TransfHAR is implemented as a real-time smartwatch application that lets users define and expand their own activity set for personalized recognition from only a few demonstrations, and indicates that broad self-supervised wrist pretraining provides an effective foundation for on-demand fine-grained activity recognition.
Abstract
Fine-grained wrist activity recognition can support applications such as procedural step guidance and context-aware assistance, yet acquiring labeled data for every new task, user, and activity granularity remains a bottleneck. We present TransfHAR, a self-supervised wrist IMU framework for on-demand, fine-grained activity recognition by learning transferable motion priors from global, unlabeled activities. We show that self-supervised pretraining on coarse wrist IMU activities (e.g., sitting, walking, exercise) learns motion structure rich enough to transfer to fine-grained manipulative, gestural, and procedural activities (e.g., snapping, stirring, waving) that are absent from pretraining. We implement TransfHAR as a real-time smartwatch application that lets users define and expand their own activity set for personalized recognition from only a few demonstrations. Across three offline cross-dataset evaluations, TransfHAR matches or exceeds fully supervised baselines that use complete label sets with equal or additional sensor channels, by 6.2 balanced-accuracy points on average. In an in-lab study with 10 participants each performing seven novel wrist activities, TransfHAR reaches 86.7% balanced accuracy across participants with five examples per class and 90.4% when updated from a single one-minute recording per class. These results indicate that broad self-supervised wrist pretraining provides an effective foundation for on-demand fine-grained activity recognition.
Inertial sensors at multiple body locations can improve activity recognition, but requiring every sensor at inference increases the deployment burden. We study whether four synchronized IMUs available during training can improve a student that uses only the right-arm IMU during fitting and inference. A frozen four-IMU teacher provides logit and feature targets. Fixed-weight knowledge distillation applies each target with the same strength to every fitting sample, although the student may not benefit equally from them. We introduce dynamic influence weighting (DIW), which tests a one-step candidate update on separate fold-internal training participants. DIW then assigns separate sample-wise gates to the logit and feature losses. On WEAR, we evaluate 19 labels and 68,298 complete windows from 22 participants using subject-disjoint five-fold cross-validation. Pooled out-of-fold macro-F1 is 0.561820 for Supervised and 0.571623 for Fixed-weight KD. DIW reaches 0.638451, gains of 7.66 and 6.68 percentage points, respectively. It exceeds Supervised for 18 of 19 labels and 21 of 22 held-out participants. All three routes retain the same 80,915-parameter right-arm student at inference. Under this protocol, DIW converts training-only multi-position information into a stronger single-IMU model without changing deployed sensing or the student forward graph.
A novel lightweight cross-domain few-shot sensor-based HAR network (CFSH-Net) is proposed for cross-domain activity recognition with limited labeled samples, which demonstrates strong cross-user generalization on PAMAP2 and USC-HAD, and stable cross-dataset transfer when trained on OPPORTUNITY and evaluated on four other datasets.
Hao Zheng, Hongji Xu, Fei Gao et al.· IEEE journal of biomedical a...· 0 citations
This work introduces RAG-HAR+, a retrieval-first and cost-optimized extension that strengthens retrieval while reducing dependence on LLM-based inference, and extends the RAG-HAR mobile prototype to demonstrate the practical feasibility of retrieval-first, LLM-assisted HAR in mobile sensing scenarios.
The proposed Joint Embedding Predictive Architecture framework designed to learn robust and generalizable representations from unlabeled datasets demonstrates superior generalization on minority, high variance transitional activities such as sit-to-stand and sit-to-lie where supervised learning tend to overfit due to limited support.
Mohd Halim Mohd Noor, AbdulRahman M. A. Baraka· arXiv.org· 0 citations
Few-shot learning improves data efficiency in surface electromyography (sEMG) gesture recognition, yet cross-subject and cross-posture generalization remains challenging due to nonstationary signals, electrode displacement, and time-varying within-gesture dynamics. We propose STC+Meta, a framework that couples temporally informed representation learning with efficient personalization. In pre-training, a soft temporal contrastive loss assigns adaptive weights by time lag to encourage temporal continuity while preserving short-lived discriminative transients, yielding coherent fused features from noninvasive sEMG and forearm accelerometer (ACC). In adaptation, implicit MAML provides data-efficient meta-updates that personalize the pretrained encoder to a new subject using only 10% of the subject-specific calibration data (∼28 s in our 7-class protocol), while matching the performance of subject-specific training that uses the full Session 1 data (∼19 min). On a self-collected multi-session dataset comprising eight upper-limb postures and seven gestures, the configuration that applies STC pre-training followed by multi-posture few-shot meta-adaptation achieves 97.63% accuracy when 10% of subject-specific data (∼28 sec) is used for calibration; in a 10-class task-transfer setting, it attains 95.75%. Relative to batch fine-tuning, STC+Meta shows faster early-stage convergence and lower inter-subject variability under limited calibration data. These results indicate that temporally aware contrastive pre-training combined with meta-learning enables calibration-efficient personalization for myoelectric gesture recognition under biased conditions.
Yu-Xiang Hu, Kunkun Zhao, Ziyu Cheng et al.· IEEE journal of biomedical a...· 0 citations
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
