TY - GEN
T1 - Task-Difficulty Aware Meta-Learning for Adaptive Few-Shot Human Activity Recognition Using UWB Sensors
AU - Park, Ji Sang
AU - Eom, Tae Hoon
AU - Jung, Seung Hwan
AU - Yun, Woo Jin
AU - Ji, Dong Jin
AU - Kim, Hyeon June
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This study presents the Difficulty-Adaptive Strategy Selector for Learning (DASSL), a task-aware metalearning framework designed for adaptive few-shot human activity recognition (HAR) with ultra-wideband (UWB) sensors. To validate the proposed system, we evaluated DASSL using data acquired with our self-developed UWB sensor. DASSL dynamically adjusts key training parameters-learning rate, regularization, and inner-loop update steps-based on taskspecific statistical difficulty indicators, including intra-class variance, inter-class similarity, and query-support alignment. Experimental results show that DASSL achieves 90.2% accuracy, outperforming fixed-strategy and task-agnostic metalearning baselines by 6.5% and 4.9%, respectively. Furthermore, the adaptive selection of hyperparameters enhances interpretability and computational efficiency, making the framework well-suited for deployment in resourceconstrained elderly health assistance systems.
AB - This study presents the Difficulty-Adaptive Strategy Selector for Learning (DASSL), a task-aware metalearning framework designed for adaptive few-shot human activity recognition (HAR) with ultra-wideband (UWB) sensors. To validate the proposed system, we evaluated DASSL using data acquired with our self-developed UWB sensor. DASSL dynamically adjusts key training parameters-learning rate, regularization, and inner-loop update steps-based on taskspecific statistical difficulty indicators, including intra-class variance, inter-class similarity, and query-support alignment. Experimental results show that DASSL achieves 90.2% accuracy, outperforming fixed-strategy and task-agnostic metalearning baselines by 6.5% and 4.9%, respectively. Furthermore, the adaptive selection of hyperparameters enhances interpretability and computational efficiency, making the framework well-suited for deployment in resourceconstrained elderly health assistance systems.
KW - Elderly health assistance system
KW - Few-shot learning
KW - Human activity recognition
KW - Meta-learning
KW - Task difficultyaware adaptation
KW - UWB sensors
UR - https://www.scopus.com/pages/publications/105034099569
U2 - 10.1109/SENSORS59705.2025.11330872
DO - 10.1109/SENSORS59705.2025.11330872
M3 - Conference contribution
AN - SCOPUS:105034099569
T3 - Proceedings of IEEE Sensors
BT - IEEE SENSORS 2025 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE SENSORS
Y2 - 19 October 2025 through 22 October 2025
ER -