Optimization of Control Functions of an Adaptive Treadmill Platform for Musculoskeletal Rehabilitation Systems

Abstract

Introduction . Restoring independent walking requires rehabilitation aids that enable intensive training while preserving the natural variability of movement. Most researchers typically examine no more than three process-related indicators, all within a single measurement loop. These parameters include, for example, the accuracy of speed and position assessment, gait parameters, ground reaction force, and the user's subjective assessment. This approach hinders direct comparison and sound selection of control functions. Parametric optimization of linear, nonlinear, and PID functions using an integral criterion, which takes into account stability, duration, and amplitude of transient processes, tracking microdynamics, and subjective comfort, has been little studied. This research fills the gap. The objective of the study is an experimental comparison and parametric optimization of linear, nonlinear and PID control functions of an adaptive treadmill platform using virtual reality (VR) and computer vision (CV). Materials and Methods . The experimental setup integrated a treadmill platform and two tracking systems: VR and CV. The processes were evaluated using an integrated quality criterion with nine parameters (position stability metrics, acceleration dynamics, tracking microdynamics, and subjective assessment). The robustness of the integrated criterion to expert weight assignment was determined by sensitivity. In each of the 1000 iterations, the weight coefficients were varied within ±20% of the initial values and re-normalized to maintain a sum equal to one. Five healthy male subjects were used to select the function parameters, and 10 male subjects were used for the final function comparison. A total of 495 valid records were obtained from 165 experiments. Results . The conditions for obtaining the minimum values of the integral criterion were determined: – for CV – a nonlinear function with a criterion of 2.683; – for VR – a linear function with a criterion of 2.002. When using a linear function, the transition from CV to VR was accompanied by a 30.3% decrease in the criterion, from 2.874 to 2.002. The preferred control function was determined by the characteristics of the user position tracking system. The application of VR trackers yielded statistically significant differences between the linear and nonlinear functions ( p  < 0.001), as well as between the linear and PID functions ( p  = 0.0020). The difference between the nonlinear and PID functions was below the level of statistical significance ( p  = 0.0574). For CV, no statistically significant differences were found in the combined profiles ( p  = 0.1407–0.5664). Discussion . For the VR‑based loop, a linear function with a 1‑meter working area is recommended as the baseline. For the CV‑based loop, a nonlinear function with a working area of 0.75 m and a nonlinearity coefficient of 0.3 is recommended. However, the superiority of one of these options has not been proven due to the close value of the PID function and a partial change in ranks when varying the weights. The functions demonstrate stability and physiologically safe latency (less than 100 ms). Due to study limitations (small sample size and only healthy volunteers), the research results are considered part of the preliminary engineering validation and tuning of adaptive treadmill platforms. Conclusion . Recommendations are provided for selecting control functions and tracking systems for adaptive treadmill platforms used in musculoskeletal rehabilitation. The proposed parameters are to be validated on a larger sample, including patients with gait disorders. Future work will also address the development of individualized control tuning based on data acquired during the initial minutes of walking on the platform .