Moving object detection via TV-L1 optical flow in fall-down videos

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524

Effect of uncoated and coated diamond on the compressive properties of porous aluminium composites

Porous aluminium composites are structural and functional materials that have vast potential, due to their lightweight and high energy absorption capacity, especially in automotive and aerospace applications. In this study, the effect of varying content of uncoated and Titanium coated diamond particles on the compressive properties of porous aluminium composite was investigated. The composites were developed using powder metallurgy technique and porosity was attained by using polymethylmethacrylate (30 wt.%) as space holder material. The morphology of the pores was found to replicate the shape and size of polymethylmethacrylate particles, that were uniformly distributed in the composites. X-ray diffraction analysis confirmed formation of Aluminium carbide in uncoated diamond-based aluminium composites while negligible amount was present in titanium coated porous composites during sintering. The porosities of composites decreased with an increase in diamond content due to the incomplete decomposition of polymethylmethacrylate particles. Moreover, the maximum plateau stress and energy absorption capacity of 9.96 MPa and 1.7 Mj/m3 were obtained for the composites with 8 wt.% of titanium coated diamond particles. Thus, coating inhibits the formation of undesirable compounds and contributes to better interfacial bonding between matrix and reinforcement

Effects of nanocellulose fiber and thymol on mechanical, thermal, and barrier properties of corn starch films

This study explores the preparation of corn starch (CS) films incorporated with nanocellulose fiber (NCF) and different concentrations of thymol (0.1, 0.3, and 0.5% weight of thymol/volume of solution (% w/v)) via the solvent casting method. The resulting films were characterized by the functional chemistry, crystallinity, morphology, mechanical, thermal, and barrier properties. The Fourier transform infrared spectroscopy analysis confirmed the presence of intermolecular hydrogen bonding between the thymol and starch, as well as the thymol and glycerol, via hydroxyl groups of glycerol, starch, and thymol. The film crystallinity decreased with increasing concentration of thymol. The addition of NCF at 1.5% weight of starch increased the tensile strength (TS) and Young's Modulus (YM), but decreased the elongation at break (EAB), oxygen permeability, and water vapor permeability of the CS films. The thermal stability of the CS films was also improved with the addition of NCF. The addition of thymol to the CS/NCF bio-nanocomposite films decreased the TS and YM, respectively but increased the EAB due to the plasticizing effect of thymol. The addition of thymol also improved the thermal stability but reduced the barrier properties of the films. The effects on the mechanical, thermal, and barrier properties were more pronounced at higher concentrations of thymol. In conclusion, the inclusion of both NCF and thymol led to the improvement of the flexibility and thermal stability of the CS films

Orthopaedic specialty committee exit examination amidst the COVID-19 pandemic in Malaysia- experiences and reflections from the candidates

Introduction: The emergence of the COVID-19 pandemic had affected the Orthopaedic Specialty Committee (OSC) Exit Examination candidates. The objective of this study was to evaluate the impact of this pandemic on the candidates’ teaching and learning, mental well-being, and personal experience during the examinations. Methods: A cross-sectional study was conducted from 1st to 31st January 2021. 103 candidates for the OSC Exit Examination November 2020 were asked to answer a questionnaire. Wilcoxon signed-rank tests were used to compare differences in the frequencies before and during the pandemic. A p-value of less than 0.05 was taken as significant. Results: There was a significant reduction in the number of classes (P-value < 0.001) and examination preparatory courses conducted, reduced number and variety of patients attended and limited exposure in the operation theatre. Most candidates had virtual and physical classes, and agreed virtual clinical teaching was less effective. A majority had increased caffeine intake and smoking habits, decreased time spent with family and sports activities and no impact on sleeping hours, alcohol and analgesic usage. During the examinations, most candidates felt disturbed by the COVID-19 safety protocol and worried about the risk of contracting the infections. Conclusion: The effect of this pandemic on the post-graduate Orthopaedics students teaching and learning is massive. Virtual teaching programmes or applications that can replace the traditional clinical teaching methods should be explored and developed for the benefit of our education system

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Investigation of morphology and compressive properties of diamond reinforced porous aluminium composites

In the present work, porous aluminium composite with varying diamond particles content (4, 8, 12, and 16 wt. %) were developed via powder metallurgy technique. Porosity was attained by using 30 wt. % Polymethylmethacrylate particles as a space holder. The effects of varying content of diamond on the morphology, densities, porosities, compressive properties as well as energy absorption were studied. Morphology of the porous Al composite demonstrated the formation of closed- cell macro pores that were uniformly distributed within the Al matrix regardless of different content of diamond particles. However, increasing diamond content was found to alleviate un-wetting phenomenon between Al matrix and diamond particles leading to increased porosities from 34.8% to 26.2%. The compressive properties also declined however maximum values for plateau stress (7.50MPa) and energy absorption capacity 1.7(Mj/m3) were acquired at 8wt.% diamond content

Symmetrically Stacked Long Short-Term Memory Networks for Fall Event Recognition Using Compact Convolutional Neural Networks-Based Tracker

In recent years, the advancement of pattern recognition algorithms, specifically the deep learning-related techniques, have propelled a tremendous amount of researches in fall event recognition systems. It is important to detect a fall incident as early as possible, whereby a slight delay in providing immediate assistance can cause severe unrecoverable injuries. One of the main challenges in fall event recognition is the imbalanced training data between fall and no-fall events, where a real-life fall incident is a sporadic event that occurs infrequently. Most of the recent techniques produce a lot of false alarms, as it is hard to train them to cover a wide range of fall situations. Hence, this paper aims to detect the exact fall frame in a video sequence, as such it will not be dependent on the whole clip of the video sequence. Our proposed approach consists of a two-stage module where the first stage employs a compact convolutional neural network tracker to generate the object trajectory information. Features of interest will be sampled from the generated trajectory paths, which will be fed as the input to the second stage. The next stage network then models the temporal dependencies of the trajectory information using symmetrical Long Short-Term Memory (LSTM) architecture. This two-stage module is a novel approach as most of the techniques rely on the detection module rather than the tracking module. The simulation experiments were tested using Fall Detection Dataset (FDD). The proposed approach obtains an expected average overlap of 0.167, which is the best performance compared to Multi-Domain Network (MDNET) and Tree-structured Convolutional Neural Network (TCNN) trackers. Furthermore, the proposed 3-layers of stacked LSTM architecture also performs the best compared to the vanilla recurrent neural network and single-layer LSTM. This approach can be further improved if the tracker model is firstly pre-tuned in offline mode with respect to a specific type of object of interest, rather than a general object

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively