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Minimizing endpoint variability through reinforcement learning during reaching movements involving shoulder, elbow and wrist.

ABSTRACT: Reaching movements are comprised of the coordinated action across multiple joints. The human skeleton is redundant for this task because different joint configurations can lead to the same endpoint in space. How do people learn to use combinations of joints that maximize success in goal-directed motor tasks? To answer this question, we used a 3-degree-of-freedom manipulandum to measure shoulder, elbow and wrist joint movements during reaching in a plane. We tested whether a shift in the relative contribution of the wrist and elbow joints to a reaching movement could be learned by an implicit reinforcement regime. Unknown to the participants, we decreased the task success for certain joint configurations (wrist flexion or extension, respectively) by adding random variability to the endpoint feedback. In return, the opposite wrist postures were rewarded in the two experimental groups (flexion and extension group). We found that the joint configuration slowly shifted towards movements that provided more control over the endpoint and hence higher task success. While the overall learning was significant, only the group that was guided to extend the wrist joint more during the movement showed substantial learning. Importantly, all changes in movement pattern occurred independent of conscious awareness of the experimental manipulation. These findings suggest that the motor system is generally sensitive to its output variability and can optimize joint-space solutions that minimize task-relevant output variability. We discuss biomechanical biases (e.g. joint's range of movement) that could impose hurdles to the learning process.

SUBMITTER: Mehler DMA

PROVIDER: S-EPMC5515434 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:BACKGROUND: Although arthroscopy of upper extremity joints was initially a diagnostic tool, it is increasingly used for therapeutic interventions. Randomized controlled trials (RCTs) are considered the gold standard for assessing treatment efficacy. We aimed to review the literature for intervention RCTs involving wrist and shoulder arthroscopy. METHODS: We performed a systematic review for RCTs in which at least one arm was an intervention performed through wrist arthroscopy or shoulder arthroscopy. PubMed and Cochrane Library databases were searched up to December 2012. Two researchers reviewed each article and recorded the condition treated, randomization method, number of randomized participants, time of randomization, outcomes measures, blinding, and description of dropouts and withdrawals. We used the modified Jadad scale that considers the randomization method, blinding, and dropouts/withdrawals; score 0 (lowest quality) to 5 (highest quality). The scores for the wrist and shoulder RCTs were compared with the Mann-Whitney test. RESULTS: The first references to both wrist and shoulder arthroscopy appeared in the late 1970s. The search found 4 wrist arthroscopy intervention RCTs (Kienböck's disease, dorsal wrist ganglia, volar wrist ganglia, and distal radius fracture; first 3 compared arthroscopic with open surgery). The median number of participants was 45. The search found 50 shoulder arthroscopy intervention RCTs (rotator cuff tears 22, instability 14, impingement 9, and other conditions 5). Of these, 31 compared different arthroscopic treatments, 12 compared arthroscopic with open treatment, and 7 compared arthroscopic with nonoperative treatment. The median number of participants was 60. The median modified Jadad score for the wrist RCTs was 0.5 (range 0-1) and for the shoulder RCTs 3.0 (range 0-5) (p = 0.012). CONCLUSION: Despite the increasing use of wrist arthroscopy in the treatment of various wrist disorders the efficacy of arthroscopically performed wrist interventions has been studied in only 4 randomized studies compared to 50 randomized studies of significantly higher quality assessing interventions performed through shoulder arthroscopy.

Project description:Background and objectivesHuman motor control (HMC) has been hypothesized to involve state estimation, prediction and feedback control to overcome noise, delays and disturbances. However, the nature of communication between these processes, and, in particular, whether it is continuous or intermittent, is still an open issue. Depending on the nature of communication, the resulting control is referred to as continuous control (CC) or intermittent control (IC). While standard HMC theories are based on CC, IC has been argued to be more viable since it reduces computational and communication burden and agrees better with some experimental results. However, to be a feasible model for HMC, IC has to cope well with inaccurately modeled plants, which are common in daily life, as when lifting lighter than expected loads. While IC may involve event-driven triggering, it is generally assumed that refractory mechanisms in HMC set a lower limit on the interval between triggers. Hence, we focus on periodic IC, which addresses this lower limit and also facilitates analysis.Theoretical methods and resultsTheoretical stability criteria are derived for CC and IC of inaccurately modeled linear time-invariant systems with and without delays. Considering a simple muscle-actuated hand model with inaccurately modeled load, both CC and IC remain stable over most of the investigated range, and may become unstable only when the actual load is much smaller than expected, usually smaller than the minimum set by the actual mass of the forearm and hand. Neither CC nor IC is consistently superior to the other in terms of the range of loads over which the system remains stable.Numerical methods and resultsNumerical simulations of time-delayed reaching movements are presented and analyzed to evaluate the effects of model inaccuracies when the control and observer gains are time-dependent, as is assumed to occur in HMC. Both IC and CC agree qualitatively with previously published experimental results with inaccurately modeled plants. Thus, our study suggests that IC copes well with inaccurately modeled plants and is indeed a viable model for HMC.

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Minimizing endpoint variability through reinforcement learning during reaching movements involving shoulder, elbow and wrist.

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