Human Depth Sensors-based Activity Recognition using Spatiotemporal Features and Hidden Markov Model for Smart Environments

116Yscopu

Functionalized Magnetic Force Enhances Magnetic Nanoparticle Guidance: From Simulation to Crossing of the Blood-Brain Barrier in vivo

In recent studies, we introduced the concept of functionalized magnetic force as a method to prevent nanoparticles from sticking to vessel walls caused by extensive simulation and in vitro experiments involving a Y-shaped channel. In this study, we further investigated the effectiveness of the functionalized magnetic force with a realistic 3D vessel through simulations. For the simulations, we considered a more realistic continuous injection of particles with different magnetic forces and frequencies. Based on the results from our simulation studies, we performed in vivo mice experiments to evaluate the effectiveness of using a functionalized magnetic force to aid magnetic nanoparticles (MNPs) in crossing the blood-brain barrier (BBB). To implement the functionalized magnetic force, we developed an electromagnetic actuator regulated by a programmable direct current (DC) power supply. Our results indicate that a functionalized magnetic field can effectively prevent MNPs from sticking, and also guide them across the BBB. We used 770-nm fluorescent carboxyl MNPs in this study. Following intravenous administration of MNPs into mice, we applied an external magnetic field (EMF) to mediate transport of the MNPs across the BBB and into the brain. Furthermore, we evaluated the differential effects of functionalized magnetic fields (0.25, 0.5, and 1 Hz) and constant magnetic fields on the transport of MNPs across the BBB. Our results showed that a functionalized magnetic field is more effective than a constant magnetic field in the transport and uptake of MNPs across the BBB in mice. Specifically, applying a functionalized magnetic field with a 3 A current and 0.5 Hz frequency mediated the greatest transport and uptake of MNPs across the BBB in mic

Functionalized Magnetic Force Enhances Magnetic Nanoparticle Guidance: From Simulation to Crossing of the Blood-Brain Barrier in vivo

In recent studies, we introduced the concept of functionalized magnetic force as a method to prevent nanoparticles from sticking to vessel walls caused by extensive simulation and in vitro experiments involving a Y-shaped channel. In this study, we further investigated the effectiveness of the functionalized magnetic force with a realistic 3D vessel through simulations. For the simulations, we considered a more realistic continuous injection of particles with different magnetic forces and frequencies. Based on the results from our simulation studies, we performed in vivo mice experiments to evaluate the effectiveness of using a functionalized magnetic force to aid magnetic nanoparticles (MNPs) in crossing the blood-brain barrier (BBB). To implement the functionalized magnetic force, we developed an electromagnetic actuator regulated by a programmable direct current (DC) power supply. Our results indicate that a functionalized magnetic field can effectively prevent MNPs from sticking, and also guide them across the BBB. We used 770-nm fluorescent carboxyl MNPs in this study. Following intravenous administration of MNPs into mice, we applied an external magnetic field (EMF) to mediate transport of the MNPs across the BBB and into the brain. Furthermore, we evaluated the differential effects of functionalized magnetic fields (0.25, 0.5, and 1 Hz) and constant magnetic fields on the transport of MNPs across the BBB. Our results showed that a functionalized magnetic field is more effective than a constant magnetic field in the transport and uptake of MNPs across the BBB in mice. Specifically, applying a functionalized magnetic field with a 3 A current and 0.5 Hz frequency mediated the greatest transport and uptake of MNPs across the BBB in mic

Development of Real-Time Electromyography Controlled 3D Printed Robot Hand Prototype

Developing an anthropomorphic robotic hand (ARH) has become a relevant research field due to the need to help the amputees live their life as normal people. However, the current state of research is unsatisfactory, especially in terms of structural design and the robot control method. This paper, which proposes a 3D printed ARH structure that follows the average size of an adult human hand, consists of five fingers with a tendon-driven actuator mechanism embedded in each finger structure. Besides that, the movement capability of the developed 3D printed robot hand validated by using motion capture analysis to ensure the similarity to the expected motion range in structural design is achieved. Its system functionality test was conducted in three stages: (1) muscular activity detection, (2) object detection for individual finger movement control, and (3) integration of both stages in one algorithm. Finally, an ARH was developed, which resembles human hand features, as well as a reliable system that can perform opened hand palm and some grasping postures for daily use

