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.

High-precision robotic grasping is necessary for extensive grasping applications in the future. Most previous grasp detection methods fail to pay enough attention to learn grasp-related features and the detection accuracy is limited. In this letter, a novel attention-augmented grasp detection network (AAGDN) is presented to generate accurate grasp poses for unknown objects. The proposed AAGDN has three elaborate designs making it achieve higher accuracy than existing methods. First, we construct a coordinate attention residual module to extract positional information and improve the spatial sensitivity of features. Then, we propose an effective feature fusion module to bridge the resolution and semantic gaps of different-level features and obtain efficient feature representations. Lastly, a feature augmentation pyramid module is developed to enhance grasp-related features as needed and reduce the loss of information. Extensive experiments on three public datasets and various real-world scenes prove that the proposed AAGDN achieves better performance than current methods. Our model obtains the state-of-the-art 99.3% and 96.2% grasp detection accuracy on the Cornell and Jacquard dataset, respectively. Moreover, in physical grasping experiments, the AAGDN attains the 94.6% success rate for unseen objects in cluttered scenes, which further demonstrates the accuracy and robustness of our method to grasp novel objects.

This article proposes coupled tendon-driven joint modules for anthropomorphic robots. Fully actuated 2-degree-of-freedom (DoF) and 3-DoF joint modules are classified and analyzed based on the motor-joint routing matrix that describes the tendon routing structure between the motors and the joints. Two-motor 2-DoF (2M2D) modules, which share the same form of motor-joint routing matrix, are classified into four types: externally actuated, internally coaxially actuated, internally separately actuated, and hybrid-actuated according to the location of the motors. Three-motor 3-DoF (3M3D) modules are classified into four forms based on the four possible forms of motor-joint routing matrix: fully routed motor-joint, 1-unrouted motor-joint, 2-unrouted motor-joint, and 3-unrouted motor-joint. The 2M2D and 3M3D coupled tendon-driven joint modules are analyzed and compared with respect to the relationship between the motor torque and the joint torque. A 7-DoF anthropomorphic robot arm was implemented with one 3M3D module for the shoulder joint and two 2M2D modules for the elbow and wrist joints, respectively, to demonstrate the utility of the proposed joint modules. The arm weighed 2.2 kg and was able to lift a 1.5-kg load with full outreach. The characteristics of the joint modules were evaluated in a current consumption experiment and a position accuracy experiment, and the performance of the robot arm was evaluated in a master–slave manipulation experiment involving dexterous movements.

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.