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. 2024 Jun 18;24(12):3934.
doi: 10.3390/s24123934.

Transferring Sensor-Based Assessments to Clinical Practice: The Case of Muscle Synergies

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Transferring Sensor-Based Assessments to Clinical Practice: The Case of Muscle Synergies

Alessandro Scano et al. Sensors (Basel). .

Abstract

Sensor-based assessments in medical practice and rehabilitation include the measurement of physiological signals such as EEG, EMG, ECG, heart rate, and NIRS, and the recording of movement kinematics and interaction forces. Such measurements are commonly employed in clinics with the aim of assessing patients' pathologies, but so far some of them have found full exploitation mainly for research purposes. In fact, even though the data they allow to gather may shed light on physiopathology and mechanisms underlying motor recovery in rehabilitation, their practical use in the clinical environment is mainly devoted to research studies, with a very reduced impact on clinical practice. This is especially the case for muscle synergies, a well-known method for the evaluation of motor control in neuroscience based on multichannel EMG recordings. In this paper, considering neuromotor rehabilitation as one of the most important scenarios for exploiting novel methods to assess motor control, the main challenges and future perspectives for the standard clinical adoption of muscle synergy analysis are reported and critically discussed.

Keywords: clinical assessment; functional synergies; instrumental assessments; kinematic synergies; muscle synergies.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Number of papers found on Scopus when using “muscle AND synergy OR synergies” as keyword for the query and searching in abstract, keywords, and title, updated in April 2024.
Figure 2
Figure 2
A summary of the challenges to be faced as summarized in this work. Logistic challenges include training and education, to design ambitious studies, to guarantee readability of the results. Technical challenges include to guarantee standardization and reproducibility, to account for intra-individual and inter-individual variability, to promote data sharing and inter-operability. Research challenges include to improve synergistic models, filling the gaps in the understanding of synergistic control, to integrate synergistically sound approaches with clinical practice, to use synergies “in the therapy loop”, including synergistic approaches in the decisional process.

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