# TActiLE: Tiny Active LEarning for wearable devices

arXiv: https://arxiv.org/abs/2505.01160 · HTML: https://mlsanity.com/paper/2505.01160/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 2 May 2025.

## Builds on (most cited first, 10 of 10)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [On-Device Training Under 256KB Memory](https://mlsanity.com/paper/2206.15472/) (2022)
- [TinyOL: TinyML with Online-Learning on Microcontrollers](https://mlsanity.com/paper/2103.08295/) (2021)
- [A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays](https://mlsanity.com/paper/2110.10486/) (2021)
- [Online Submodular Maximization with Preemption](https://mlsanity.com/paper/1501.05801/) (2015)
- [Active Learning for Deep Neural Networks on Edge Devices](https://mlsanity.com/paper/2106.10836/) (2021)
- [TinySV: Speaker Verification in TinyML with On-device Learning](https://mlsanity.com/paper/2406.01655/) (2024)
- [TinyProp -- Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning](https://mlsanity.com/paper/2308.09201/) (2023)
- [Dendron: Enhancing Human Activity Recognition with On-Device TinyML Learning](https://mlsanity.com/paper/2503.01353/) (2025)

## Built on by (most cited first, 1 of 1)

- [What changes after deployment? A survey on On-device Learning in TinyML](https://mlsanity.com/paper/2605.31226/) (2026)

## Abstract (arXiv)

Tiny Machine Learning (TinyML) algorithms have seen extensive use in recent years, enabling wearable devices to be not only connected but also genuinely intelligent by running machine learning (ML) computations directly on-device. Among such devices, smart glasses have particularly benefited from TinyML advancements. TinyML facilitates the on-device execution of the inference phase of ML algorithms on embedded and wearable devices, and more recently, it has expanded into On-device Learning (ODL), which allows both inference and learning phases to occur directly on the device. The application of ODL techniques to wearable devices is particularly compelling, as it enables the development of more personalized models that adapt based on the data of the user. However, one of the major challenges of ODL algorithms is the scarcity of labeled data collected on-device. In smart wearable contexts, requiring users to manually label large amounts of data is often impractical and could lead to user disengagement with the technology. To address this issue, this paper explores the application of Active Learning (AL) techniques, i.e., techniques that aim at minimizing the labeling effort, by actively selecting from a large quantity of unlabeled data only a small subset to be labeled and added to the training set of the algorithm. In particular, we propose TActiLE, a novel AL algorithm that selects from the stream of on-device sensor data the ones that would help the ML algorithm improve the most once coupled with labels provided by the user. TActiLE is the first Active Learning technique specifically designed for the TinyML context. We evaluate its effectiveness and efficiency through experiments on multiple image classification datasets. The results demonstrate its suitability for tiny and wearable devices.