Closed-Loop Targeted Memory Reactivation during Sleep Improves Spatial Navigation

Sounds associated with newly learned information that are replayed during non-rapid eye movement (NREM) sleep can improve recall in simple tasks. The mechanism for this improvement is presumed to be reactivation of the newly learned memory during sleep when consolidation takes place. We have developed an EEG-based closed-loop system to precisely deliver sensory stimulation at the time of down-state to up-state transitions during NREM sleep. Here, we demonstrate that applying this technology to participants performing a realistic navigation task in virtual reality results in a significant improvement in navigation efficiency after sleep that is accompanied by increases in the spectral power especially in the fast (12\u201315 Hz) sleep spindle band. Our results show promise for the application of sleep-based interventions to drive improvement in real-world tasks

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Person-specific changes in motor performance accompany upper extremity functional gains after stroke

In animal models, hundreds of repetitions of upper extremity (UE) task practice promote neural adaptation and functional gain. Recently, we demonstrated improved UE function following a similar intervention for people after stroke. In this secondary analysis, computerized measures of UE task performance were used to identify movement parameters that changed as function improved. Ten people with chronic post-stroke hemiparesis participated in high-repetition UE task-specific training 3 times per week for 6 weeks. Before and after training, we assessed UE function with the Action Research Arm Test (ARAT), and evaluated motor performance using computerized motion capture during a reach-grasp-transport-release task. Movement parameters included the duration of each movement phase, trunk excursion, peak aperture, aperture path ratio, and peak grip force. Group results showed an improvement in ARAT scores (p = 0.003). Although each individual changed significantly on at least one movement parameter, across the group there were no changes in any movement parameter that reached or approached significance. Changes on the ARAT were not closely related to changes in movement parameters. Since aspects of motor performance that contribute to functional change vary across individuals, an individualized approach to upper extremity motion analysis appears warranted

Learning Task Priorities from Demonstrations

Bimanual operations in humanoids offer the possibility to carry out more than one manipulation task at the same time, which in turn introduces the problem of task prioritization. We address this problem from a learning from demonstration perspective, by extending the Task-Parameterized Gaussian Mixture Model (TP-GMM) to Jacobian and null space structures. The proposed approach is tested on bimanual skills but can be applied in any scenario where the prioritization between potentially conflicting tasks needs to be learned. We evaluate the proposed framework in: two different tasks with humanoids requiring the learning of priorities and a loco-manipulation scenario, showing that the approach can be exploited to learn the prioritization of multiple tasks in parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic