mlsanityData to 7 Oct 2026

NapTune: Efficient Model Tuning for Mood Classification using Previous Night's Sleep Measures along with Wearable Time-series

arXiv 2409.04723 ↗First posted 7 Sep 2024eess.SP cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Sleep is known to be a key factor in emotional regulation and overall mental health. In this study, we explore the integration of sleep measures from the previous night into wearable-based mood recognition. To this end, we propose NapTune, a novel prompt-tuning framework that utilizes sleep-related measures as additional inputs to a frozen pre-trained wearable time-series encoder by adding and training lightweight prompt parameters to each Transformer layer. Through rigorous empirical evaluation, we demonstrate that the inclusion of sleep data using NapTune not only improves mood recognition performance across different wearable time-series namely ECG, PPG, and EDA, but also makes it more sample-efficient. Our method demonstrates significant improvements over the best baselines and unimodal variants. Furthermore, we analyze the impact of adding sleep-related measures on recognizing different moods as well as the influence of individual sleep-related measures.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 62 references that are in the map. First posted on arXiv 7 Sep 2024.

Builds on

13 of 62 references.

References · 13

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  5. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  6. 2020HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Unitscited by 45
  7. 2019Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Networkcited by 38
  8. 2019Self-supervised Learning for ECG-based Emotion Recognitioncited by 32
  9. 2023SpeechPrompt v2: Prompt Tuning for Speech Classification Taskscited by 31
  10. 2021A Transformer Architecture for Stress Detection from ECGcited by 27
  11. 2017Convolutional Recurrent Neural Networks for Electrocardiogram Classificationcited by 14
  12. 2020Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Datacited by 10
1 more
  1. 2023In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detectioncited by 7

Built on by

Not cited yet.