Effect of treadmill training on fibrocartilage complex repair in tendon-bone insertion healing in the postinflammatory stage.

Aims: Mechanical stimulation is a key factor in the development and healing of tendon-bone insertion. Treadmill training is an important rehabilitation treatment. This study aims to investigate the benefits of treadmill training initiated on postoperative day 7 for tendon-bone insertion healing. Methods: A tendon-bone insertion injury healing model was established in 92 C57BL/6 male mice. All mice were divided into control and training groups by random digital table method. The control group mice had full free activity in the cage, and the training group mice started the treadmill training on postoperative day 7. The quality of tendon-bone insertion healing was evaluated by histology, immunohistochemistry, reverse transcription quantitative polymerase chain reaction, Western blotting, micro-CT, micro-MRI, open field tests, and CatWalk gait and biomechanical assessments. Results: Our results showed a significantly higher tendon-bone insertion histomorphological score in the training group, and the messenger RNA and protein expression levels of type II collagen (COL2A1), SOX9, and type X collagen (COL10A1) were significantly elevated. Additionally, tendon-bone insertion resulted in less scar hyperplasia after treadmill training, the bone mineral density (BMD) and bone volume/tissue volume (BV/TV) were significantly improved, and the force required to induce failure became stronger in the training group. Functionally, the motor ability, limb stride length, and stride frequency of mice with tendon-bone insertion injuries were significantly improved in the training group compared with the control group. Conclusion: Treadmill training initiated on postoperative day 7 is beneficial to tendon-bone insertion healing, promoting biomechanical strength and motor function. Our findings are expected to guide clinical rehabilitation training programmes.

Comparison of Suture Button and Syndesmotic Screw for Ankle Syndesmotic Injuries: A Meta-analysis of Randomized Controlled Trials.

Background: The syndesmotic screw (SS) and suture button (SB) fixation methods are both widely used for the reduction of ankle syndesmotic injury, with varying outcomes. Purpose: To review recently published randomized controlled trials (RCTs) to assess the outcomes between SS and SB fixation for ankle syndesmotic injury. Study Design: Systematic review; Level of evidence, 1. Methods: The PubMed, Embase, ClinicalTrials.gov, and Cochrane databases were searched for relevant RCTs published between 1966 and 2021 according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Eligible studies were RCTs comparing SS and SB fixation for ankle syndesmotic injury. The risk of bias was evaluated using the Cochrane Risk of Bias tool. Primary outcomes included complications, malreduction, and unplanned reoperation, and secondary outcomes were the American Orthopaedic Foot & Ankle Society (AOFAS) score, Olerud-Molander ankle score (OMAS), and EuroQol-5 Domain (EQ-5D) score. The mean difference (MD) and risk ratio (RR) were calculated for continuous and dichotomous outcomes, respectively. Random- or fixed-effects model was applied according to heterogeneity. Results: Of 389 studies, 8 RCTs involving 512 patients were included. Overall, 257 patients received SS fixation and 255 patients received SB fixation. The 2 groups did not differ significantly in malreduction (RR, -0.06; 95% CI, -0.18 to 0.07) or EQ-5D (MD, 0.01; 95% CI, -0.01 to 0.03). However, the SB group showed significant advantages over the SS group in complications (RR, 0.42; 95% CI, 0.26 to 0.66), unplanned reoperation (RR, 0.62; 95% CI, 0.43 to 0.89), AOFAS score (MD, 3.04; 95% CI, 1.77 to 4.31), and OMAS (MD, 4.51; 95% CI, 1.54 to 7.48). The risk of bias of the included studies was acceptable. Conclusion: The results showed that there were no significant differences between the SS and SB groups in malreduction and EQ-5D scores. However, the SB group had significantly better local irritation rates, unplanned reoperation rates, AOFAS scores, and OMASs.

Inhibition of CX3CL1 by treadmill training prevents osteoclast-induced fibrocartilage complex resorption during TBI healing.

Introduction: The healing of tendon-bone injuries is very difficult, often resulting in poor biomechanical performance and unsatisfactory functional recovery. The tendon-bone insertion has a complex four distinct layers structure, and previous studies have often focused on promoting the regeneration of the fibrocartilage layer, neglecting the role of its bone end repair in tendon-bone healing. This study focuses on the role of treadmill training in promoting bone regeneration at the tendon-bone insertion and its related mechanisms. Methods: After establishing the tendon-bone insertion injury model, the effect of treadmill training on tendon-bone healing was verified by Micro CT and HE staining; then the effect of CX3CL1 on osteoclast differentiation was verified by TRAP staining and cell culture; and finally the functional recovery of the mice was verified by biomechanical testing and behavioral test. Results: Treadmill training suppresses the secretion of CX3CL1 and inhibits the differentiation of local osteoclasts after tendon-bone injury, ultimately reducing osteolysis and promoting tendon bone healing. Discussion: Our research has found the interaction between treadmill training and the CX3CL1-C3CR1 axis, providing a certain theoretical basis for rehabilitation training.

s-CAM: An Untethered Insertable Laparoscopic Surgical Camera Robot with Non-Contact Actuation.

