What does this research mean for the field?

The TActiLE active learning algorithm enables efficient on-device learning for wearable devices by outperforming random selection and complex stream active learning algorithms within stringent resource constraints. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

The goal is to develop an active learning algorithm that minimizes user labeling effort for models on wearable devices.

March 24, 20261 citations

TActiLE: Tiny Active LEarning for wearable devices

MPMassimo Pavan CGClaudio Galimberti MRManuel Roveri

Key Points

The goal is to develop an active learning algorithm that minimizes user labeling effort for models on wearable devices.
Propose TActiLE, a novel active learning algorithm for TinyML.
Evaluate the algorithm using multiple image classification datasets.
Compare TActiLE against random selection and Stream Active Learning algorithms.
TActiLE outperforms simple random selection and more complex algorithms.
Demonstrated effectiveness within resource constraints of wearable environments.

Abstract

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 user's data. 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 that, within the stringent resource constraints of TinyML and wearables environments, TActiLE outperforms both a simple random selection baseline and more complex Stream Active Learning algorithms, demonstrating its suitability for tiny and wearable devices.

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