# B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows

arXiv: https://arxiv.org/abs/2101.10870 · HTML: https://mlsanity.com/paper/2101.10870/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 69 references that are in the map.
- First posted on arXiv 23 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey](https://mlsanity.com/paper/2004.08821/) (2020)
- [Joint Distribution and Transitions of Pain and Activity in Critically Ill Patients](https://mlsanity.com/paper/2004.09134/) (2020)

## Built on by (most cited first, 4 of 4)

- [Estimating indoor occupancy through low-cost BLE devices](https://mlsanity.com/paper/2102.03351/) (2021)
- [Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition](https://mlsanity.com/paper/2404.11742/) (2024)
- [Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables](https://mlsanity.com/paper/2511.05985/) (2025)
- [WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition](https://mlsanity.com/paper/2606.13194/) (2026)

## Abstract (arXiv)

Human Activity Recognition (HAR), based on machine and deep learning algorithms is considered one of the most promising technologies to monitor professional and daily life activities for different categories of people (e.g., athletes, elderly, kids, employers) in order to provide a variety of services related, for example to well-being, empowering of technical performances, prevention of risky situation, and educational purposes. However, the analysis of the effectiveness and the efficiency of HAR methodologies suffers from the lack of a standard workflow, which might represent the baseline for the estimation of the quality of the developed pattern recognition models. This makes the comparison among different approaches a challenging task. In addition, researchers can make mistakes that, when not detected, definitely affect the achieved results. To mitigate such issues, this paper proposes an open-source automatic and highly configurable framework, named B-HAR, for the definition, standardization, and development of a baseline framework in order to evaluate and compare HAR methodologies. It implements the most popular data processing methods for data preparation and the most commonly used machine and deep learning pattern recognition models.