As a ubiquitous portable device, mobile phones play an important role in large-scale data collection and remote health detection. Parkinson's disease (PD), a typical movement disorder, can be detected by capturing digital biomarkers using mobile phone sensors. Nevertheless, it is difficult to obtain reliable label information in large-scale remote data collection, especially for time-series digital biomarkers. Based on this, we develop a novel multi-dimensional self-supervised contrastive learning framework for remote detection of early PD by mobile phone time-series digital biomarkers. Specifically, depending on two different augmentation views, the proposed framework considers temporal contrasting, spatial contrasting, contextual contrasting, and inter-modal contrasting to enable the model to learn more discriminative features. For temporal and spatial contrasting, certain time steps (channels) of one view are used to predict the next time steps (channels) of the other view, thereby constructing a cross-view prediction task. Meanwhile, contextual contrasting is introduced into the contrast framework to consider temporal prediction and spatial prediction context representation, respectively, further increasing similarity between positive pairs and decreasing it between negative pairs. In addition, inter-modal contrasting is used to force the model to capture the potential relationship between different modal data. Experimental results show that fine-tuning with only 10% of the labeled data can outperform supervised learning and generally surpass state-of the-art algorithms.

Mobile phones have evolved from basic communication tools to feature-rich mobile devices. These ubiquitous and portable devices, equipped with inertial sensors and high-speed network access, create opportunities for remote health monitoring, especially for movement disorders such as Parkinson's disease (PD). Inertial sensors (gyroscopes and accelerometers) endow smartphones with a natural ability to monitor movement disorders. Based on this, we develop a novel vision-based time-series feature augmentation framework for remote diagnosis and severity grading of PD using mobile phone walking records. Specifically, preprocessed time-series data is encoded into RGB images for the teacher model, while the time-series data is input into the student model, with the teacher guiding the student's learning. The teacher model is based on MobileNetV2 and incorporates spatial and channel relation-aware attention mechanisms to capture important features and filter out irrelevant information. The inter-modal feature fusion module combines attention and CNN to emphasize both global and local features. The student model utilizes a simple CNN to directly extract features from time-series data and perform classification. For the three-level classification task, the teacher model achieves accuracies of 0.887, 0.886, and 0.896 across the three datasets, while the distillation student model reaches 0.779, 0.828, and 0.827, generally surpassing state-of-the-art algorithms.

Recent years have witnessed the increasing popularity and dramatic progress of contactless sensing technologies, which are able to conduct remote signal acquisition without body contact. Both the physical signs and the physiological parameters can be acquired with contactless sensing. This paper introduces popular contactless sensing technologies, explores their application scenarios, and delves into the underlying theoretical principles. It comprehensively reviews the open datasets released in this field, encompassing collection scenarios, sample counts, data formats, and volunteer information. The performance baseline, typical work, and accessible links are also furnished. In addition, it includes discussions on the primary challenges and potential solutions in the context of contactless sensing with open datasets. Finally, suggestions for establishing a high-quality dataset are also given to the community.