Effects of segment masses and cut-off frequencies on the estimation of vertical ground reaction forces in running

Estimation of Vertical Ground Reaction Force during Single-leg Landing Using Two-dimensional Video Images and Pose Estimation Artificial Intelligence

OBJECTIVE

Assessment of the vertical ground reaction force (VGRF) during landing tasks is crucial for physical therapy in sports. The purpose of this study was to determine whether the VGRF during a single-leg landing can be estimated from a two-dimensional (2D) video image and pose estimation artificial intelligence (AI).

METHODS

Eighteen healthy male participants (age: 23.0 ± 1.6 years) performed a single-leg landing task from a 30-cm height. The VGRF was measured using a force plate and estimated using center of mass (COM) position data from a 2D video image with pose estimation AI (2D-AI) and three-dimensional optical motion capture (3D-Mocap). The measured and estimated peak VGRFs were compared using a paired t-test and Pearson's correlation coefficient. The absolute errors of the peak VGRF were also compared between the two estimations.

RESULTS

No significant difference in the peak VGRF was found between the force plate measured VGRF and the 2D-AI or 3D-Mocap estimated VGRF (force plate: 3.37 ± 0.42 body weight [BW], 2D-AI: 3.32 ± 0.42 BW, 3D-Mocap: 3.50 ± 0.42 BW). There was no significant difference in the absolute error of the peak VGRF between the 2D-AI and 3D-Mocap estimations (2D-AI: 0.20 ± 0.16 BW, 3D-Mocap: 0.13 ± 0.09 BW, P = 0.163). The measured peak VGRF was significantly correlated with the estimated peak by 2D-AI (R = 0.835, P <0.001).

CONCLUSION

The results of this study indicate that peak VGRF estimation using 2D video images and pose estimation AI is useful for the clinical assessment of single-leg landing.

Unsupervised IMU-based evaluation of at-home exercise programmes: a feasibility study

BACKGROUND

The benefits to be obtained from home-based physical therapy programmes are dependent on the proper execution of physiotherapy exercises during unsupervised treatment. Wearable sensors and appropriate movement-related metrics may be used to determine at-home exercise performance and compliance to a physical therapy program.

METHODS

A total of thirty healthy volunteers (mean age of 31 years) had their movements captured using wearable inertial measurement units (IMUs), after video recordings of five different exercises with varying levels of complexity were demonstrated to them. Participants were then given wearable sensors to enable a second unsupervised data capture at home. Movement performance between the participants' recordings was assessed with metrics of movement smoothness, intensity, consistency and control.

RESULTS

In general, subjects executed all exercises similarly when recording at home and as compared with their performance in the lab. However, participants executed all movements faster compared to the physiotherapist's demonstrations, indicating the need of a wearable system with user feedback that will set the pace of movement.

CONCLUSION

In light of the Covid-19 pandemic and the imperative transition towards remote consultation and tele-rehabilitation, this work aims to promote new tools and methods for the assessment of adherence to home-based physical therapy programmes. The studied IMU-derived features have shown adequate sensitivity to evaluate home-based programmes in an unsupervised manner. Cost-effective wearables, such as the one presented in this study, can support therapeutic exercises that ought to be performed with appropriate speed, intensity, smoothness and range of motion.

Vertical Ground Reaction Force Estimation From Benchmark Nonstationary Kinematic Data

Time-differentiating kinematic signals from optical motion capture amplifies the inherent noise content of those signals. Commonly, biomechanists address this problem by applying a Butterworth filter with the same cutoff frequency to all noisy displacement signals prior to differentiation. Nonstationary signals, those with time-varying frequency content, are widespread in biomechanics (eg, those containing an impact) and may necessitate a different filtering approach. A recently introduced signal filtering approach wherein signals are divided into sections based on their energy content and then Butterworth filtered with section-specific cutoff frequencies improved second derivative estimates in a nonstationary kinematic signal. Utilizing this signal-section filtering approach for estimating running vertical ground reaction forces saw more of the signal's high-frequency content surrounding heel strike maintained without allowing inappropriate amounts of noise contamination in the remainder of the signal. Thus, this signal-section filtering approach resulted in superior estimates of vertical ground reaction forces compared with approaches that either used the same filter cutoff frequency across the entirety of each signal or across the entirety of all signals. Filtering kinematic signals using this signal-section filtering approach is useful in processing data from tasks containing an impact when accurate signal second derivative estimation is of interest.