Assistant Professor San Diego State University, United States
Introduction:: Falling is one of the top health issues for older adults in the United States and is a significant barrier to independent living. Falls happen in one out of four older adults (65 and older), where one in every five falls results in a serious injury and are the leading cause of injury death for older adults. Disability related to falls and fear of falling severely limit quality of life and independent living for older adults. Successful walking balance is achieved through the interaction between the stability of the limb dynamics (biomechanical stability) and the sensorimotor control system. Current balance assessments only predict fall risk up to 6 months out and do not provide insight to causes of increased fall risk over longer periods. While sensorimotor degradations can be identified there are currently no tests to measure biomechanical stability and understand how it changes with age. We have developed a virtual reality treadmill system and data analysis techniques for uniquely quantifying the biomechanical stability contributions to walking balance (Figure 1 Left).
Materials and Methods:: SDSU approved the protocol, and all subjects (N=12) gave written informed consent. Subjects received visual information through a treadmill virtual reality system and experienced visual pitch perturbations (VP) and physical perturbations (PP) of the treadmill belt speed. Body state kinematics and muscle activity (EMG) of 16 lower limb muscles were recorded for at least 150 strides per trial. Body kinematics were recorded using an optical motion capture system (Qualisys) and quantified balance outcome (BO) measures for several balance strategies (foot placement, foot steering, ankle adjustment, push off, and hip placement) every stride. Electromyography (EMG) signals of the Medial Gastrocnemius (MGS), Soleus (SOL), Anterior Tibialis (TA), Sartorius (SAR), Rectus Femoris (RF), Gluteus Maximus (GM), Bicep Femoris (BF), and Semitendinosus (ST) were recorded (2000 Hz) using surface electrodes and quantified the average muscle activity (MA) every stride. There were 10 trials, 5 trials at nominal walking speed and 5 trials and two-thirds of the nominal walking speed, where each speed had one control (no perturbation), 2 visual perturbations (VP = 15° and 30°) and 2 physical perturbations (PP = 5% and 10% of leg length). We quantified muscle activity (MAVP and MAPP) and balance outcome (BOVP, and BOPP) sensitivity metrics from the two measured outputs (BO variability and MA variability) and two perturbation inputs (VP amplitude and PP amplitude) using linear regression analysis. A biomechanical stability metric, which represents the fraction of balance control attributed to the pendular leg dynamics, was calculated as a mathematical combination of the four-sensitivity metrics.
Results, Conclusions, and Discussions:: For the foot placement balance strategy, we significantly induced foot placement and muscle activity corrections for two hip muscles (ST and RF) and computed average biomechanical stability metric values of 0.50 + 0.57 and 0.40+ 0.61, respectively, suggesting that limb dynamics are responsible for 50 and 40% of the stabilization of foot placement. For the ankle adjustment strategy, we found average biomechanical stability metric values of 0.40+ 0.48 and 0.00+ 0.97 for two ankle muscles (MGS and SOL). For the hip placement strategy, average biomechanical stability metrics were 0.53+ 0.51 and 0.73+ 0.27 for two hip muscles (ST and BF). However, for almost all biomechanical stability metrics, the confidence intervals were larger than the expected biomechanical stability range (between 0 and 1), suggesting that the current methodological implementation is not sufficient to compare biomechanical stability across population groups. In conclusion, we have developed a theoretical basis for quantifying the degree to which biomechanical stability contributes to the sagittal plane walking balance and developed measurement techniques for quantifying biomechanical stability. Despite having high variability that prevented an accurate partition between active and passive balance control, this work serves as a basis for future study refinements to better quantify biomechanical stability across walking conditions and subject populations. Successful attainment of that goal will support biomechanical stability as a useful walking balance metric, which may serve as a unique and early diagnostic marker for fall risk in older adults. Further, determining which compensatory changes to the average walking pattern are most associated with changes in biomechanical stability may provide potential new rehabilitation pathways for early balance interventions that will promote maintenance of physical independence.
