Evidence map›Paper›PMID 38502611›Full record

ReviewIEEE transactions on haptics

Motor Learning in Robot-Based Haptic Dyads: A Review.

Erica L Waters, Michelle J Johnson

Abstract readReview
In one paragraph

Review in IEEE transactions on haptics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Can an Optimal Control Model Capture Tracking Kinematics? Insights from Healthy and Post-Stroke Robot Interactions.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Erica L Waters
Michelle J Johnson

Funding

Rehabilitation Using Community-Based Affordable Robotic Exercise Systems (Rehab CARES)R42HD104325 · NICHD · RECUPERO ROBOTICS LLC · PI JOHNSON, ALWYN P., JOHNSON, MICHELLE J. · 2021 to 2023
$1.8M
NICHD NIH HHS R42 HD104325
6 · The paper itself

Abstract

Rehabilitation robots have the potential to alleviate the global burden of neurorehabilitation. Robot-based multiplayer gaming with virtual and haptic interaction may improve motivation, engagement, and implicit learning in robotic therapy. Over the past few years, there has been growing interest in robot mediated haptic dyads, or human-robot-robot-human interaction. The effect of such a paradigm on motor learning in general and specifically for individuals with motor and/or cognitive impairments is an open area of research. We reviewed the literature to investigate the effect of a robot-based haptic dyad on motor learning. Thirty-eight articles met the inclusion criteria for this review. We summarize study characteristics including device, haptic rendering, and experimental task. Our main findings indicate that dyadic training's impact on motor learning is inconsistent in that some studies show significant improvement of motor training while others show no influence. We also find that the relative skill level of the partner and interaction characteristics such as stiffness of connection and availability of visual information influence motor learning outcomes. We discuss implications for neurorehabilitation and conclude that additional research is needed to determine optimal interaction characteristics for motor learning and to extend this research to individuals with cognitive and motor impairments.

Indexed as

LearningNeurological RehabilitationRoboticsTouch PerceptionHumans

Identifiers

PMID38502611
PMCPMC11831855

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.