Abstract: A prevention and safety management system utilizes a non-intrusive imaging sensor (e.g. surveillance cameras, smartphone cameras) and a computer vision system to record videos of workers not wearing sensors. The videos are analyzed using a deep machine learning algorithm to detect kinematic activities (set of predetermined body joint positions and angles) of the workers and recognizing various physical activities (walk/posture, lift, push, pull, reach, force, repetition, duration etc.). The measured kinematic variables are then parsed into metrics relevant to workplace ergonomics, such as number of repetitions, total distance travelled, range of motion, and the proportion of time in different posture categories. The information gathered by this system is fed into an ergonomic assessment system and is used to automatically populate exposure assessment tools and create risk assessments.

Abstract: A prevention and safety management system utilizes a non-intrusive imaging sensor (e.g. surveillance cameras, smartphone cameras) and a computer vision system to record videos of workers not wearing sensors. The videos are analyzed using a deep machine learning algorithm to detect kinematic activities (set of predetermined body joint positions and angles) of the workers and recognizing various physical activities (walk/posture, lift, push, pull, reach, force, repetition, duration etc.). The measured kinematic variables are then parsed into metrics relevant to workplace ergonomics, such as number of repetitions, total distance travelled, range of motion, and the proportion of time in different posture categories. The information gathered by this system is fed into an ergonomic assessment system and is used to automatically populate exposure assessment tools and create risk assessments.

Abstract: A prevention and safety management system utilizes a non-intrusive imaging sensor (e.g. surveillance cameras, smartphone cameras) and a computer vision system to record videos of workers not wearing sensors. The videos are analyzed using a deep machine learning algorithm to detect kinematic activities (set of predetermined body joint positions and angles) of the workers and recognizing various physical activities (walk/posture, lift, push, pull, reach, force, repetition, duration etc.). The measured kinematic variables are then parsed into metrics relevant to workplace ergonomics, such as number of repetitions, total distance travelled, range of motion, and the proportion of time in different posture categories. The information gathered by this system is fed into an ergonomic assessment system and is used to automatically populate exposure assessment tools and create risk assessments.

Abstract: A prevention and safety management system utilizes a non-intrusive imaging sensor (e.g. surveillance cameras, smartphone cameras) and a computer vision system to record videos of workers not wearing sensors. The videos are analyzed using a deep machine learning algorithm to detect kinematic activities (set of predetermined body joint positions and angles) of the workers and recognizing various physical activities (walk/posture, lift, push, pull, reach, force, repetition, duration etc.). The measured kinematic variables are then parsed into metrics relevant to workplace ergonomics, such as number of repetitions, total distance travelled, range of motion, and the proportion of time in different posture categories. The information gathered by this system is fed into an ergonomic assessment system and is used to automatically populate exposure assessment tools and create risk assessments.

Abstract: An improved hand tool is disclosed having an elongated handle member (40) coupled to a tool head (20) and includes a junction (60) between the handle member and a surface of the tool head that defines an elongated interface. A weld (36) is provided along substantially all of the elongated interface so that the tool head is rigidly attached to a lower end of the handle member. A gripping member (80) is coupled to an upper end of the handle member and has a top portion (88) with an arcuate profile coupled to a bottom portion (90) having a substantially U-shaped profile. The tool head includes a blade member formed of a material having a first thickness and integrally formed with a step member having a second thickness greater than the first thickness.