Fully insertable robotic imaging devices represent a promising future of minimally invasive laparoscopic vision. Emerging research efforts in this field have resulted in several proof-of-concept prototypes. One common drawback of these designs derives from their clumsy tethering wires which not only cause operational interference but also reduce camera mobility. In this paper, a tetherless insertable surgical camera (s-CAM) robot with non-contact transabdominal actuation is presented for single-incision laparoscopic vision. Wireless video transmission and control communication using onboard power help eliminate cumbersome tethering wires. Furthermore, magnetic based camera actuation gets rid of intrinsic physical constraints of mechanical driving mechanisms, thereby improving camera mobility and reducing operational interference. In addition, a custom Bluetooth low energy (BLE) application profile and a real-time operating system (RTOS) based multitask programming framework are also proposed to facilitate embedded software design for insertable medical devices. Initial ex vivo test results of the s-CAM design have demonstrated technical feasibility of a tetherless insertable laparoscopic camera. Effective imaging is confirmed at as low as 500 lx illumination. Wireless laparoscopic vision is accessible within a distance of more than 10 m. Transabdominal BLE communication is stable at over -52 dBm and shows its potential for wireless control of insertable medical devices. RTOS based sfotware event response is bounded within 1 ms while the CPU usage is at 3∼5%. The device is able to work for 50 min with its onboard power. For the mobility, the robot can translate against the interior abdominal wall to reach full abdomen quadrants, tilt between -180∘ and +180∘, and pan in the range of 0∘∼360∘. The s-CAM has brought robotic laparoscopic imaging one step further toward less invasiveness and more dexterity.

Transmission dynamics and control methodology of COVID-19: A modeling study.

The coronavirus disease 2019 (COVID-19) has grown up to be a pandemic within a short span of time. To investigate transmission dynamics and then determine control methodology, we took epidemic in Wuhan as a study case. Unfortunately, to our best knowledge, the existing models are based on the common assumption that the total population follows a homogeneous spatial distribution, which is not the case for the prevalence occurred both in the community and in hospital due to the difference in the contact rate. To solve this problem, we propose a novel epidemic model called SEIR-HC, which is a model with two different social circles (i.e., individuals in hospital and community). Using the model alongside the exclusive optimization algorithm, the spread process of COVID-19 epidemic in Wuhan city is reproduced and then the propagation characteristics and unknown data are estimated. The basic reproduction number of COVID-19 is estimated to be 7.9, which is far higher than that of the severe acute respiratory syndrome (SARS). Furthermore, the control measures implemented in Wuhan are assessed and the control methodology of COVID-19 is discussed to provide guidance for limiting the epidemic spread.

Segmenting areas of potential contamination for adaptive robotic disinfection in built environments.

Mass-gathering built environments such as hospitals, schools, and airports can become hot spots for pathogen transmission and exposure. Disinfection is critical for reducing infection risks and preventing outbreaks of infectious diseases. However, cleaning and disinfection are labor-intensive, time-consuming, and health-undermining, particularly during the pandemic of the coronavirus disease in 2019. To address the challenge, a novel framework is proposed in this study to enable robotic disinfection in built environments to reduce pathogen transmission and exposure. First, a simultaneous localization and mapping technique is exploited for robot navigation in built environments. Second, a deep-learning method is developed to segment and map areas of potential contamination in three dimensions based on the object affordance concept. Third, with short-wavelength ultraviolet light, the trajectories of robotic disinfection are generated to adapt to the geometries of areas of potential contamination to ensure complete and safe disinfection. Both simulations and physical experiments were conducted to validate the proposed methods, which demonstrated the feasibility of intelligent robotic disinfection and highlighted the applicability in mass-gathering built environments.

Validating an automated image identification process of a passive image-assisted dietary assessment method: proof of concept.

