Human Body Posture Recognition Approaches: A Review

Human body posture recognition has become the focus of many researchers in recent years. Recognition of body posture is used in various applications, including surveillance, security, and health monitoring. However, these systems that determine the body’s posture through video clips, images, or data from sensors have many challenges when used in the real world. This paper provides an important review of how most essential ‎ hardware technologies are ‎used in posture recognition systems‎. These systems capture and collect datasets through ‎accelerometer sensors or computer vision. In addition, this paper presents a comparison ‎study with state-of-the-art in terms of accuracy. We also present the advantages and ‎limitations of each system and suggest promising future ideas that can increase the ‎efficiency of the existing posture recognition system. Finally, the most common datasets ‎applied in these systems are described in detail. It aims to be a resource to help choose one of the methods in recognizing the posture of the human body and the techniques that suit each method. It analyzes more than 80 papers between 2015 and 202

Sensorized garments developed for remote postural and motor rehabilitation

Every day, all around the world, millions of people request postural and/or motor rehabilitation. The rehabilitation process, also known as Tertiary Prevention, intends to be a sort of therapy to restore functionality and self-sufficiency of the patient, and regards not only millions of patients daily, but involves also a huge number of professionals in medical staffs, i.e. specialists, nurses, physiotherapists and therapists, social workers, psychologists, physiatrists. The care is given in hospitals, clinics, geriatric facilities, and with territorial home care. For the large number of patients as well as the medical staff and facilities necessary to support the appropriate postural and motor training, the monetary costs of rehabilitation is so large, it is difficult to estimate. So, every effort towards a simplification of the rehabilitation route is desirable and welcome, and this chapter covers this aspect

Lessons Learned: Solutions for Workplace Safety and Health

Provides case studies of workplace health hazards, regulatory actions taken, and solutions, including product and design alternatives; a synthesis of findings and lessons learned; and federal- and state-level recommendations

A case study of an ergonomic evaluation for a shop floor facility

The aim of this paper is to discuss and recommend solutions for the ergonomic hazards present in a shop floor type of manufacturing facility. This type of study is important since the ergonomic issues that concern the shop floor worker are different than those faced by the assembly line worker. The shop floor employee for the most part enjoys work satisfaction, task variety, and is able to control his own work pace. From an ergonomic standpoint, this is the preferred work environment. The focus of this paper is a case study. This study is a one-day ergonomic assessment of a plastic manufacturing facility located in New Jersey. The ergonomic hazards found in this facility provided valuable information for developing guidelines that can be applied in most shop floor facilities. Among these guidelines is the implementation of a program that includes joint participation from management and workers for hazard evaluation

Perception and manipulation for robot-assisted dressing

Assistive robots have the potential to provide tremendous support for disabled and elderly people in their daily dressing activities. This thesis presents a series of perception and manipulation algorithms for robot-assisted dressing, including: garment perception and grasping prior to robot-assisted dressing, real-time user posture tracking during robot-assisted dressing for (simulated) impaired users with limited upper-body movement capability, and finally a pipeline for robot-assisted dressing for (simulated) paralyzed users who have lost the ability to move their limbs. First, the thesis explores learning suitable grasping points on a garment prior to robot-assisted dressing. Robots should be endowed with the ability to autonomously recognize the garment state, grasp and hand the garment to the user and subsequently complete the dressing process. This is addressed by introducing a supervised deep neural network to locate grasping points. To reduce the amount of real data required, which is costly to collect, the power of simulation is leveraged to produce large amounts of labeled data. Unexpected user movements should be taken into account during dressing when planning robot dressing trajectories. Tracking such user movements with vision sensors is challenging due to severe visual occlusions created by the robot and clothes. A probabilistic real-time tracking method is proposed using Bayesian networks in latent spaces, which fuses multi-modal sensor information. The latent spaces are created before dressing by modeling the user movements, taking the user's movement limitations and preferences into account. The tracking method is then combined with hierarchical multi-task control to minimize the force between the user and the robot. The proposed method enables the Baxter robot to provide personalized dressing assistance for users with (simulated) upper-body impairments. Finally, a pipeline for dressing (simulated) paralyzed patients using a mobile dual-armed robot is presented. The robot grasps a hospital gown naturally hung on a rail, and moves around the bed to finish the upper-body dressing of a hospital training manikin. To further improve simulations for garment grasping, this thesis proposes to update more realistic physical properties values for the simulated garment. This is achieved by measuring physical similarity in the latent space using contrastive loss, which maps physically similar examples to nearby points.Open Acces

