Attention-based MIL fuses irregular smartwatch activity, sleep, and ECG HRV data to predict discretized changes in handgrip strength and FACIT-F with balanced accuracies of 0.59-0.70 under LOSO evaluation.
Minimal detectable change in handgrip strength and usual and maximum gait speed scores in community- dwelling Japanese older adults requiring long-term care/support
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Frailty Estimation in Elderly Oncology Patients Using Multimodal Wearable Data and Multi-Instance Learning
Attention-based MIL fuses irregular smartwatch activity, sleep, and ECG HRV data to predict discretized changes in handgrip strength and FACIT-F with balanced accuracies of 0.59-0.70 under LOSO evaluation.