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Review
. 2021 Dec 27;18(1):183.
doi: 10.1186/s12984-021-00974-5.

Human-machine-human interaction in motor control and rehabilitation: a review

Emek Barış Küçüktabak  1   2 , Sangjoon J Kim  2   3 , Yue Wen  2   3 , Kevin Lynch  1 , Jose L Pons  4   5   6   7
Affiliations
Review

Human-machine-human interaction in motor control and rehabilitation: a review

Emek Barış Küçüktabak et al. J Neuroeng Rehabil. .

Abstract

Background: Human-human (HH) interaction mediated by machines (e.g., robots or passive sensorized devices), which we call human-machine-human (HMH) interaction, has been studied with increasing interest in the last decade. The use of machines allows the implementation of different forms of audiovisual and/or physical interaction in dyadic tasks. HMH interaction between two partners can improve the dyad's ability to accomplish a joint motor task (task performance) beyond either partner's ability to perform the task solo. It can also be used to more efficiently train an individual to improve their solo task performance (individual motor learning). We review recent research on the impact of HMH interaction on task performance and individual motor learning in the context of motor control and rehabilitation, and we propose future research directions in this area.

Methods: A systematic search was performed on the Scopus, IEEE Xplore, and PubMed databases. The search query was designed to find studies that involve HMH interaction in motor control and rehabilitation settings. Studies that do not investigate the effect of changing the interaction conditions were filtered out. Thirty-one studies met our inclusion criteria and were used in the qualitative synthesis.

Results: Studies are analyzed based on their results related to the effects of interaction type (e.g., audiovisual communication and/or physical interaction), interaction mode (collaborative, cooperative, co-active, and competitive), and partner characteristics. Visuo-physical interaction generally results in better dyadic task performance than visual interaction alone. In cases where the physical interaction between humans is described by a spring, there are conflicting results as to the effect of the stiffness of the spring. In terms of partner characteristics, having a more skilled partner improves dyadic task performance more than having a less skilled partner. However, conflicting results were observed in terms of individual motor learning.

Conclusions: Although it is difficult to draw clear conclusions as to which interaction type, mode, or partner characteristic may lead to optimal task performance or individual motor learning, these results show the possibility for improved outcomes through HMH interaction. Future work that focuses on selecting the optimal personalized interaction conditions and exploring their impact on rehabilitation settings may facilitate the transition of HMH training protocols to clinical implementations.

Keywords: Human-machine-human interaction; Human-robot interaction; Motor control; Motor learning; Multi-human interaction; Rehabilitation.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Examples of different interaction modes
Fig. 2
Fig. 2
Flowchart of study selection (PRISMA diagram [35])
Fig. 3
Fig. 3
The focus of the research question in selected studies
Fig. 4
Fig. 4
Devices used in the selected studies
Fig. 5
Fig. 5
Experimental tasks used in the selected studies
See this image and copyright information in PMC

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