# Temporal interference stimulation of peripheral nerves induces functionally diverse limb movements revealed by automated pose estimation and unsupervised behavioral analysis

**Authors:** Joshua Philippe Olorocisimo, Sudip Nag, Hengjia Zhang, Songyu Yang, Matvii Prytula, Serena Liu, Mustafa Kanchwala, Yinghe Sun, Jose Zariffa, Roman Genov

PMC · DOI: 10.1186/s12984-025-01825-3 · 2025-12-29

## TL;DR

A new method for stimulating peripheral nerves can produce diverse limb movements using AI-based analysis, offering potential for neurorehabilitation.

## Contribution

A high-selectivity extraneural nerve interface and AI-driven analysis pipeline for evaluating movement diversity in neuromodulation.

## Key findings

- TIS produced 1.75 times more distinct movement clusters than standard stimulation.
- TIS had a significantly higher effect on movement selectivity (β = 2.75, p < 0.005).
- AI analysis revealed functionally diverse limb movements from TIS stimulation.

## Abstract

Peripheral nerve stimulation can help restore limb movement after paralysis and enable advanced rehabilitation technologies; however, current extraneural interfaces are typically limited by low fascicle selectivity and laborious functional evaluation. This study has developed an extraneural peripheral nerve interface with high fascicle selectivity, and an AI-facilitated video analysis pipeline for assessing limb movement during neuromodulation. This was achieved by deploying temporal interference stimulation (TIS) in a high-density nerve cuff electrode and by using machine learning algorithms for automated pose estimation and unsupervised behavioral analysis to evaluate movement selectivity and diversity. Using this unbiased semi-automated analysis revealed that TIS elicited more selective motor responses than standard biphasic stimulation, as evidenced by the formation of 1.75 times more distinct movement clusters and behavioral syllables. Furthermore, logit link beta regression modeling showed that TIS had a significantly higher positive effect on movement selectivity (β = 2.75, p < 0.005) compared to biphasic stimulation. Our statistical and machine learning-based analysis provides a computational and objective pipeline for quantifying complex motor outcomes in neuromodulation research. The results suggest that extraneural TIS can be used to generate individually targeted and functionally diverse limb movement patterns and offers a promising approach for neurorehabilitation applications, including restoring movement to individuals living with spinal cord injury.

The online version contains supplementary material available at 10.1186/s12984-025-01825-3.

## Full-text entities

- **Diseases:** AMI (MESH:D000275), spinal cord injuries (MESH:D013119), neurological disorders (MESH:D009461), TIS (MESH:C536956), nerve damage (MESH:D000080902), pain (MESH:D010146), paralysis (MESH:D010243), migraine (MESH:D008881), chronic pain (MESH:D059350), muscle fatigue (MESH:D005221), UMAP (MESH:C567162), epilepsy (MESH:D004827), neuropathic pain (MESH:D009437)
- **Chemicals:** oxygen (MESH:D010100), povidone-iodine (MESH:D011206), gold (MESH:D006046), HDBSCAN (-)
- **Species:** Rattus norvegicus (brown rat, species) [taxon 10116], Homo sapiens (human, species) [taxon 9606], Rodentia (rodent, order) [taxon 9989]

## Figures

11 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12825211/full.md

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Source: https://tomesphere.com/paper/PMC12825211