INDUSTRIAL SAFETY USING AUGMENTED REALITY AND ARTIFICIAL INTELLIGENCE

Industrialization brought benefits to the development of societies, albeit at the cost of the safety of industrial workers. Industrial operators were often severely injured or lost their lives during the working process. The causes can be cuts or lacerations resulting from moving machine parts, burns or scalds resulting from touch, or mishandling of thermal, electrical, and chemical objects. Fatigue, distraction, or inattention can exacerbate the risk of industrial accidents. The accidents can cause service downtime of manufacturing machinery, leading to lower productivity and significant financial losses. Therefore, regulations and safety measures were formulated and overseen by the government and local authorities. Safety measures include effective training of workers, an inspection of the workplace, safety rules, safeguarding, and safety warning systems. For instance, safeguarding prevents contact with hazardous moving parts by isolating or stopping them, whereas a safety warning system detects accident risks and issues an alert warning. Warning systems were mostly mounted detection sensors and alerting systems. Mobile alerting devices can be gadgets such as phones, tablets, smartwatches, or smart glasses. Smart goggles can be utilized for industrial safety to protect, detect, and warn about potential risks. Adopting new technologies such as augmented reality and artificial intelligence can enhance the safety of workers in the industry. Augmented reality systems developed for head-mounted displays can extend workers’ perception of the environment. Artificial intelligence utilizing state-of-the-art sensors can improve industrial safety by making workers aware of potential hazards in the environment. For instance, thermal or infrared sensors can detect hot objects in the workplace. Built-in infrared sensors in smart glasses can detect the state of attention of users. Using smart glasses, potential hazards can be conveyed to industrial workers using various modalities, such as audial, visual, or tactile. We have successfully developed advanced safety systems for industrial workers. Our innovative approach incorporates cutting-edge technologies such as eye tracking, spatial mapping, and thermal imaging. By utilizing eye tracking, we are able to identify instances of user inattention, while spatial mapping allows us to analyze the user’s behavior and surroundings. Furthermore, the integration of thermal imaging enables us to detect hot objects within the user’s field of view. The first system we developed is a warning system that harnesses the power of augmented reality and artificial intelligence. This system effectively issues alerts and presents holographic warnings to combat instances of inattention or distraction. By utilizing visual cues and immersive technology, we aim to proactively prevent accidents and promote worker safety. The second safety system we designed involves the integration of a third-party thermal imaging system into smart glasses. Through this integration, our safety system overlays false-color holograms onto hot objects, enabling workers to easily identify and avoid potential hazards. To evaluate the effectiveness of our systems, we conducted comprehensive experiments with human participants. These experiments involved both qualitative and quantitative measurements, and we further conducted semi-structured interviews with the participants to gather their insights. The results and subsequent discussions from our experiments have provided valuable insights for the future implementation of safety systems. Through this research, we envision the continued advancement and refinement of safety technologies to further enhance worker safety in industrial settings

A survey on wireless body area networks for eHealthcare systems in residential environments

The progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to the base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 129, June 1974

This special bibliography lists 280 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1974

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

Cataloged from PDF version of article.Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded

Lateral Inhibition in Accumulative Computation and Fuzzy Sets for Human Fall Pattern Recognition in Colour and Infrared Imagery

Fall detection is an emergent problem in pattern recognition. In this paper, a novel approach which enables to identify a type of a fall and reconstruct its characteristics is presented. The features detected include the position previous to a fall, the direction and velocity of a fall, and the postfall inactivity. Video sequences containing a possible fall are analysed image by image using the lateral inhibition in accumulative computation method. With this aim, the region of interest of human figures is examined in each image, and geometrical and kinematic characteristics for the sequence are calculated. The approach is valid in colour and in infrared video