"Production Ergonomics

"Production ergonomics – the science and practice of designing industrial workplaces to optimize human well-being and system performance – is a complex challenge for a designer. Humans are a valuable and flexible resource in any system of creation, and as long as they stay healthy, alert and motivated, they perform well and also become more competent over time, which increases their value as a resource. However, if a system designer is not mindful or aware of the many threats to health and system performance that may emerge, the end result may include inefficiency, productivity losses, low working morale, injuries and sick-leave. To help budding system designers and production engineers tackle these design challenges holistically, this book offers a multi-faceted orientation in the prerequisites for healthy and effective human work. We will cover physical, cognitive and organizational aspects of ergonomics, and provide both the individual human perspective and that of groups and populations, ending up with a look at global challenges that require workplaces to become more socially and economically sustainable. This book is written to give you a warm welcome to the subject, and to provide a solid foundation for improving industrial workplaces to attract and retain healthy and productive staff in the long run.

Microanalysis of nonverbal communication: Development of a nonverbal research method using high-performance 3D character animation

This work provides a novel research tool for the field of nonverbal communication, with the goal being to transform 3D motion data into metric measurements that allow for the application of standard statistical methods such as analysis of variance, factor analysis, or multiple regression analysis. 3D motion data are automatically captured by motion capture systems or manually coded by humans using 3D character animation software. They precisely describe human movements, but without any furter data processing, they cannot meaningfully be interpreted and statistically analyzed. To make this possible, three nonverbal coding systems describing static body postures, dynamic body movements, and proper body part motions such as head nods have been developed. A geometrical model describing postures and movements as flexion angles of body parts on three clearly understandable and nonverbal relevant dimensions—the sagittal, the rotational, and the lateral—has been developed and provides the basis for math formulas which allow the transformation of motion capture data or 3D animation data into metric measures. Furthermore, math formulas were developed to compute around 30 nonverbal cues described in the literature on kinesics that can be understood as geometrical features of body parts such as openness, symmetry, and expansiveness of body postures, head position and head nods, gaze direction and body orientation, pointing behavior and relational gestures, interactional synchrony, proxemics, and touch, including dynamic features of movements such as rate, velocity, and acceleration. To obtain accurate measurements, the software APEx (Automatic Parameter Extraction) has been developed with a number of convenient features extracting more than 150 nonverbal parameters consisting 380 metric variables out of available motion data

Computer vision based techniques for fall detection with application towards assisted living

In this thesis, new computer vision based techniques are proposed to detect falls of an elderly person living alone. This is an important problem in assisted living. Different types of information extracted from video recordings are exploited for fall detection using both analytical and machine learning techniques. Initially, a particle filter is used to extract a 2D cue, head velocity, to determine a likely fall event. The human body region is then extracted with a modern background subtraction algorithm. Ellipse fitting is used to represent this shape and its orientation angle is employed for fall detection. An analytical method is used by setting proper thresholds against which the head velocity and orientation angle are compared for fall discrimination. Movement amplitude is then integrated into the fall detector to reduce false alarms. Since 2D features can generate false alarms and are not invariant to different directions, more robust 3D features are next extracted from a 3D person representation formed from video measurements from multiple calibrated cameras. Instead of using thresholds, different data fitting methods are applied to construct models corresponding to fall activities. These are then used to distinguish falls and non-falls. In the final works, two practical fall detection schemes which use only one un-calibrated camera are tested in a real home environment. These approaches are based on 2D features which describe human body posture. These extracted features are then applied to construct either a supervised method for posture classification or an unsupervised method for abnormal posture detection. Certain rules which are set according to the characteristics of fall activities are lastly used to build robust fall detection methods. Extensive evaluation studies are included to confirm the efficiency of the schemes