Human–Robot Interaction (HRI) using hand gesture recognition offers an effective and non-contact approach to enhancing operational intuitiveness and user convenience. However, most existing studies primarily focus on either static sign language recognition or the tracking of hand position and orientation in space. These approaches often prove inadequate for controlling complex robotic systems. This paper proposes an advanced HRI system leveraging dynamic hand gestures for controlling quadruped robots equipped with a robotic arm. The proposed system integrates both semantic and pose information from dynamic gestures to enable comprehensive control over the robot’s diverse functionalities. First, a Depth–MediaPipe framework is introduced to facilitate the precise three-dimensional (3D) coordinate extraction of 21 hand bone keypoints. Subsequently, a Semantic-Pose to Motion (SPM) model is developed to analyze and interpret both the pose and semantic aspects of hand gestures. This model translates the extracted 3D coordinate data into corresponding mechanical actions in real-time, encompassing quadruped robot locomotion, robotic arm end-effector tracking, and semantic-based command switching. Extensive real-world experiments demonstrate the proposed system’s effectiveness in achieving real-time interaction and precise control, underscoring its potential for enhancing the usability of complex robotic platforms.

Aim: To systematically review current literature to determine the association between symptomatic osteoarthritis and dietary patterns, diet quality and food groups in adults aged ≥45 years.Methods: The review was registered on PROSPERO (CRD42021270891). Cochrane Central Library, Cumulative Index of Nursing and Allied Health Literature, Embase, Medline and Web of Science databases were searched. A total of 3816 records were identified. Eligible articles involved populations aged ≥45 years with symptomatic osteoarthritis, assessing dietary patterns, diet quality or food groups, with pain in joints as outcomes. The Joanna Briggs Institute Critical Appraisal Checklists were used for quality assessment. Grading of Recommendations, Assessment, Development and Evaluation was used to assess the certainty of evidence.Results: Six cohort studies were included. The Prudent dietary pattern and the Mediterranean dietary pattern reduced the progression of osteoarthritis symptoms. The Western dietary pattern increased symptomatic osteoarthritis progression. Increased total fibre consumption reduced symptomatic osteoarthritis progression and pain worsening, but the effects of fibre from each food group were inconclusive. Diet with high inflammatory potential increased risk of new onset symptomatic osteoarthritis, but the effects of overall diet quality were inconclusive.Conclusions: The Prudent dietary pattern showed the highest protection on symptomatic osteoarthritis in adults aged 45 years and over. The body of evidence is limited, suggesting that further research is needed to corroborate the estimated effect at a high certainty of evidence, and to incorporate previously unstudied dietary patterns and food groups. Identifying the most beneficial dietary pattern may inform future guidelines for reducing symptomatic osteoarthritis in middle aged and older adults.

Bacterial infection is rising as a threatening health issue. Because of the present delay in early diagnosis of bacterial diseases as well as the abuse of antibiotics, it has become a vital issue in the development of in-time detection and therapy of bacterial infections. Herein, we designed a multifunctional nanotheranostics platform based on the unique micro-environment of bacterial infections to achieve specific bioimaging and simultaneous inactivation of the target bacteria. We showed that in bacterial infections, the metal precursors (i.e., HAuCl₄, FeCl₂, and herring sperm DNA) could be readily bio-self-assembled to multifunctional nanoclusters (NCs) that exhibit luminescence, in which AuCl₄⁻ was biosynthesized via reductive biomolecules such as NADPH to the fluorescent AuNCs. The DNA may assist as an encapsulation and delivery vector, and Fe²⁺ served as a fluorescence intensifier and reduced reactive oxygen species (ROS) to produce the iron oxides. While the bacteria were being visualized, the microenvironment-responsive NCs were enabled to sterilize bacteria efficiently due to electrostatic effect, cell membrane destruction, inhibition of biofilm formation, and ROS accumulation. Besides, the bio-responsive self-assembled NCs complexes contributed to accelerating bacteria-infected wound healing and showed negligible side effects in long-term toxicity tests in vivo. Also, intracellular molecules involved in microenvironmental response were investigated. The work may become an effective strategy for the detection and real-time sterilization of intractable bacterial infections.

Walking robots have been widely applied in complex terrains due to their good terrain adaptability and trafficability. However, in some environments (such as disaster relief, field navigation, etc.), although a single strategy can adapt to various environments, it is unable to strike a balance between speed and stability. Existing control schemes like model predictive control (MPC) and traditional incremental control can manage certain environments. However, they often cannot balance speed and stability well. These methods usually rely on a single strategy and lack adaptability for dynamic adjustment to different terrains. To address this limitation, this paper proposes an innovative double-layer reinforcement learning algorithm. This algorithm combines Deep Double Q-Network (DDQN) and Proximal Policy Optimization (PPO), leveraging their complementary strengths to achieve both fast adaptation and high stability in complex terrains. This algorithm utilizes terrain information and the robot’s state as observations, determines the walking speed command of the quadruped robot Unitree Go1 through DDQN, and dynamically adjusts the current walking speed in complex terrains based on the robot action control system of PPO. The speed command serves as a crucial link between the robot’s perception and movement, guiding how fast the robot should walk depending on the environment and its internal state. By using DDQN, the algorithm ensures that the robot can set an appropriate speed based on what it observes, such as changes in terrain or obstacles. PPO then executes this speed, allowing the robot to navigate in real time over difficult or uneven surfaces, ensuring smooth and stable movement. Then, the proposed model is verified in detail in Isaac Gym. Wecompare the distances walked by the robot using six different control methods within 10 s. The experimental results indicate that the method proposed in this paper demonstrates excellent speed adjustment ability in complex terrains. On the designed test route, the quadruped robot Unitree Go1 can not only maintain a high walking speed but also maintain a high degree of stability when switching between different terrains. Ouralgorithm helps the robot walk 25.5 m in 10 s, outperforming other methods.