sensor-modeling is a research toolkit for behavioural sensor streams in ambient assisted living, digital health and smart-home studies. It runs an end-to-end pipeline from heterogeneous sensor observations to explained alerts, on top of a modelling core of Bernoulli autoregressive models, hidden Markov models, change-point detection and non-homogeneous Poisson processes. The methods are chosen for the awkward properties of ambient data: irregular sampling, frequent missingness, binary activations, and the need for models a clinician can read.
It is a research toolkit, not a medical device. Nothing it produces is a diagnosis, and the quantitative results in its documentation come from the bundled simulator rather than validated real-world data.
Install
1
pip install sensor-modeling
Project Links
- PyPI: sensor-modeling
- Documentation: sensor-modeling.readthedocs.io
- Source: github.com/DiogoRibeiro7/behavioral-sensing-research
- Issues: github.com/DiogoRibeiro7/behavioral-sensing-research/issues
- Changelog: CHANGELOG.md
- Citation: CITATION.cff, archived at doi.org/10.5281/zenodo.17070041
Package Metadata
- Current release:
0.2.0 - Requires Python:
>=3.10,<3.13 - Status: beta
Where It Fits
Use it for reproducible analysis of activity and presence sensors where interpretability matters more than raw accuracy: routine modelling, anomaly and change-point detection in daily patterns, and privacy-preserving multimodal fusion. It is the software companion of the behavioural-sensing research listed on the papers page.