Overview

This service delivers an accessible, open-source software solution designed to extract, process, and analyse movement and human walking patterns from wearable sensors. By applying advanced algorithmic workflows to raw motion data gathered from inertial measurement units (IMUs), the service automatically calculates key stride, timing, and distance metrics. The primary objective is to give researchers, clinicians, and health tech innovators a standardized, reproducible way to translate raw sensor readings into valuable physical movement insights without building complex data pipelines from scratch.

How can the service help you?

Developing custom movement-tracking algorithms from raw sensor noise requires deep domain expertise and significant development time. This service bridges that gap by transforming raw, unstructured accelerometer and gyroscope readings into validated, actionable gait metrics.

• Before: Raw, complex, multi-axis IMU streams that require specialized mathematical filtering and manual spatial-temporal extraction.
• After: Standardized, high-level gait characteristics ready for clinical interpretation, algorithm validation, or product integration.

It helps developers and clinical teams accelerate product validation, compare prototype performance, and lower the technical barriers to integrating movement tracking into digital health tools.

How will the service be delivered?

The service is executed remotely as a software-based data processing workflow. Clients submit anonymized raw IMU data files via secure digital channels. After execution, the processed output is returned alongside structured data reports according to agreed project parameters.

Additional information

Provider description

Operating from the Department of Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg, we are a service provider node within the TEF-Health consortium. The research group specializes in machine learning, biomedical signal processing, and multimodal sensor synchronization. Our team provides testing infrastructure and scientific support for evaluating medical devices, wearables, and contactless sensing systems.

Technical details

The framework is built around gaitmap, an open-source Python library tailored for spatial-temporal gait parameter extraction.

• Input Data Requirements: Raw tri-axial accelerometer and gyroscope data collected from foot, ankle, or lower-body IMUs. Data must be anonymized prior to transfer.
• Core Tasks & Processing: Signal pre-filtering, stance/stride phase segmentation, step event detection, and spatial parameters calculation (stride length, velocity).
• Outputs: Structured data exports (e.g., JSON/CSV) detailing gait cadence, stride time, swing phase duration, step length, and walking speed.
• Reference & Code Base: https://github.com/mad-lab-fau/gaitmap

Service customization

While the open-source pipeline offers standard default parameters for adult gait analysis, the pipeline can be customized for specific target populations (e.g., geriatric care, neurological conditions, high-performance sports). SMEs and startups can choose specific processing modules (e.g., requesting only temporal parameters or custom data visualization) rather than executing the full end-to-end pipeline.