Position Control of the Single Spherical Wheel Mobile Robot by Using the Fuzzy Sliding Mode Controller

A spherical wheel robot or Ballbot—a robot that balances on an actuated spherical ball—is a new and recent type of robot in the popular area of mobile robotics. This paper focuses on the modeling and control of such a robot. We apply the Lagrangian method to derive the governing dynamic equations of the system. We also describe a novel Fuzzy Sliding Mode Controller (FSMC) implemented to control a spherical wheel mobile robot. The nonlinear nature of the equations makes the controller nontrivial. We compare the performance of four different fuzzy controllers: (a) regulation with one signal, (b) regulation and position control with one signal, (c) regulation and position control with two signals, and (d) FSMC for regulation and position control with two signals. The system is evaluated in a realistic simulation and the robot parameters are chosen based on a LEGO platform, so the designed controllers have the ability to be implemented on real hardware

Respiratory Compensated Robot for Liver Cancer Treatment: Design, Fabrication, and Benchtop Characterization

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death in the world. Radiofrequency ablation (RFA) is an effective method for treating tumors less than 5 cm. However, manually placing the RFA needle at the site of the tumor is challenging due to the complicated respiratory induced motion of the liver. This paper presents the design, fabrication, and benchtop characterization of a patient mounted, respiratory compensated robotic needle insertion platform to perform percutaneous needle interventions. The robotic platform consists of a 4-DoF dual-stage cartesian platform used to control the pose of a 1-DoF needle insertion module. The active needle insertion module consists of a 3D printed flexible fluidic actuator capable of providing a step-like, grasp-insert-release actuation that mimics the manual insertion procedure. Force characterization of the needle insertion module indicates that the device is capable of producing 22.6 ± 0.40 N before the needle slips between the grippers. Static phantom targeting experiments indicate a positional error of 1.14 ± 0.30 mm and orientational error of 0.99° ± 0.36°. Static ex-vivo porcine liver targeting experiments indicate a positional error of 1.22 ± 0.31 mm and orientational error of 1.16° ± 0.44°. Dynamic targeting experiments with the proposed active motion compensation in dynamic phantom and ex-vivo porcine liver show 66.3% and 69.6% positional accuracy improvement, respectively. Future work will continue to develop this platform with the long-term goal of applying the system to RFA for HCC

Impact of UAV Hardware Options on Bridge Inspection Mission Capabilities

Uncrewed Aerial Vehicles (UAV) constitute a rapidly evolving technology field that is becoming more accessible and capable of supplementing, expanding, and even replacing some traditionally manual bridge inspections. Given the classification of the bridge inspection types as initial, routine, in-depth, damage, special, and fracture critical members, specific UAV mission requirements can be developed, and their suitability for UAV application examined. Results of a review of 23 applications of UAVs in bridge inspections indicate that mission sensor and payload needs dictate the UAV configuration and size, resulting in quadcopter configurations being most suitable for visual camera inspections (43% of visual inspections use quadcopters), and hexa- and octocopter configurations being more suitable for higher payload hyperspectral, multispectral, and Light Detection and Ranging (LiDAR) inspections (13%). In addition, the number of motors and size of the aircraft are the primary drivers in the cost of the vehicle. 75% of vehicles rely on GPS for navigation, and none of them are capable of contact inspections. Factors that limit the use of UAVs in bridge inspections include the UAV endurance, the capability of navigation in GPS deprived environments, the stability in confined spaces in close proximity to structural elements, and the cost. Current research trends in UAV technologies address some of these limitations, such as obstacle detection and avoidance methods, autonomous flight path planning and optimization, and UAV hardware optimization for specific mission requirements

Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed