What this means
Researchers used models and a body-shaped test tool to explore a new way for pacemakers to send data. Their results are based on assumptions and do not show it works in real people.
Plain-English summary written by an AI model from the abstract. The original is below.
- Who it was tested in
- A computer energy model, a review of implant chips, and a human torso phantom
- Kind of research
- A study
The science
- Original title
- Continuous Monitoring in Implantable Cardiac Devices: Electro-Quasistatic Human Body Communication - Challenges and Opportunities.
- Authors
- Biswas A, Jiang A, Datta A et al.
- Journal
- IEEE open journal of engineering in medicine and biology
- Published
- Oct 15, 2026
- Study type
- Study
- PMID
- 42877818 ↗
Abstract
Cardiovascular diseases are the leading cause of global mortality, with millions of patients relying on implantable pacemakers. Finite battery energy fundamentally limits remote monitoring. Using open-source manufacturer longevity data, under stated parameter assumptions, we develop an energy-balance model to show a stringent ≈ 130 J/year communication budget, too low for continuous monitoring with conventional RF links. We investigate electro-quasistatic human body communication (EQS-HBC) as an alternative physical layer for pacemakers, combining a systematic review of implant-grade transceivers with a proof-of-concept channel feasibility study in a human-torso phantom. Our analyses indicate that, under these modeling assumptions EQS-HBC could, in principle, support 1 kSps, 16-bit equivalent continuous streaming at [Formula: see text] ([Formula: see text] more energy-efficient than RF) while remaining within safety standards and preserving [Formula: see text]-year lifetime, outlining design guidance for future implantable cardiac devices.
Biswas A, Jiang A, Datta A et al. Continuous Monitoring in Implantable Cardiac Devices: Electro-Quasistatic Human Body Communication - Challenges and Opportunities. IEEE open journal of engineering in medicine and biology. 2026. doi:10.1109/OJEMB.2026.3722470. PMID: 42877818. https://pubmed.ncbi.nlm.nih.gov/42877818/