This dissertation focuses on the low force dexterous manipulation capabilities of the fingers and legs and the effects of age, sex, and clinical condition. The StrengthDexterity (SD) paradigm, based one’s ability to compress a slender spring prone to buckling at low forces, allowed us to quantify dexterity in over 300 participants from 15-93 years of age. We find dexterous manipulation capabilities improve significantly during young adulthood, followed by gradual, but significant, declines from the middle age. Interestingly, we find sex differences in both upper and lower extremity dexterity across the lifespan. We also find that clinical conditions (i.e., Parkinson’s disease (PD), and thumb osteoarthritis) affect finger dexterity.

Traditional linear analyses (i.e., mean compression force, root mean square of the time series variability, the time derivatives of the force traces, and frequency analyses) can quantify dexterity and have shown limited successes quantifying differences among populations. However, the nonlinear nature of the SD paradigm dictates that nonlinear dynamical analyses must be also considered, particularly when exploring between group differences. Therefore, we incorporate the delayed embedding theorem to reconstruct the attractors from time series data collected during the SD paradigm. We find that while linear techniques are certainly informative, nonlinear dynamical analyses are much more suitable to discern differences between contributors to dexterous ability (e.g., age, sex, and clinical condition) and among populations (e.g., skilled versus non-skilled athletes and healthy versus pathologic participants).