A Novel Local-Global Feature Fusion Framework for Body-weight Exercise Recognition with Pressure Mapping Sensors

We present a novel local-global feature fusion framework for body-weight exercise recognition with floor-based dynamic pressure maps. One step further from the existing studies using deep neural networks mainly focusing on global feature extraction, the proposed framework aims to combine local and global features using image processing techniques and the YOLO object detection to localize pressure profiles from different body parts and consider physical constraints. The proposed local feature extraction method generates two sets of high-level local features consisting of cropped pressure mapping and numerical features such as angular orientation, location on the mat, and pressure area. In addition, we adopt a knowledge distillation for regularization to preserve the knowledge of the global feature extraction and improve the performance of the exercise recognition. Our experimental results demonstrate a notable 11 percent improvement in F1 score for exercise recognition while preserving label-specific features.

TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction

The rapid tracer kinetics of rubidium-82 ($^{82}$Rb) and high variation of cross-frame distribution in dynamic cardiac positron emission tomography (PET) raise significant challenges for inter-frame motion correction, particularly for the early frames where conventional intensity-based image registration techniques are not applicable. Alternatively, a promising approach utilizes generative methods to handle the tracer distribution changes to assist existing registration methods. To improve frame-wise registration and parametric quantification, we propose a Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) to transform the early frames into the late reference frame using an all-to-one mapping. Specifically, a feature-wise linear modulation layer encodes channel-wise parameters generated from temporal tracer kinetics information, and rough cardiac segmentations with local shifts serve as the anatomical information. We validated our proposed method on a clinical $^{82}$Rb PET dataset and found that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, motion estimation accuracy and clinical myocardial blood flow (MBF) quantification were improved compared to using the original frames. Our code is published at https://github.com/gxq1998/TAI-GAN.

Two-stage Early Prediction Framework of Remaining Useful Life for Lithium-ion Batteries

Early prediction of remaining useful life (RUL) is crucial for effective battery management across various industries, ranging from household appliances to large-scale applications. Accurate RUL prediction improves the reliability and maintainability of battery technology. However, existing methods have limitations, including assumptions of data from the same sensors or distribution, foreknowledge of the end of life (EOL), and neglect to determine the first prediction cycle (FPC) to identify the start of the unhealthy stage. This paper proposes a novel method for RUL prediction of Lithium-ion batteries. The proposed framework comprises two stages: determining the FPC using a neural network-based model to divide the degradation data into distinct health states and predicting the degradation pattern after the FPC to estimate the remaining useful life as a percentage. Experimental results demonstrate that the proposed method outperforms conventional approaches in terms of RUL prediction. Furthermore, the proposed method shows promise for real-world scenarios, providing improved accuracy and applicability for battery management.

Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field

Manufacturing industries strive to improve production efficiency and product quality by deploying advanced sensing and control systems. Wearable sensors are emerging as a promising solution for achieving this goal, as they can provide continuous and unobtrusive monitoring of workers' activities in the manufacturing line. This paper presents a novel wearable sensing prototype that combines IMU and body capacitance sensing modules to recognize worker activities in the manufacturing line. To handle these multimodal sensor data, we propose and compare early, and late sensor data fusion approaches for multi-channel time-series convolutional neural networks and deep convolutional LSTM. We evaluate the proposed hardware and neural network model by collecting and annotating sensor data using the proposed sensing prototype and Apple Watches in the testbed of the manufacturing line. Experimental results demonstrate that our proposed methods achieve superior performance compared to the baseline methods, indicating the potential of the proposed approach for real-world applications in manufacturing industries. Furthermore, the proposed sensing prototype with a body capacitive sensor and feature fusion method improves by 6.35%, yielding a 9.38% higher macro F1 score than the proposed sensing prototype without a body capacitive sensor and Apple Watch data, respectively.

PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps

We propose PressureTransferNet, a novel method for Human Activity Recognition (HAR) using ground pressure information. Our approach generates body-specific dynamic ground pressure profiles for specific activities by leveraging existing pressure data from different individuals. PressureTransferNet is an encoder-decoder model taking a source pressure map and a target human attribute vector as inputs, producing a new pressure map reflecting the target attribute. To train the model, we use a sensor simulation to create a diverse dataset with various human attributes and pressure profiles. Evaluation on a real-world dataset shows its effectiveness in accurately transferring human attributes to ground pressure profiles across different scenarios. We visually confirm the fidelity of the synthesized pressure shapes using a physics-based deep learning model and achieve a binary R-square value of 0.79 on areas with ground contact. Validation through classification with F1 score (0.911$\pm$0.015) on physical pressure mat data demonstrates the correctness of the synthesized pressure maps, making our method valuable for data augmentation, denoising, sensor simulation, and anomaly detection. Applications span sports science, rehabilitation, and bio-mechanics, contributing to the development of HAR systems.

Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation

Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge from related source domains to an unlabeled target domain. While recent MUDA methods have shown promising results, most focus on aligning the overall feature distributions across source domains, which can lead to negative effects due to redundant features within each domain. Moreover, there is a significant performance gap between MUDA and supervised methods. To address these challenges, we propose a novel approach called Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation (D3AAMDA). Firstly, we establish a multi-source dynamic modulation mechanism during the training process based on the degree of distribution differences between source and target domains. This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains. Additionally, we propose a Multi-source Active Boundary Sample Selection (MABS) strategy, which utilizes a guided dynamic boundary loss to design an efficient query function for selecting important samples. This strategy achieves improved generalization to the target domain with minimal sampling costs. We extensively evaluate our proposed method on commonly used domain adaptation datasets, comparing it against existing UDA and ADA methods. The experimental results unequivocally demonstrate the superiority of our approach.

