Disease detection from smartphone data represents an open research challenge
in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are
an important case study in this area and their early detection is a potential
real instrument to counteract the pandemic situation. The efficacy of this
solution mainly depends on the performances of AI algorithms applied to the
collected data and their possible implementation directly on the users' mobile
devices. Considering these issues, and the limited amount of available data, in
this paper we present the experimental evaluation of 3 different deep learning
models, compared also with hand-crafted features, and of two main approaches of
transfer learning in the considered scenario: both feature extraction and
fine-tuning. Specifically, we considered VGGish, YAMNET, and
L\textsuperscript{3}-Net (including 12 different configurations) evaluated
through user-independent experiments on 4 different datasets (13,447 samples in
total). Results clearly show the advantages of L\textsuperscript{3}-Net in all
the experimental settings as it overcomes the other solutions by 12.3\% in
terms of Precision-Recall AUC as features extractor, and by 10\% when the model
is fine-tuned. Moreover, we note that to fine-tune only the fully-connected
layers of the pre-trained models generally leads to worse performances, with an
average drop of 6.6\% with respect to feature extraction. %highlighting the
need for further investigations. Finally, we evaluate the memory footprints of
the different models for their possible applications on commercial mobile
devices.

通过实验评估了 3 种不同的深度学习模型以及两种传输学习方法，结果表明 L**3-Net 作为特征提取器在所有实验设置中具有 12.3％的精确度 - 召回曲线下面积的优势，并且在模型微调时优势为 10％。此外，我们注意到仅微调预训练模型的全连接层通常会导致性能下降，平均下降 6.6％。最后，我们评估了不同模型在商用移动设备上的内存占用。

手机音频数据的高效 COVID-19 检测的迁移学习

Transfer Learning for the Efficient Detection of COVID-19 from  Smartphone Audio Data

Predicting mental health from smartphone and social media data on a
longitudinal basis has recently attracted great interest, with very promising
results being reported across many studies. Such approaches have the potential
to revolutionise mental health assessment, if their development and evaluation
follows a real world deployment setting. In this work we take a closer look at
state-of-the-art approaches, using different mental health datasets and
indicators, different feature sources and multiple simulations, in order to
assess their ability to generalise. We demonstrate that under a pragmatic
evaluation framework, none of the approaches deliver or even approach the
reported performances. In fact, we show that current state-of-the-art
approaches can barely outperform the most na\"ive baselines in the real-world
setting, posing serious questions not only about their deployment ability, but
also about the contribution of the derived features for the mental health
assessment task and how to make better use of such data in the future.

本文探讨了近期关于使用智能手机数据和社交媒体数据预测精神健康问题，有很多研究报告表明其非常具有潜力，但是在实际环境中，并没有表现出较好的通用性。研究发现，在考虑真实世界环境并使用不同的特征源和多重模拟等多种方法进行比较后，目前最先进的方法仅能超过最基础的基线，无法持续改善数据和效果质量。

社交媒体和智能设备是否能够评估心理健康？解决方法和评价中的偏见问题

Can We Assess Mental Health through Social Media and Smart Devices?  Addressing Bias in Methodology and Evaluation

Accurate and computationally efficient means for classifying human activities
have been the subject of extensive research efforts. Most current research
focuses on extracting complex features to achieve high classification accuracy.
We propose a template selection approach based on Dynamic Time Warping, such
that complex feature extraction and domain knowledge is avoided. We demonstrate
the predictive capability of the algorithm on both simulated and real
smartphone data.

本文提出了一种基于动态时间规整的模板选择方法，避免了复杂特征提取和领域知识，展示了该算法在模拟和真实智能手机数据上的预测能力。