Magnetic tracking has been well studied for the localization of wireless capsule robots. However, most of the existing magnetic localization systems require a stable background magnetic field, which is not suitable for wearable applications as the movement of the human body inevitably changes the magnetic field to each sensor. The purpose of this study is to reduce the disturbance of the ambient magnetic field for localizations in different background scenarios. The proposed approach applies the magnetic density of multiple sampling points for simultaneous localization. By subtracting the magnetic field value in different positions, the background noise (BGN) field can be offset. As a result, multipoint simultaneous positioning can be achieved by using an optimization algorithm. The influence of the signal-to-noise ratio on localization accuracy has been determined through simulation analysis. Experiments first verified the feasibility of multipoint simultaneous positioning and then conducted in different geomagnetic noise and permanent magnet environments. The results show that the proposed method has been verified to be robust in different BGN environments. The proposed method is expected to be used in wearable systems for tracking magnetic capsule endoscope (MCE).

Accurate and unbiased examinations of skin lesions are critical for the early diagnosis and treatment of skin diseases. Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment. Recent studies have reported that ensembled convolutional neural networks (CNNs) are practical to classify the images for early diagnosis of skin disorders. However, the practical use of these ensembled CNNs is limited as these networks are heavyweight and inadequate for processing contextual information. Although lightweight networks (e.g., MobileNetV3 and EfficientNet) were developed to achieve parameter reduction for implementing deep neural networks on mobile devices, insufficient depth of feature representation restricts the performance. To address the existing limitations, we develop a new lite and effective neural network, namely HierAttn. The HierAttn applies a novel deep supervision strategy to learn the local and global features by using multi-stage and multi-branch attention mechanisms with only one training loss. The efficacy of HierAttn was evaluated by using the dermoscopy images dataset ISIC2019 and smartphone photos dataset PAD-UFES-20 (PAD2020). The experimental results show that HierAttn achieves the best accuracy and area under the curve (AUC) among the state-of-the-art lightweight networks. The code is available at https://github.com/anthonyweidai/HierAttn .

Infertility is a global problem that affects many couples. Sperm analysis plays an essential role in the clinical diagnosis of human fertility. The examination of sperm morphology is an essential technique because sperm morphology is a proven indicator of biological functions. At present, the morphological classification of human sperm is conducted manually by medical experts. However, manual classification is laborious and highly dependent on the experience and capability of clinicians. To address these limitations, we propose a transfer learning method based on AlexNet to automatically classify the sperms into four different categories in terms of the World Health Organization (WHO) standards by analyzing their morphology. We adopt the feature extraction architecture of AlexNet as well as its pre-training parameters. Besides, we redesign the classification network by adding the Batch Normalization layers to improve the performance. The proposed method achieves an average accuracy of 96.0% and an average precision of 96.4% in the freely-available HuSHeM dataset, which exceeds the performance of previous algorithms. Our method shows that automatic sperm classification has great potential to replace manual sperm classification in the future.

Magnetically actuated miniature robots are limited in their mechanical outputting capability, because the magnetic forces decrease significantly with decreasing robot size and increasing actuating distance. Hence, the output force of these robots can hardly meet the demand for specific biomedical applications (e.g., tissue penetration). This article proposes a tetherless magnetic impact needle robot (MINRob) based on a triple-magnet system with reversible and repeatable magnetic collisions to overcome this constraint on output force. The working procedure of the proposed system is divided into several states, and a mathematical model is developed to predict and optimize the force output. These force values in magnetic impact and penetration are obtained from a customized setup, indicating a 10-fold increase compared with existing miniature robots that only utilize magnetic attractive force. Eventually, the proposed MINRob is integrated with a teleoperation system, enabling remote and precise control of the robot's position and orientation. The triple-magnet system offers promising locomotion patterns and penetration capacity via the notably increased force output, showing great potential in robot-assisted tissue penetration in minimally invasive healthcare.