Selecting the motion ground truth for loose-fitting wearables: benchmarking optical MoCap methods

To help smart wearable researchers choose the optimal ground truth methods for motion capturing (MoCap) for all types of loose garments, we present a benchmark, DrapeMoCapBench (DMCB), specifically designed to evaluate the performance of optical marker-based and marker-less MoCap. High-cost marker-based MoCap systems are well-known as precise golden standards. However, a less well-known caveat is that they require skin-tight fitting markers on bony areas to ensure the specified precision, making them questionable for loose garments. On the other hand, marker-less MoCap methods powered by computer vision models have matured over the years, which have meager costs as smartphone cameras would suffice. To this end, DMCB uses large real-world recorded MoCap datasets to perform parallel 3D physics simulations with a wide range of diversities: six levels of drape from skin-tight to extremely draped garments, three levels of motions and six body type - gender combinations to benchmark state-of-the-art optical marker-based and marker-less MoCap methods to identify the best-performing method in different scenarios. In assessing the performance of marker-based and low-cost marker-less MoCap for casual loose garments both approaches exhibit significant performance loss (>10cm), but for everyday activities involving basic and fast motions, marker-less MoCap slightly outperforms marker-based MoCap, making it a favorable and cost-effective choice for wearable studies.

Transformer-based Dual-domain Network for Few-view Dedicated Cardiac SPECT Image Reconstructions

Cardiovascular disease (CVD) is the leading cause of death worldwide, and myocardial perfusion imaging using SPECT has been widely used in the diagnosis of CVDs. The GE 530/570c dedicated cardiac SPECT scanners adopt a stationary geometry to simultaneously acquire 19 projections to increase sensitivity and achieve dynamic imaging. However, the limited amount of angular sampling negatively affects image quality. Deep learning methods can be implemented to produce higher-quality images from stationary data. This is essentially a few-view imaging problem. In this work, we propose a novel 3D transformer-based dual-domain network, called TIP-Net, for high-quality 3D cardiac SPECT image reconstructions. Our method aims to first reconstruct 3D cardiac SPECT images directly from projection data without the iterative reconstruction process by proposing a customized projection-to-image domain transformer. Then, given its reconstruction output and the original few-view reconstruction, we further refine the reconstruction using an image-domain reconstruction network. Validated by cardiac catheterization images, diagnostic interpretations from nuclear cardiologists, and defect size quantified by an FDA 510(k)-cleared clinical software, our method produced images with higher cardiac defect contrast on human studies compared with previous baseline methods, potentially enabling high-quality defect visualization using stationary few-view dedicated cardiac SPECT scanners.

Lala Shakti Swarup Ray, Bo Zhou, Sungho Suh, Paul Lukowicz

To help smart wearable researchers choose the optimal ground truth methods for motion capturing (MoCap) for all types of loose garments, we present a benchmark, DrapeMoCapBench (DMCB), specifically designed to evaluate the performance of optical marker-based and marker-less MoCap. High-cost marker-based MoCap systems are well-known as precise golden standards. However, a less well-known caveat is that they require skin-tight fitting markers on bony areas to ensure the specified precision, making them questionable for loose garments. On the other hand, marker-less MoCap methods powered by computer vision models have matured over the years, which have meager costs as smartphone cameras would suffice. To this end, DMCB uses large real-world recorded MoCap datasets to perform parallel 3D physics simulations with a wide range of diversities: six levels of drape from skin-tight to extremely draped garments, three levels of motions and six body type - gender combinations to benchmark state-of-the-art optical marker-based and marker-less MoCap methods to identify the best-performing method in different scenarios. In assessing the performance of marker-based and low-cost marker-less MoCap for casual loose garments both approaches exhibit significant performance loss (>10cm), but for everyday activities involving basic and fast motions, marker-less MoCap slightly outperforms marker-based MoCap, making it a favorable and cost-effective choice for wearable studies.

SynthCal: A Synthetic Benchmarking Pipeline to Compare Camera Calibration Algorithms

Accurate camera calibration is crucial for various computer vision applications. However, measuring camera parameters in the real world is challenging and arduous, and there needs to be a dataset with ground truth to evaluate calibration algorithms' accuracy. In this paper, we present SynthCal, a synthetic camera calibration benchmarking pipeline that generates images of calibration patterns to measure and enable accurate quantification of calibration algorithm performance in camera parameter estimation. We present a SynthCal-generated calibration dataset with four common patterns, two camera types, and two environments with varying view, distortion, lighting, and noise levels. The dataset evaluates single-view calibration algorithms by measuring reprojection and root-mean-square errors for identical patterns and camera settings. Additionally, we analyze the significance of different patterns using Zhang's method, which estimates intrinsic and extrinsic camera parameters with known correspondences between 3D points and their 2D projections in different configurations and environments. The experimental results demonstrate the effectiveness of SynthCal in evaluating various calibration algorithms and patterns.