OBJECTIVE: To validate an automated food image identification system, DietCam, which has not been validated, in identifying foods with different shapes and complexities from passively taken digital images. DESIGN: Participants wore Sony SmartEyeglass that automatically took three images per second, while two meals containing four foods, representing regular- (i.e., cookies) and irregular-shaped (i.e., chips) foods and single (i.e., grapes) and complex (i.e., chicken and rice) foods, were consumed. Non-blurry images from the meals' first 5 min were coded by human raters and compared with DietCam results. Comparisons produced four outcomes: true positive (rater/DietCam reports yes for food), false positive (rater reports no food; DietCam reports food), true negative (rater/DietCam reports no food) or false negative (rater reports food; DietCam reports no food). SETTING: Laboratory meal. PARTICIPANTS: Thirty men and women (25·1 ± 6·6 years, 22·7 ± 1·6 kg/m2, 46·7 % White). RESULTS: Identification accuracy was 81·2 and 79·7 % in meals A and B, respectively (food and non-food images) and 78·7 and 77·5 % in meals A and B, respectively (food images only). For food images only, no effect of food shape or complexity was found. When different types of images, such as 100 % food in the image and on the plate, <100 % food in the image and on the plate and food not on the plate, were analysed separately, images with food on the plate had a slightly higher accuracy. CONCLUSIONS: DietCam shows promise in automated food image identification, and DietCam is most accurate when images show food on the plate.

Fine orientation control of an insertable robotic camera system for single incision laparoscopic surgery.

BACKGROUND: Insertable laparoscopic camera systems were developed to improve the minimally invasive surgeries. Robotic degrees of freedom for an insertable laparoscopic camera are required to adjust the camera's orientation and position inside an abdominal cavity. METHODS: This paper demonstrates an insertable magnetic actuated robotic camera system with two-degree-of-freedom (2-DoF) orientation control for single incision laparoscopic surgery. The camera system design consists of an external magnetic control unit and a fully insertable camera capsule. This system features a unified mechanism for anchoring, navigating, and rotating the insertable camera capsule by externally generated rotational magnetic field from the control unit. The motor-free camera capsule is encapsulated in an one-piece housing with two ring-shaped tail-end magnets and one cylindrical central magnet. The control unit that positioned externally consists of both permanent magnets and electromagnetic coils to generate rotational magnetic field and control the camera capsule. RESULTS: The experimental investigations indicated that the camera control system can achieve less than 1° control accuracies with average errors 0.594° and 0.524° for tilt motion and pan motion, respectively. CONCLUSION: The designed control system provides fine orientation control for the insertable camera capsule which guarantees proper vision for the surgeon during single incision laparoscopic surgery.

Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation.

Vertebrae computed tomography (CT) image automatic segmentation is an essential step for Image-guided minimally invasive spine surgery. However, most of state-of-the-art methods still require human intervention due to the inherent limitations of vertebrae CT image, such as topological variation, irregular boundaries (double boundary, weak boundary), and image noise. Therefore, this paper intentionally designed an automatic global level set approach (AGLSA), which is capable of dealing with these issues for lumbar vertebrae CT image segmentation. Unlike the traditional level set methods, we firstly propose an automatically initialized level set function (AILSF) that comprises hybrid morphological filter (HMF) and Gaussian mixture model (GMM) to automatically generate a smooth initial contour which is precisely adjacent to the object boundary. Secondly, a regularized level set formulation is introduced to overcome the weak boundary leaking problem, which utilizes the region correlation of histograms inside and outside the level set contour as a global term. Ultimately, a gradient vector flow (GVF) based edge-stopping function is employed to guarantee a fast convergence rate of the level set evolution and to avoid level set function oversegmentation at the same time. Our proposed approach has been tested on 115 vertebrae CT volumes of various patients. Quantitative comparisons validate that our proposed AGLSA is more accurate in segmenting lumbar vertebrae CT images with irregular boundaries and more robust to various levels of salt-and-pepper noise.

Adaptive Absolute Ego-Motion Estimation Using Wearable Visual-Inertial Sensors for Indoor Positioning.

This paper proposes an adaptive absolute ego-motion estimation method using wearable visual-inertial sensors for indoor positioning. We introduce a wearable visual-inertial device to estimate not only the camera ego-motion, but also the 3D motion of the moving object in dynamic environments. Firstly, a novel method dynamic scene segmentation is proposed using two visual geometry constraints with the help of inertial sensors. Moreover, this paper introduces a concept of "virtual camera" to consider the motion area related to each moving object as if a static object were viewed by a "virtual camera". We therefore derive the 3D moving object's motion from the motions for the real and virtual camera because the virtual camera's motion is actually the combined motion of both the real camera and the moving object. In addition, a multi-rate linear Kalman-filter (MR-LKF) as our previous work was selected to solve both the problem of scale ambiguity in monocular camera tracking and the different sampling frequencies of visual and inertial sensors. The performance of the proposed method is evaluated by simulation studies and practical experiments performed in both static and dynamic environments. The results show the method's robustness and effectiveness compared with the results from a Pioneer robot as the ground truth.

