MEDWEAR

Use Cases

Pilot studies demonstrating MEDWEAR in clinical and assistive-technology settings

Overview

MEDWEAR is validated through two ongoing pilot studies in collaboration with clinical and robotics research groups at ETH Zurich. Both pilots share the same end destination - the MEDWEAR data collection platform - but differ in how data flows from the wearable to that platform: one is offline via a smartphone app, the other is online via ROS 2 middleware.

Two pilot studies: SPZ offline via smartphone, server, MEDWEAR format and platform; LLUI online via ROS and MEDWEAR platform Diagram showing two pilots with matching container sizes. SPZ: wearable sends data to a smartphone running the SCAI app, which sends it to a server, then converted to MEDWEAR format and sent to the MEDWEAR data collection platform, offline. LLUI: wearable streams PPG, ECG, EDA, IMU in MEDWEAR format via ROS middleware and MED-API to the MEDWEAR data collection platform, online, used for real-time cognitive load estimation. SPZ - Pilot 1 Offline, via smartphone app Wearable device PPG, EDA, IMU Smartphone - SCAI app Collects and sends data Server Intermediate storage MEDWEAR format MEDWEAR platform Visualization, roles, export LLUI - Pilot 2 Online cognitive load estimation Wearable device PPG, ECG, EDA, IMU MEDWEAR format ROS middleware MED-API MEDWEAR platform Visualization, roles, export
Data flow for the two MEDWEAR pilot studies. Both pilots use MEDWEAR schemas and converge on the same data collection platform.
Pilot 1

SPZ - Offline data collection

Context

Clinical study in collaboration with SPZ (Schweizerisches Paraplegiker-Zentrum). Participants wear sensors during daily activities outside a lab environment. The study focuses on capturing physiological signals - PPG, EDA, and IMU - using consumer-grade wearable devices.

Data flow

  1. A wearable device streams PPG, EDA, and IMU signals.
  2. A smartphone running the SCAI app collects and buffers the raw data locally.
  3. Batches are uploaded to an intermediate server for secure storage.
  4. A post-processing pipeline converts the data to MEDWEAR format (JSON Schema validated).
  5. Standardized data is ingested into the MEDWEAR platform for visualization, access control, and export.

Why offline?

Participants may not always have reliable internet connectivity. The smartphone buffers data locally and syncs when a connection is available, decoupling data collection from transmission.

Signals

PPG EDA IMU
Pilot 2

LLUI - Online cognitive load estimation

Context

Research study at ETH Zurich's LLUI (Lower-Limb User Interaction) lab. Participants wear sensors during human-robot interaction tasks. The goal is real-time cognitive load estimation to adapt assistive robot behavior based on the user's physiological state.

Data flow

  1. A wearable device streams PPG, ECG, EDA, and IMU signals continuously.
  2. Raw data is immediately formatted as MEDWEAR messages at the source.
  3. ROS 2 middleware transports the data in real time using healthcare_msgs message definitions.
  4. The MED-API ROS 2 bridge ingests the stream into the platform with low latency.
  5. The MEDWEAR platform serves the data for live dashboards, role-based access, and downstream ML pipelines.

Why online?

Cognitive load estimation requires sub-second latency. An online pipeline eliminates buffering delays and enables closed-loop feedback between the wearable signals and the robot controller.

Signals

PPG ECG EDA IMU

What the Pilots Validate

Property SPZ (Offline) LLUI (Online)
Connectivity model Asynchronous batch upload Synchronous real-time stream
Transport layer SCAI smartphone app ROS 2 + MED-API bridge
Schema format JSON Schema (post-hoc conversion) ROS 2 healthcare_msgs (native)
Primary signal types PPG, EDA, IMU PPG, ECG, EDA, IMU
Primary use Clinical data collection & export Real-time cognitive load estimation
Downstream consumer Researcher / clinician dashboard Closed-loop robot controller + dashboard