Textile-integrated electrochemical wearables with edge AI for on-body heat-strain monitoring

All our international places for 2026 entry are now filled and we are no longer accepting applications from overseas students 

Research Theme

Sensor Signal Processing

Autonomous Sensing Platforms

Aim

Build a smart garment sensor that spots heat strain on the body in real time, using very low power (about one-thousandth of a watt) and reaching high accuracy (around 90%) in realistic military settings.

Objectives

1. Build a garment-mounted panel measuring sweat rate, electrolytes (sodium/chloride or bulk conductivity), sweat lactate, and skin temperature.
2. Implement edge artificial intelligence (Tiny Machine Learning) to deliver continuous on-body inference at ≤1 mW average power.
3. Validate in controlled heat/exertion trials, demonstrating ≥90% event-detection F1 and robust operation under sweat and movement.
4. Quantify inter-subject variability (sex, BMI, fitness/acclimation) and perform a 5–10 min per-user calibration (offset/scale or few-shot adaptation); report within- and cross-subject performance.
5. Exposure add-on: Include a chemical-exposure co-monitor (oxidants/irritants) with event qualification (duration–intensity product; recovery slope), targeting F1 ≥0.85 at ≤1 mW incremental power.

Description

We will develop a textile-integrated electrochemical wearable for real-time heat-strain detection at ≤1mW. It measures sweat rate, electrolytes (Na/Cl or conductivity), sweat lactate, and skin temperature, using on-garment edge AI. H₂O₂ serves as an oxidative-stress co-signal and enzyme-assay reporter, not a primary marker. The sensing stack uses printable carbon electrodes with solid-contact ion-selective and enzymatic sensors in breathable laminates; electronics are snap-in and reusable, with peel-and-stick microfluidics as the consumable. An oxidant/irritant co-monitor adds event qualification (duration–intensity product, recovery slope) to distinguish brief surges from prolonged low-level exposure. We will quantify inter-subject variability and conduct a per-user calibration; performance will be reported for within- and cross-subject evaluations. The project aligns with SPADS themes—Sensor Signal Processing and Autonomous Sensing Platforms— and draws on supervisor expertise: Dr E (electrochemical sensing; wearable integration) and Dr Escudero Rodríguez (edge artificial intelligence; signal processing). Output: a prototype targeting ≥90% F1-score in defence-relevant conditions.

Applications

First-round applications have closed and the applications for SPADS are now being considered on a gathered field basis, where applications will be considered at the end of every month until all places are filled.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

Full funding is available for this position.