Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis

The applicability of Doppler radar for gait analysis is investigated by quantitatively comparing the measured biomechanical parameters to those obtained using motion capturing and ground reaction forces. Nineteen individuals walked on a treadmill at two different speeds, where a radar system was positioned in front of or behind the subject. The right knee angle was confined by an adjustable orthosis in five different degrees. Eleven gait parameters are extracted from radar micro-Doppler signatures. Here, new methods for obtaining the velocities of individual lower limb joints are proposed. Further, a new method to extract individual leg flight times from radar data is introduced. Based on radar data, five spatiotemporal parameters related to rhythm and pace could reliably be extracted. Further, for most of the considered conditions, three kinematic parameters could accurately be measured. The radar-based stance and flight time measurements rely on the correct detection of the time instant of maximal knee velocity during the gait cycle. This time instant is reliably detected when the radar has a back view, but is underestimated when the radar is positioned in front of the subject. The results validate the applicability of Doppler radar to accurately measure a variety of medically relevant gait parameters. Radar has the potential to unobtrusively diagnose changes in gait, e.g., to design training in prevention and rehabilitation. As contact-less and privacy-preserving sensor, radar presents a viable technology to supplement existing gait analysis tools for long-term in-home examinations.Comment: 13 pages, 9 figures, 2 tables, accepted for publication in the IEEE Journal of Biomedical and Health Informatics (J-BHI

Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking

Lower limb exoskeletons require the correct support magnitude and timing to achieve user assistance. This study evaluated whether the sign of the angular velocity of lower limb segments can be used to determine the timing of the stance and the swing phase during walking. We assumed that stance phase is characterized by a positive, swing phase by a negative angular velocity. Thus, the transitions can be used to also identify heel-strike and toe-off. Thirteen subjects without gait impairments walked on a treadmill at speeds between 0.5 and 2.1 m/s on level ground and inclinations between -10 and +10°. Kinematic and kinetic data was measured simultaneously from an optical motion capture system, force plates, and five inertial measurement units (IMUs). These recordings were used to compute the angular velocities of four lower limb segments: two biological (thigh, shank) and two virtual that were geometrical projections of the biological segments (virtual leg, virtual extended leg). We analyzed the reliability (two sign changes of the angular velocity per stride) and the accuracy (offset in timing between sign change and ground reaction force based timing) of the virtual and biological segments for detecting the gait phases stance and swing. The motion capture data revealed that virtual limb segments seem superior to the biological limb segments in the reliability of stance and swing detection. However, increased signal noise when using the IMUs required additional rule sets for reliable stance and swing detection. With IMUs, the biological shank segment had the least variability in accuracy. The IMU-based heel-strike events of the shank and both virtual segment were slightly early (3.3-4.8% of the gait cycle) compared to the ground reaction force-based timing. Toe-off event timing showed more variability (9.0% too early to 7.3% too late) between the segments and changed with walking speed. The results show that the detection of the heel-strike, and thus stance phase, based on IMU angular velocity is possible for different segments when additional rule sets are included. Further work is required to improve the timing accuracy for the toe-off detection (swing)

Automated gait segmentation and tracking using inertial measurement units

Abstract. In this thesis, a methodology is presented to automate the labelling, event detection, segmentation, tracking, and parameter extraction of IMU gait data for sensors placed on the feet and shanks. The algorithms presented were tested using IMU data from three different styles of gait, normal gait, antalgic gait, and limited mobility gait. The algorithms developed were found effective for all of the simulated gait styles without mislabelling or detecting erroneous gait segments. The resultant gait trajectories and parameters were analyzed and were found to accurately depict the differences between each of the different styles of gait. The methodology presented can be used for the rapid and accurate processing of gait data for multiple styles of gait. This quantification of gait data can enable the collection of IMU gait data on a larger scale. This provides an accessible, low-cost option for out-of-laboratory gait data collection

Developing Muscle Synergy Functions For Remote Gait Analysis

Digital medicine promises to improve healthcare and enable its delivery to rural and underserved communities. A key component of digital medicine is accurate and robust remote patient monitoring. For example, remote monitoring of biomechanical measures of limb impairment during daily life could allow near real-time tracking of rehabilitation progress and personalization of rehabilitation paradigms in those recovering from orthopedic surgery. Wearable sensors have long been suggested as a means for quantifying muscle and joint loading, which can provide a direct measure of limb impairment. However, current approaches either do not provide these measures or require unwieldy wearable sensor arrays and/or in-person calibration activities that limit their use. In this thesis, I advance the use of muscle synergy functions, which leverage the synergistic relationship within a group of muscles, to reduce the complexity of wearable sensor arrays and overcome the current need for an in-person visit to a human performance laboratory for calibration. Surface electromyography (EMG) and kinematic data were recorded from leg muscles and segments of nine healthy subjects during walking. Subject-general muscle synergy models were validated using the leave-one-subject-out method for 4 different pairs of input muscle model sets using filtered EMG data. The effect of adding kinematic data (angular velocity) from thigh and shank segment locations was investigated. The average correlation between true and estimated excitations was 96% higher when angular velocity data was included in the 4-muscle input model set. The estimated excitations informed muscle activations with 6.7% mean absolute error (MAE) and 43% variance accounted for (VAF) averaged across all muscles when kinematic data was included in the model, and 7.3% MAE and 43% VAF without kinematic data. These results lay the groundwork for developing muscle synergy functions that no longer require in-person calibration, paving the way for completely remote studies of muscle and joint loading