Wearable Heading Estimation for Motion Tracking in Health Care by Adaptive Fusion of Visual-Inertial Measurementsments.

The increasing demand for health informatics has become a far-reaching trend in the ageing society. The utilization of wearable sensors enables monitoring senior people daily activities in free-living environments, conveniently and effectively. Among the primary health-care sensing categories, the wearable visual-inertial modality for human motion tracking gradually exerts promising potentials. In this paper, we present a novel wearable heading estimation strategy to track the movements of human limbs. It adaptively fuses inertial measurements with visual features following locality constraints. Body movements are classified into two types: general motion (which consists of both rotation and translation). or degenerate motion (which consists of only rotation). A specific number of feature correspondences between camera frames are adaptively chosen to satisfy both the feature descriptor similarity constraint and the locality constraint. The selected feature correspondences and inertial quaternions are employed to calculate the initial pose, followed by the coarse-to-fine procedure to iteratively remove visual outliers. Eventually, the ultimate heading is optimized using the correct feature matches. The proposed method has been thoroughly evaluated on the straight-line, rotatory and ambulatory movement scenarios. As the system is lightweight and requires small computational resources, it enables effective and unobtrusive human motion monitoring, especially for the senior citizens in the long-term rehabilitation.

Wearable Heading Estimation for Motion Tracking in Health Care by Adaptive Fusion of Visual-Inertial Measurements.

The increasing demand for health informatics has become a far-reaching trend in the ageing society. The utilization of wearable sensors enables monitoring senior people daily activities in free-living environments, conveniently and effectively. Among the primary health-care sensing categories, the wearable visual-inertial modality for human motion tracking gradually exerts promising potentials. In this paper, we present a novel wearable heading estimation strategy to track the movements of human limbs. It adaptively fuses inertial measurements with visual features following locality constraints. Body movements are classified into two types: general motion (which consists of both rotation and translation). or degenerate motion (which consists of only rotation). A specific number of feature correspondences between camera frames are adaptively chosen to satisfy both the feature descriptor similarity constraint and the locality constraint. The selected feature correspondences and inertial quaternions are employed to calculate the initial pose, followed by the coarse-to-fine procedure to iteratively remove visual outliers. Eventually, the ultimate heading is optimized using the correct feature matches. The proposed method has been thoroughly evaluated on the straight-line, rotatory and ambulatory movement scenarios. As the system is lightweight and requires small computational resources, it enables effective and unobtrusive human motion monitoring, especially for the senior citizens in the long-term rehabilitation.

Optical design of an in vivo laparoscopic lighting system.

This paper proposes an in vivo laparoscopic lighting system design to address the illumination issues, namely poor lighting uniformity and low optical efficiency, existing in the state-of-the-art in vivo laparoscopic cameras. The transformable design of the laparoscopic lighting system is capable of carrying purposefully designed freeform optical lenses for achieving lighting performance with high illuminance uniformity and high optical efficiency in a desired target region. To design freeform optical lenses for extended light sources such as LEDs with Lambertian light intensity distributions, we present an effective and complete freeform optical design method. The procedures include (1) ray map computation by numerically solving a standard Monge-Ampere equation; (2) initial freeform optical surface construction by using Snell's law and a lens volume restriction; (3) correction of surface normal vectors due to accumulated errors from the initially constructed surfaces; and (4) feedback modification of the solution to deal with degraded illuminance uniformity caused by the extended sizes of the LEDs. We employed an optical design software package to evaluate the performance of our laparoscopic lighting system design. The simulation results show that our design achieves greater than 95% illuminance uniformity and greater than 89% optical efficiency (considering Fresnel losses) for illuminating the target surgical region.

DietCam: Multiview Food Recognition Using a Multikernel SVM.

Food recognition is a key component in evaluation of everyday food intakes, and its challenge is due to intraclass variation. In this paper, we present an automatic food classification method, DietCam, which specifically addresses the variation of food appearances. DietCam consists of two major components, ingredient detection and food classification. Food ingredients are detected through a combination of a deformable part-based model and a texture verification model. From the detected ingredients, food categories are classified using a multiview multikernel SVM. In the experiment, DietCam presents reliability and outperformance in recognition of food with complex ingredients on a database including 15,262 food images of 55 food types.

