The non-contact monitoring of vital signs without body-attached sensors is a promising home healthcare technique. Recent studies have shown that millimeter-wave (mmW) radar can be used to continuous daily monitoring of vital signs such as heart rate (HR) and respiratory rate (RR). To minimize disruption to the user’s daily life and accommodate for home environments, low-power radar or radar with antennas in package (AiP) is becoming increasingly popular. However, the lower transmitting power of these systems can lead to decreased Signal-to-Noise Ratio (SNR) of the radar signal, making established measurement methods ineffective. The vital signs, particularly HR, are no longer accurately measured due to the small amplitude associated with heartbeats. In this work, we present HeRe for low-power radar, a deep learning approach for heartbeat signal reconstruction. The proposed method utilizes a neural network to identify patterns in the signal, leading to a significant improvement in the SNR of the heartbeat signal. By leveraging historical data, the current HR estimation is further refined, yielding improved precision in the results. Experiments have demonstrated the efficacy of signal reconstruction for improving the heartbeat signal in both the time and frequency domains. A comparison of the proposed approach to related works on various HR distributions, sensing ranges, and subjects showed significantly higher accuracy. To further assess the robustness of the proposed approach, extended continuous monitoring was performed and 97.5% accuracy was achieved. This article highlights the advantages of deep learning techniques for non-contact, radar-based HR monitoring, leading to accurate HR monitoring with the lowest power consumer-grade millimeter wave radar currently available on the market.

Industrial bin picking is a challenging task as lots of highly reflective and textureless objects are stacked. Previous methods generally predict gripper configurations or 6D object pose by expensive industrial 3D camera, ignoring the role of consumer-grade RGB-D cameras in this task. For highly reflective objects, depth maps of consumer-grade RGB-D cameras are generally of a lower quality with much stronger noise, making it challenging to detect robust grasp poses in stacked industrial scenarios. To address these challenges, we propose a novel grasp detection framework including a synthetic data generation pipeline, a novel local grasp detection method, and a global search algorithm. Specifically, in the training phase, we utilize the Pybullet and OpenGL to generate synthetic data by approximate 3D models. We introduce a novel grasp representation and propose MixStyle-ResUnet to predict and achieve sim2real transfer for this representation. In the testing phase, the Object Singleness Metric (OSM) is proposed for the global search block to evaluate the graspable probability. In addition, the RGB image enhanced by edge feature is utilized as the input of the network, which can bridge the domain shift. Extensive experiments in real scenes demonstrate that our method can achieve competitive results in grasping disordered metal parts. In over 2400 robotic grasp trials, our method achieves an average success rate of 91.1% in dense stack scenarios. We also confirm that the proposed method can be applied to unseen objects which are not included in the training dataset. Code and videos are available at https://github.com/sunhan1997/IndusGrasp.

Robust and efficient contactless human–machine interaction is critical for integrated sensor systems in clinical settings, demanding low-power solutions adaptable to edge computing platforms. This paper presents a real-time hand gesture recognition system using a low-power Frequency-Modulated Continuous Wave (FMCW) radar sensor, featuring a novel Multiple Feature Fusion (MFF) framework optimized for deployment on edge devices. The proposed system integrates velocity profiles, angular variations, and spatial-temporal features through a dual-stage processing architecture: an adaptive energy thresholding detector segments gestures, followed by an attention-enhanced neural classifier. Innovations include dynamic clutter suppression and multi-path cancellation optimized for complex clinical environments. Experimental validation demonstrates high performance, achieving 98% detection recall and 93.87% classification accuracy under LOSO cross-validation. On embedded hardware, the system processes at 28 FPS, showing higher robustness against environmental noise and lower computational overhead compared with existing methods. This low-power, edge-based solution is highly suitable for applications like sterile medical control and patient monitoring, advancing contactless interaction in healthcare by addressing efficiency and robustness challenges in radar sensing for edge computing.

Existing visual simultaneous localization and mapping (SLAM) systems struggle with loop closure under significant viewpoint variations, such as revisiting the same place orthogonally or oppositely. This limitation primarily stems from the lack of viewpoint invariance in both macroscopic place definition and microscopic feature description. Based on the crucial insight that geometric information is more viewpoint-invariant than visual information, we create map segments via leveraging the position and coplanarity distribution of map elements within the map constructed by monocular SLAM to overcome the limitations of frame-based place definition, and extract perspective-invariant ORB (PRIOR) features via accounting for local surface perspective distortions to enhance perspective invariance without the need for costly perspective invariance estimation or additional depth data. We further utilize map segments and PRIOR features to hierarchically detect and correct loop closures with a coarse-to-fine geometric consistency check. We integrate our novelties into the prevalent SLAM framework, thereby proposing PRIOR-SLAM, which achieves state-of-the-art performance in feature matching and retrieval, visual place recognition, and loop closure under large viewpoint changes.

The nursing bed has become an important tool for medical and elder care, as a well-designed bed can prevent users from bedsores. To improve the user convenience, a user-friendly human-computer interface is also necessary. In this paper, we design a new nursing bed containing a FMCW millimeter wave radar for gesture recognition. A control system can help user frictionless back-up, integral turning over and leg raising when lying in the bed. Besides, we adopt the convolutional recurrent neural network for recognition of 6 gestures. Experiments with the nursing bed show that the control success rate of the bed reaches 93.75% and the response time achieves 1.03 s.