Quantified analysis of facial movement: A reference for clinical applications

Kinematic Analysis of Human Gait in Healthy Young Adults Using IMU Sensors: Exploring Relevant Machine Learning Features for Clinical Applications

BACKGROUND

Gait is the manner or style of walking, involving motor control and coordination to adapt to the surrounding environment. Knowing the kinesthetic markers of normal gait is essential for the diagnosis of certain pathologies or the generation of intelligent ortho-prostheses for the treatment or prevention of gait disorders. The aim of the present study was to identify the key features of normal human gait using inertial unit (IMU) recordings in a walking test.

METHODS

Gait analysis was conducted on 32 healthy participants (age range 19-29 years) at speeds of 2 km/h and 4 km/h using a treadmill. Dynamic data were obtained using a microcontroller (Arduino Nano 33 BLE Sense Rev2) with IMU sensors (BMI270). The collected data were processed and analyzed using a custom script (MATLAB 2022b), including the labeling of the four relevant gait phases and events (Stance, Toe-Off, Swing, and Heel Strike), computation of statistical features (64 features), and application of machine learning techniques for classification (8 classifiers).

RESULTS

Spider plot analysis revealed significant differences in the four events created by the most relevant statistical features. Among the different classifiers tested, the Support Vector Machine (SVM) model using a Cubic kernel achieved an accuracy rate of 92.4% when differentiating between gait events using the computed statistical features.

CONCLUSIONS

This study identifies the optimal features of acceleration and gyroscope data during normal gait. The findings suggest potential applications for injury prevention and performance optimization in individuals engaged in activities involving normal gait. The creation of spider plots is proposed to obtain a personalised fingerprint of each patient's gait fingerprint that could be used as a diagnostic tool. A deviation from a normal gait pattern can be used to identify human gait disorders. Moving forward, this information has potential for use in clinical applications in the diagnosis of gait-related disorders and developing novel orthoses and prosthetics to prevent falls and ankle sprains.

Correlation of 3D Morphometric Changes, Kinematics, and Muscle Activity During Smile

OBJECTIVE

Knowing the morphological, kinematic, and electrophysiological parameters of the smile in healthy individuals may contribute to evaluating, planning, and monitoring the smile reanimation. This study aimed to determine the correlation between 3D morphometric changes, movement kinematics, and muscle activity in the facial soft tissue of healthy individuals.

METHOD

In this cohort study, 20 volunteers were selected from healthy individuals with no facial disorders. During smiling, three-dimensional face scanning, facial motion capture, and surface electromyography (sEMG) were performed. The average displacement, velocity, and acceleration during facial movements were measured. The mean change in 3D surface morphometry and activation of the zygomaticus major were determined.

RESULTS

The volunteers, comprising 10 males and 10 females, had a mean age of 24 ± 10 years; for female, mean age was 23 ± 5 years and for men 26 ± 13 years. Significant correlations were found between kinematic and morphometric data (r = 0.51, p < 0.001), sEMG and morphometric (r = 0.50, p < 0.001) data, and sEMG and kinematic data (r = 0.49, p < 0.002). The maximum acceleration occurred during approximately 65% of the muscle activation time and 64% of the peak muscle activation value. Additionally, the maximum velocity was reached at around 73% of the muscle activation time and 67% of the peak muscle activation value. Furthermore, the maximum displacement values were observed at approximately 88% of the muscle activation time and 76% of the peak muscle activation value.

CONCLUSION

The findings may provide insights into the smile's functional parameters, contribute to understanding facial muscle-related disorders, and aid in improving the diagnosis and treatment of the smile.

LEVEL OF EVIDENCE

N/A Laryngoscope, 2024.

Measurement of Ocular Vestibular Evoked Myogenic Potentials: Nasion Reference Montage as an Alternative to the Clinical Standard Montage

OBJECTIVE

To compare two novel electrode montages for ocular, vestibular evoked myogenic potential using single-nasion reference electrodes with the clinical standard montage.

STUDY DESIGN

Randomized crossover experiment.

SETTING

Tertiary referral center.

PARTICIPANTS

Sixty healthy participants.

INTERVENTION

Normal hearing and vestibular function were confirmed with an extensive test-battery. All ocular, vestibular evoked myogenic potential settings were measured with air-conducted tone bursts at 100-dB normal hearing level and a frequency of 500 Hz. Three electrode montages were measured in randomized order: the clinical standard montage ("S"), the nasion reference montage ("N"), and the nasion reference montage with a more lateral active electrode ("L"). Upgaze was standardized to 35 degrees.

MAIN OUTCOME MEASURES

Detection rate, latency of N1 and P1, peak-to-peak amplitude of N1 and P1, signal-to-noise ratio (SNR), asymmetry ratio (AR), concordance of expert assessment, and reliability.

RESULTS

All electrode montages showed detection rates greater than 90%. Latencies for "L" were shorter than for "S" and "N." Amplitudes and SNR for "S" and "N" were higher than for "L," whereas the values for "S" and "N" did not differ significantly. For AR, no significant differences between the montages were assessed. Concordance of experts ranged from 78% for "L" and 89.8% for "N." All montages provided excellent day-to-day reliability (intraclass correlation coefficient ≥0.9) for amplitudes and SNR.

CONCLUSIONS

Montage N could be a useful alternative to the clinical standard montage: although results are roughly equivalent, montage N requires one less electrode to do so.