

Table 1. Running Speed Agreement

Treadmill Speed | Agreement | CV

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3.89 | 0.11 + 0.08 | 2.1%

4.44 | 0.20 + 0.11 | 3.3%

5.00 | 0.15 + 0.12 | 2.3%

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Note: Treadmill speed, mean bias and 95% random error presented in m/s

Minju Kim, Meihui Li, Sean K.T. Gaiesky, Christopher Napier

ACSM Annual Meeting 2023 (Poster)

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Abstract: Insole-embedded inertial measurement units (IMUs) are portable and affordable compared to cumbersome and expensive laboratory equipment used to conduct gait analysis. However, there is a paucity of research on the short-term reliability of insole-embedded IMUs for walking gait.

Purpose: We aimed to investigate the 1-week reliability of spatiotemporal outcomes derived from insole-embedded IMUs in walking. Methods: Eight healthy, recreationally active adults (5 males, 3 females) participated in two data collections one week apart. Each participant was equipped with two insole-embedded IMUs and standardized footwear. Participants walked for 1-minute on a treadmill at three different speeds (1.0, 1.4, and 1.8 m/s) in randomized order. Spatiotemporal outcomes included ground contact time (GCT), swing time, single limb support, double limb support, and cadence. To assess the reliability of the spatiotemporal variables, we calculated intra-class correlation coefficient (ICC), standard error of measurement (SEM), and minimal detectable change (MDC).

Results: Spatiotemporal variables exhibited excellent reliability with low SEM and MDC values across all speeds between the two sessions (Table 1). The smallest SEM and greatest ICC values were at the fastest walking speed.

Conclusion: The results of this study suggest that spatiotemporal variables from insole-embedded IMUs are reliable at different speeds of walking. Therefore, insole-embedded IMUs may be a valid alternative for clinicians to analyze walking gait patterns from slow to high speed of walking as well as to monitor key metrics for a progress assessment tool between therapy sessions.

Full summary

Sean K.T. Gaiesky, Minju Kim, Meihui Li, Christopher Napier

ACSM Annual Meeting 2023 (Poster)

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Abstract: Traditional running gait analysis suffers from accessibility issues due to the requirement of expensive laboratory-based equipment. Cost-effective, user-friendly insole-embedded inertial measurement units (IMUs) provide clinicians with the opportunity to democratize gait analysis. However, prior to the clinical application of insole-embedded IMUs, their reliability must be investigated.

Purpose: To investigate the 1-week reliability of insole-embedded IMU-derived running gait spatiotemporal variables.

Methods: A total of 8 (5 males, 3 females; 6 rearfoot strike, 2 non-rearfoot strike) recreationally active adults (age: 27.1 ± 4.7 years; BMI: 21.4 ± 2.2 kg/m2) attended 2 sessions separated by 7 days. All participants completed a 3-minute warm-up at a self-selected speed before completing three 30-second trials in random order at 2.5, 3.0, and 3.5 m/s on a treadmill in standardized footwear. Spatiotemporal running gait variables (left/right ground contact time (GCT) (ms), left/right swing time (ms), flight time (ms), duty factor (DF), and step rate (steps/min)) were calculated using insole-embedded triaxial IMUs placed within each shoe. Inter-session reliability was measured using intraclass correlation coefficient (ICC) model (2,1), minimal detectable change (MDC), and the standard error of measurement (SEM). All statistical analyses were performed using SPSS (Version 27).

Results: Good to excellent 1-week reliability was exhibited across all speeds for step rate (ICC range: 0.84 – 0.96), flight time (0.96 – 0.97), left swing time (0.95 – 0.98), right swing time (0.93 – 0.98), left GCT (0.91 – 0.97), right GCT (0.88 – 0.94), and DF (0.93 – 0.97). Correspondingly, relatively low SEM (range: 1.1 – 4.4%) and MDC (range: 3.0 – 12.2%) values were observed for all variables across all speeds except for flight time at the 2.5 m/s speed (SEM: 6.8 %; MDC: 18.8%). As a general trend, reliability measures improved as speed increased.

Conclusion: The results of this study suggest that insole-embedded IMUs can provide clinicians with reliable running-related spatiotemporal variables over a 1-week period. Aside from flight time at the lowest speed, the good-to-excellent ICC scores, and relatively low SEM and MDC values mean clinicians can confidently track these variables week-to-week. Supported by Mitacs.

Full summary

Meihui Li, Sean K.T. Gaiesky, Minju Kim, Christopher Napier

ACSM Annual Meeting 2023 (Poster)

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Abstract

Purpose: Inertial measurement units (IMUs) have become valuable in biomechanical research and in the clinic because of their ability to measure movements in the field. Jump tests are often used to assess lower extremity power capacity, muscle strength, speed, and performance. However, the reliability of insole-embedded IMUs for jump tests has not been examined. We aimed to test the reliability of IMUs for the most common jump tests performed in clinical and on-field assessments.

Methods: Eight healthy participants were fitted with two insole-embedded IMUs and standardized footwear in a lab setting. Jump tests included: single-leg jumps for distance (SLJ-D), height (SLJ-H), and reactive strength index (SLJ-RSI); consecutive countermovement jump (CCMJ); and cyclic jump (Cyc-J). Outcomes included: RSI, average jump height (AJH), maximum jump height (MJH), and maximum jump distance (MJD). Participants repeated the jumps over two test sessions separated by 7 days. Intraclass correlation coefficient (ICC), standard error of measurement (SEM), and minimum detectable change (MDC) were used to analyze the reliability of each outcome for the five jump tests. ICC was categorised as: poor (<0.5), moderate (0.5 ≤ 0.7), high (0.7 ≤ 0.9) and excellent (>0.9).

Results: Seven participants (3 females, 4 males) were included in the analysis. One participant was excluded based on improper completion of the jump protocol. We found that all outcomes from the five jump movements presented high (ICC > 0.7) or excellent (ICC > 0. 9) reliability (Table 1).

Conclusion: The insole-embedded IMUs presented high and excellent reliability in the most common jump tests. Clinicians can use the MDC values presented to assess for true change between testing sessions. These findings support the use of insole-embedded IMUs as a tool to monitor jump performance and evaluate the risk of lower extremity injuries in the field.

Full summary