The scarcity of labeled training data have significantly hindered the deployment of Inertial Measurement Unit-based Human Activity Recognition (IMU-HAR) in real-world scenarios. To address this limitation, we pro-pose SSSL-HAR (Synthetic-data-driven Self-Supervised-Learning HAR), a novel framework that leverages large amount synthetic IMU data for self-supervised pre-training, followed by fine-tuning with minimal real-world data. This approach mitigates the reliance on large-scale real IMU data collection compared to the traditional self-supervised-learning frameworks and also bypasses the need for labor-intensive annotation or cross-modality alignment compared to the traditional cross-modality methods. Furthermore, by adopting multi-view contrastive learning (MVCL) architectures, our method effectively captures the intrinsic relationships between synthetic sensor views, enabling robust generalization to diverse sensor placements and configurations. Experiments on the PAMAP2 dataset and a more complex custom fitness monitoring dataset demonstrate that SSSL-HAR achieves performance comparable to models pre-trained on real data, highlighting its potential for scalable and adaptive HAR deployment.

The recognition of motions based on surface electromyography (sEMG) has been extensively studied, yielding promising results from initial machine learning approaches to contemporary deep learning methods. However, most previous research has concentrated on the classification of movements from individual body parts, such as the widely used gesture dataset, NinaPro. Furthermore, much of work has been restricted to convolutional neural networks (CNNs) and their variants, without a thorough exploration of the synergistic effects of muscles from different body parts, often assigning equal weights to all muscles. This study presents the collection of electromyographic data from sixteen major muscles across the entire body, acquiring the MultiMotion-sEMG Dataset, which includes forty-three full-body movements from thirteen participants. According to the current knowledge, this is the first dataset designed to synchronize the capture of full-body surface electromyography (sEMG) signals. Based on this dataset, a novel sEMG recognition network, SEmgFormer, is proposed, which is augmented by a vision transformer (ViT). The short-time Fourier transform (STFT) is utilized to transform conventional time-domain sEMG signal recognition tasks into visual understanding tasks of time-frequency spectrograms, utilizing the Cutmix method for data augmentation. In addition, a novel Muscle Synergy Channel Attention (MS-CA) mechanism is introduced, improving the channel attention mechanism (CA). The results indicate that the proposed model surpasses other methods in performance, including CNN-based networks, achieving optimal accuracy. This validates the efficacy of the proposed ViT classifier using time-frequency spectrograms as input, enhancing the accuracy of sEMG recognition based on full-body signals, and paving new avenues for research in this field.

This paper proposes DML-FitAR, a novel deep metric learning framework for IMU-based fitness action recognition, addressing critical challenges in real-world deployment. Unlike traditional transfer learning methods requiring fine-tuning for new action types, DML-FitAR achieves competitive accuracy on unseen actions through a retraining-free paradigm. Evaluated on a custom dataset (560+ fitness actions) and the MyoGYM dataset, DML-FitAR demonstrates superior performance over contrastive learning and visual backbone-based approaches, achieving cross-action-type recognition accuracy ranging from 80% to 90%. Besides that, the framework also exhibits robustness to sensor placement variations and noteworthy cross-dataset generalization.

Against the backdrop of an aging population and the high prevalence of chronic diseases, the demand for rehabilitation medical services has surged. However, traditional rehabilitation action difficulty assessment relies on expert experience, suffering from strong subjectivity and low reliability. Existing assessment methods struggle to cover the full-body kinematic characteristics, and existing models lack directed modeling of action difficulty relationships and fail to effectively capture muscle synergy. To address this, this study constructs a 16-channel full-body sEMG dataset, sEmgHuman-594, which includes 594 rehabilitation actions. This study proposes an sEMG-DGCN assessment method based on Directed Graph Convolutional Network (DGCN), which integrates 11-dimensional expert-annotated difficulty criteria to derive difficulty labels, employs directed graphs to model difficulty relationships between actions, and introduces an Anatomically Constrained Spatiotemporal Attention mechanism. Experimental results show that the sEMG-DGCN model achieves an accuracy of 93.61% in difficulty relationship classification, significantly outperforming comparative models. Ablation experiments verify the effectiveness of the attention mechanism, providing a new pathway for rehabilitation action difficulty assessment.

Brain atrophy emerges as a distinctive hallmark in various neurodegenerative diseases, demonstrating a progressive trajectory across diverse disease stages and concurrently manifesting in tandem with a discernible decline in cognitive abilities. Understanding the individualized patterns of brain atrophy is critical for precision medicine and the prognosis of neurodegenerative diseases. However, it is difficult to obtain longitudinal data to compare changes before and after the onset of diseases. In this study, we present a deep disentangled generative model (DDGM) for capturing individualized atrophy patterns via disentangling patient images into "realistic" healthy counterfactual images and abnormal residual maps. The proposed DDGM consists of four modules: normal MRI synthesis, residual map synthesis, input reconstruction module, and mutual information neural estimator (MINE). The MINE and adversarial learning strategy together ensure independence between disease-related features and features shared by both disease and healthy controls. In addition, we proposed a comprehensive evaluation of the effectiveness of synthetic pseudo-healthy images, focusing on both their healthiness and subject identity. The results indicated that the proposed DDGM effectively preserves these characteristics in the synthesized pseudo-healthy images, outperforming existing methods. The proposed method demonstrates robust generalization capabilities across two independent datasets from different races and sites. Analysis of the disease residual/saliency maps revealed specific atrophy patterns associated with Alzheimer's disease (AD), particularly in the hippocampus and amygdala regions. These accurate individualized atrophy patterns enhance the performance of AD classification tasks, resulting in an improvement in classification accuracy to 92.50 $\pm$ 2.70%.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method for capturing the microanatomical information of tissues by measuring the diffusion weighted signals along multiple directions, which is widely used in the quantification of microstructures. Obtaining microscopic parameters requires dense sampling in the q space, leading to significant time consumption. The most popular approach to accelerating dMRI acquisition is to undersample the q-space data, along with applying deep learning methods to reconstruct quantitative diffusion parameters. However, the reliance on a predetermined q-space sampling strategy often constrains traditional deep learning-based reconstructions. The present study proposed a novel deep learning model, named attention-based q-space deep learning (aqDL), to implement the reconstruction with variable q-space sampling strategies. The aqDL maps dMRI data from different scanning strategies onto a common feature space by using a series of Transformer encoders. The latent features are employed to reconstruct dMRI parameters via a multilayer perceptron. The performance of the aqDL model was assessed utilizing the Human Connectome Project datasets at varying undersampling numbers. To validate its generalizability, the model was further tested on two additional independent datasets. Our results showed that aqDL consistently achieves the highest reconstruction accuracy at various undersampling numbers, regardless of whether variable or predetermined q-space scanning strategies are employed. These findings suggest that aqDL has the potential to be used on general clinical dMRI datasets.