flexlib: Robust Biomechanics With Hysteresis and Rust-Powered Speed
How Schmitt-trigger style hysteresis enables reliable state detection in noisy wearable data, and why we pair Python with Rust via Maturin.
Our FlexTail Software Ecosystem: Advancing Biomechanical Analysis
flexlib is our Python library for processing and analyzing data from the wearable FlexTail sensor, designed for real-world robustness and scientific depth.
What flexlib Does
- Data ingestion from CSV and RSF formats.
- Signal analysis with hysteresis‑based state detection.
- Biomechanical metrics such as sagittal flexion, lateral flexion, and rotation.
The Power of a Hybrid Approach: Python meets Rust via Maturin
We implement performance‑critical components in Rust for its exceptional speed and memory safety, and expose them to Python with Maturin. This gives data scientists a familiar interface while delivering native performance for large‑scale processing.
Why Hysteresis Matters in Noisy Signals
Wearable sensor data can be noisy. Hysteresis solves this by introducing two thresholds and a memory of the previous state, often implemented with a Schmitt trigger. It filters transient noise and prevents rapid toggling when the input hovers around a single limit.
Benefits:
- Noise rejection without heavy smoothing filters.
- Preservation of sharp, meaningful transitions.
- Deterministic behavior under borderline conditions.
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