FALLNEST is a data‑centric framework that condenses large, imbalanced inertial measurement unit (IMU) datasets into tiny, class‑balanced synthetic training sets for wearable fall detection. It combines dual‑domain time‑series condensation, normalized‑gradient matching, MMD‑based embedding alignment, and multi‑view Fourier augmentation to generate 32–146 synthetic windows that enable a 102K‑parameter TinyCNN to achieve 93.5–95.8% mean accuracy and 0.89–0.98 PR‑AUC on SisFall, KFall, and MobiFall benchmarks.
arXiv:2608. 13197v1 Announce Type: new Abstract: Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention.
By Timilehin B. Aderinola, Ilaria D'Ascanio, Luca Palmerini, Lorenzo Chiari, Jochen Klenk, Clemens Becker, Brian Caulfield, Georgiana Ifrim
Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention. However, real-world falls are extremely rare: collecting 100 of them requires an estimated 100,000 days of monitoring, resulting in severely limited labelled data for training machine learning models.
arXiv:2608. 05782v1 Announce Type: cross Abstract: Wearable human activity recognition (HAR) is often limited by the scarcity of labeled sensor data, especially in low-resource, class-imbalanced, and subject-generalization settings.
By Lala Shakti Swarup Ray, Vitor Fortes Rey, Mengxi Liu, Paul Lukowicz, Bo Zhou
arXiv:2606. 10250v1 Announce Type: cross Abstract: Class imbalance is a common problem in deep learning that severely degrades performance.
By Haengbok Chung, Jae Sung Lee
arXiv:2610.27443v1 Announce Type: new
Abstract: Set-valued classification (SVC) offers a practical approach to communicating uncertainty while maintaining accuracy by predicting variable-sized sets p...
By Kevin Kasa, Scott C. Lowe, Graham W. Taylor