Our work addresses the contradiction between the age-related increase in the contribution of cognition for the execution of motor tasks and the age-related decrease in the cognitive component of motor adaptation. We predicted that elderly adults would need more cognitive resources for reaches and would, therefore, not have enough cognitive resources available for adaptation. Rather, we observed that visuospatial abilities could better explain the amount of cognition used by our participants for motor adaptation.

Is proprioceptive acuity linked to amount of implicit motor adaptation across ages? The latter is larger in old compared with younger people? In light of reliability-based sensory integration, we hypothesized that this larger implicit adaptation was linked to an age-related lower reliability of proprioception. Over 2 experiments and 130 participants, we failed to find any evidence for this. We discussed alternative explanations for the increase in implicit adaptation with age and the validity of our proprioceptive assessment.

Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.