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Industrial environments present unique challenges in ensuring worker safety and optimizing productivity. The emergence of smart wearable technologies such as smart insoles has provided new opportunities to address these challenges through accurate unobtrusive monitoring and analysis of workers' activities and physical parameters. This systematic review aims to analyze the utilization of smart wearable insoles in industrial environments, focusing on their applications, employed analysis methods, and potential future directions. A comprehensive review was conducted, involving the analysis of 27 papers that utilized smart wearable insoles in industrial settings. The reviewed articles were evaluated to determine the trends in application and methodology, explore the implementation of smart insoles across different industries, and identify the prevalent machine learning models and analyzed activities in the relevant literature. The majority of the reviewed articles (67%) primarily focused on human activity recognition and gesture estimation using smart wearable insoles, aiming to enhance safety and productivity in industrial settings. Furthermore, 10% of the studies focused on fatigue identification, 10% on slip, trip, and fall hazard detection, and 13% on biomechanical analyses of workers' body joint loads. The construction industry accounted for approximately 60% of the studies conducted in industrial settings using smart insoles. The most prevalent machine learning models utilized in these studies were neural networks (48%), support vector machines (33%), k-nearest neighbors (30%), decision trees (26%), and random forests (15%). These models achieved median accuracies of 95%, 96%, 91%, 92%, and 95%, respectively. Among the analyzed activities, walking, bending with/without lifting/lowering a load, and carrying a load were the most frequently considered, with frequencies of 10, 10, and 7 out of the 27 studies, respectively. The findings of this systematic review demonstrate the growing interest in implementing smart wearable insoles in industrial environments to enhance safety and productivity. However, the effectiveness of these systems is dependent on factors such as accuracy, reliability, and generalizability of the models. The review highlights the need for further research to address these challenges and to explore the potential of these systems for use in other industrial applications such as manufacturing. Overall, this systematic review provides valuable insights for researchers, practitioners, and policymakers in the field of occupational health and safety. [ABSTRACT FROM AUTHOR]

This study developed and validated a lumped parameter model for the FLEXI-BAR, a popular training instrument that provides vibration stimulation. The model which can be used in conjunction with musculoskeletal-modeling software for quantitative biomechanical analyses, consists of 3 rigid segments, 2 torsional springs, and 2 torsional dashpots. Two different sets of experiments were conducted to determine the model's key parameters including the stiffness of the springs and the damping ratio of the dashpots. In the first set of experiments, the free vibration of the FLEXI-BAR with an initial displacement at its end was considered, while in the second set, forced oscillations of the bar were studied. The properties of the mechanical elements in the lumped parameter model were derived utilizing a non-linear optimization algorithm which minimized the difference between the model's prediction and the experimental data. The results showed that the model is valid (8% error) and can be used for simulating exercises with the FLEXI-BAR for excitations in the range of the natural frequency. The model was then validated in combination with AnyBody musculoskeletal modeling software, where various lumbar disc, spinal muscles and hand muscles forces were determined during different FLEXI-BAR exercise simulations. [ABSTRACT FROM AUTHOR]

As autonomous mobile robots (AMR) are introduced into workspace environments shared with people, effective human-robot communication is critical to the prevention of injury while maintaining a high level of productivity. This research presents an empirical study that evaluates four alternative methods for communicating between an autonomous mobile robot and a human at a warehouse intersection. The results demonstrate that using an intent communication system for human-AMR interaction improves objective measures of productivity (task time) and subjective metrics of trust and comfort. • Investigation of intent communication systems (ICS) for human-robot interaction. • Results demonstrate that an ICS improves measures of warehouse productivity and trust. • Study emulates the interaction of humans and forklifts at a warehouse intersection. • Four alternative ICS methods include text, symbol, and avatar displays, and no ICS. • A humanoid avatar ICS was ranked highest in terms of participant preference. [ABSTRACT FROM AUTHOR]

Slips, trips, and falls are some of the most substantial and prevalent causes of occupational injuries and fatalities, and these events may contribute to low-back problems. We quantified lumbar kinematics (i.e., lumbar angles relative to pelvis) and kinetics during unexpected slip and trip perturbations, and during normal walking, among 12 participants (6F, 6 M). Individual anthropometry, lumbar muscle geometry, and lumbar angles, along with electromyography from 14 lumbar muscles were used as input to a 3D, dynamic, EMG-based model of the lumbar spine. Results indicated that, in comparison with values during normal walking, lumbar range of motion, lumbosacral (L5/S1) loads, and lumbar muscle activations were all significantly higher during the slip and trip events. Maximum L5/S1 compression forces exceeded 2700 N during slip and trip events, compared with ∼ 1100 N during normal walking. Mean values of L5/S1 anteroposterior (930 N), and lateral (800 N) shear forces were also substantially larger than the shear force during the normal walking (230 N). These observed levels of L5/S1 reaction forces, along with high levels of bilateral lumbar muscle activities, suggest the potential for overexertion injuries and tissue damage during unexpected slip and trip events, which could contribute to low back injuries. Outcomes of this study may facilitate the identification and control of specific mechanisms involved with low back disorders consequent to slips or trips. [ABSTRACT FROM AUTHOR]

Using traditional back tables (BT) in operating rooms (OR) can lead to high physical/cognitive demand on nurses due to repetitive manual material handling activities. A multi-tier table (MTT) has been developed to relieve such stressors by providing extra working surfaces to avoid stacking the instrument trays and facilitate access to surgical tools. In this study, sixteen participants performed lifting/lowering and instrument findings tasks on each table, where kinematics, kinetics, subjective, and performance-related measures were recorded. Results indicated that MTT required lesser shoulder flexion (p-value<0.001), ∼14% lower shoulder loads (0.012), task completion time (<0.001), and cognitive/physical workloads (<0.004). Although peak low-back demands were ∼15% higher using MTT, the number of lifts to complete the same task was 60% lower, leading to lower cumulative demand on the low-back musculature. Utilizing MTT in OR could reduce demand and increase nurses' efficiency, leading to reduced risk of WMSDs and the total time of surgery. [ABSTRACT FROM AUTHOR]