RGB-D SLAM Combining Visual Odometry and Extended Information Filter.

In this paper, we present a novel RGB-D SLAM system based on visual odometry and an extended information filter, which does not require any other sensors or odometry. In contrast to the graph optimization approaches, this is more suitable for online applications. A visual dead reckoning algorithm based on visual residuals is devised, which is used to estimate motion control input. In addition, we use a novel descriptor called binary robust appearance and normals descriptor (BRAND) to extract features from the RGB-D frame and use them as landmarks. Furthermore, considering both the 3D positions and the BRAND descriptors of the landmarks, our observation model avoids explicit data association between the observations and the map by marginalizing the observation likelihood over all possible associations. Experimental validation is provided, which compares the proposed RGB-D SLAM algorithm with just RGB-D visual odometry and a graph-based RGB-D SLAM algorithm using the publicly-available RGB-D dataset. The results of the experiments demonstrate that our system is quicker than the graph-based RGB-D SLAM algorithm.

Loop Closing Detection in RGB-D SLAM Combining Appearance and Geometric Constraints.

A kind of multi feature points matching algorithm fusing local geometric constraints is proposed for the purpose of quickly loop closing detection in RGB-D Simultaneous Localization and Mapping (SLAM). The visual feature is encoded with BRAND (binary robust appearance and normals descriptor), which efficiently combines appearance and geometric shape information from RGB-D images. Furthermore, the feature descriptors are stored using the Locality-Sensitive-Hashing (LSH) technique and hierarchical clustering trees are used to search for these binary features. Finally, the algorithm for matching of multi feature points using local geometric constraints is provided, which can effectively reject the possible false closure hypotheses. We demonstrate the efficiency of our algorithms by real-time RGB-D SLAM with loop closing detection in indoor image sequences taken with a handheld Kinect camera and comparative experiments using other algorithms in RTAB-Map dealing with a benchmark dataset.

A novel approach to ECG classification based upon two-layered HMMs in body sensor networks.

This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient's ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen.

Monocular camera and IMU integration for indoor position estimation.

This paper presents a monocular camera (MC) and inertial measurement unit (IMU) integrated approach for indoor position estimation. Unlike the traditional estimation methods, we fix the monocular camera downward to the floor and collect successive frames where textures are orderly distributed and feature points robustly detected, rather than using forward oriented camera in sampling unknown and disordered scenes with pre-determined frame rate and auto-focus metric scale. Meanwhile, camera adopts the constant metric scale and adaptive frame rate determined by IMU data. Furthermore, the corresponding distinctive image feature point matching approaches are employed for visual localizing, i.e., optical flow for fast motion mode; Canny Edge Detector & Harris Feature Point Detector & Sift Descriptor for slow motion mode. For superfast motion and abrupt rotation where images from camera are blurred and unusable, the Extended Kalman Filter is exploited to estimate IMU outputs and to derive the corresponding trajectory. Experimental results validate that our proposed method is effective and accurate in indoor positioning. Since our system is computationally efficient and in compact size, it's well suited for visually impaired people indoor navigation and wheelchaired people indoor localization.

An adaptive-gain complementary filter for real-time human motion tracking with MARG sensors in free-living environments.

High-resolution, real-time data obtained by human motion tracking systems can be used for gait analysis, which helps better understanding the cause of many diseases for more effective treatments, such as rehabilitation for outpatients or recovery from lost motor functions after a stroke. In order to achieve real-time ambulatory human motion tracking with low-cost MARG (magnetic, angular rate, and gravity) sensors, a computationally efficient and robust algorithm for orientation estimation is critical. This paper presents an analytically derived method for an adaptive-gain complementary filter based on the convergence rate from the Gauss-Newton optimization algorithm (GNA) and the divergence rate from the gyroscope, which is referred as adaptive-gain orientation filter (AGOF) in this paper. The AGOF has the advantages of one iteration calculation to reduce the computing load and accurate estimation of gyroscope measurement error. Moreover, for handling magnetic distortions especially in indoor environments and movements with excessive acceleration, adaptive measurement vectors and a reference vector for earth's magnetic field selection schemes are introduced to help the GNA find more accurate direction of gyroscope error. The features of this approach include the accurate estimation of the gyroscope bias to correct the instantaneous gyroscope measurements and robust estimation in conditions of fast motions and magnetic distortions. Experimental results are presented to verify the performance of the proposed method, which shows better accuracy of orientation estimation than several well-known methods.

A real-time cardiac arrhythmia classification system with wearable sensor networks.

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient's ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches.