ECSS Paris 2023: CP-AP06
INTRODUCTION: Since running is a fundamental and core movement in all sports, there is a lot of research on motion analysis to quantify the lower limb movements during running to improve performance, prevent injuries, and develop shoes for runners(1). However, the equipment used for motion analysis is expensive, requires time-consuming data processing, is not portable, and is limited to laboratories. To solve this problem, wearable devices are being used to analyze movements in real-world situations, such as sports(2). Previous studies using wearables have analyzed movement at slow speeds or at a single, fixed high speed, so it was deemed necessary to verify the reliability of IMUs as speed increases. Therefore, the purpose of this study was to check the reliability of IMUs at various speeds in real sports situations(3). METHODS: Twelve healthy adult males (age: 27.3±3.8 years, height: 173.1±4.3 cm, weight: 75.3±9.3 kg) with no history of musculoskeletal injury or surgery within the last 6 months participated in this study. Participants were asked to perform running on a treadmill at three different speeds (2.7 m/s, 3.3 m/s, and 4.0 m/s). A 3 dimensional motion analysis was performed using eight infrared cameras (sampling rate: 100 Hz) and four IMUs (sampling rate: 100 Hz) to determine kinematic differences in the lower extremity joints measured by infrared cameras and IMUs with increasing speed. A two-way ANOVA with repeated measures with the statistical significance level set at α=.05. RESULTS: In RoM of ankle joint, equipment, and an interaction effects between running speed and equipment showed significant differences. Post hoc tests showed that the RoM of the ankle joint was larger for motion capture than IMU at all speeds. The RoM of the knee and hip joints showed a main effect of speed, equipment, and an interaction effect between speed and equipment. Post hoc tests showed that the RoM of the knee and hip joints increased as running speed increased for both Motion Capture and IMU. Finally like ankle joint, Motion Capture had a larger RoM than IMU for both the knee and hip joints. CONCLUSION: The results of this study showed that the RoM of the lower extremity joints differed between motion capture and IMU with increasing speed, and larger in Motion Capture. Therefore, when analyzing IMU equipment in the field, it is necessary to apply it with the knowledge that there is an error from the Motion Capture. However, since this study compared the overall RoM in the running section, it was not possible to compare within the entire running cycle. Therefore, in future studies, it is necessary to determine the reliability of the entire section through SPM analysis. REFERENCES: 1) Perry, J., & Burnfield, J. M. Gait analysis. Normal and pathological function 2nd ed. California: Slack. 2010 2) Park, S., Yoon, S, Sensors, 2021 3) Jeon, T., Lee. J. Journal of the Korean Society for Precision Engineering. 2018
Read CV Ji Hyuk KangECSS Paris 2023: CP-AP06
INTRODUCTION: Researchers have determined that a single, sacrum-placed inertial measurement unit (IMU) sensor can be used to record pelvis motion during sprinting [1]. This approach is convenient but lacks reference to other valuable metrics needed to track pelvis motion during sprinting (e.g. contact detection). Signal processing techniques can be used to find step kinematic metrics [2,3] but have not been evaluated for short, accelerated sprints. The purpose of this study was to establish the magnitude of systematic bias and random error in determining ground contact time (CT) obtained from a sport market ready IMU to those obtained from a high-speed camera recording during a 20 m sprint run. METHODS: Seventeen competitive collegiate athletes completed a 20 m sprint with an IMU and two high-speed video cameras (iPhone 11, Apple Inc., USA; frame rate = 240 Hz) simultaneously recording. The IMU (Blue Trident, Vicon Motion Systems, UK) was placed on the posterior sacrum. To process the IMU data, acceleration vectors were rotated into the global reference frame using the on-board global orientation estimates in quaternion format. This resulted in a vertical acceleration value represented by the third element of the rotated acceleration vector, which was filtered by a fourth order low-pass butterworth filter with a 10 Hz cutoff frequency. CT for steps 1, 2, 4, 7, and 10 were identified as the time between the maximum and minimum vertical acceleration peaks following methods described in [4]. The video recordings were manually analyzed (Kinovea, vers. 0.9.5). To compare CT derived from the IMU to the cameras the following, bias (mean measurement difference between the two devices, IMU – Camera) and random error (1.96 × standard deviation of the differences between the devices) were determined [5]. RESULTS: An average negative bias, indicating a lower measurement for the IMU, was found for all steps (1: -67 ms; 2: -45 ms; 4: -33 ms; 7: -28 ms; 10: -3 ms). Bias measures reported as a percent of the CT identified by video are -35%, -28%, -25%, -22%, and -2.5% for steps 1, 2, 4, 7, and 10, respectively. Random error was largest for step 1 (±95 ms). For steps 2, 4, 7, and 10 random error was ±90 ms, ±76 ms, ±68 ms, and ±78 ms respectively. CONCLUSION: This study evaluated the use of a sacrum mounted IMU to determine CT for individual steps during a 20 m accelerated sprint. An average negative bias was found for all steps, indicating a lower CT time for the IMU method. The bias values lessened as the sprint progressed, reaching -3 ms at step 10. However, random error at step 10 (±78 ms) represented ±35% of the reference video mean CT. Although bias was relatively low by step 10, practitioners should consider if the potential for error is acceptable with respect to their specific application context. 1. Wada et al. (2020) 2. Day et al. (2021) 3. Lee et al. (2010) 4. Miranda-Oliveira et al. (2023) 5. Bland & Altman (1999)
Read CV Erin FeserECSS Paris 2023: CP-AP06
INTRODUCTION: Jump height is often considered a key outcome measure in sport performance and rehabilitation, but its measurement in practice relies on the availability of specialized equipment [1]. Smartphone sensors may be a low-cost, widely-available alternative to characterize motor performance in clinics and remotely [2]. We sought to determine whether time of flight estimated using smartphone accelerometer is valid when compared with force platform, reliable between laboratory and home sessions, and whether it can detect changes in performance due to acute pain and fatigue. METHODS: Twenty healthy participants (20.6 years old, 6 males) participated in one laboratory session and two remote, unsupervised session. In the laboratory participants performed: 1) five maximal countermovement jumps; 2) five maximal countermovement jumps while experiencing during acute knee pain, induced by means of electrical stimulation at an intensity that induced a perceived pain of 5 out of 10 [3]; 3) a 30s continuous jump test. Participants then performed five countermovement jumps at home, 3-5 and 10-12 days after the laboratory session. Two force platforms were used to collect ground reaction forces in the laboratory. Participants held their own smartphone on their chest and collected acceleration data using the Phyphox application [4]. The time of flight during the jumps was calculated as the time when the ground reaction forces were less than 50N, and when the vertical acceleration was higher than -1 m/s2. Validity and reliability between force plate and smartphone estimates were estimated using Intraclass Correlation Coefficient and T-tests, whereas the effect of acute pain and fatigue was estimated using paired T-tests. RESULTS: Validity of time of flight estimates obtained from force plates and smartphone accelerometer was excellent (ICC=0.96) despite 25ms larger estimates for smartphones (p<0.001). Between-day reliability was good (ICC=0.87) between the laboratory and the first home session, and excellent between the two home sessions (ICC=0.93), with no bias in either case (p>0.08). Time of flight decreased during experimental pain (force platform: -3.2%, p=0.001; smartphone: -2.5%, p=0.029) and when comparing the first and last five jumps during the 30s continuous jump test (force platform: -16.8%, p<0.001; smartphone: -13.7%, p<0.001). CONCLUSION: Time of flight estimated using smartphone accelerometer is valid compared to force plates, reliable between days and in different environments, and sensitive to changes in performance due to fatigue and pain. The use of the participants’ own smartphones, the performance of the task at home unsupervised, and the fact that participants held the smartphone instead of needing a harness, demonstrates the ecological validity of the proposed approach. Future work should assess the usefulness of this technology in practice. 1. Dutaillis, MSSE 2024;56:181-92 2. Devecchi, PLoS ONE 2023;18:11 3. Gallina, J Physiol 2021;599:2401-17 4. Staacks, Phys Educ 2018;045009
Read CV Alessio GallinaECSS Paris 2023: CP-AP06