Manual demolition tasks are heavy, physically demanding tasks that could cause muscle fatigue accumulation and lead to work-related musculoskeletal disorders (WMSDs). Fatigue and recovery models of muscles are essential in understanding the accumulation and the reduction in muscle fatigue for forceful exertion tasks. This study aims to explore the onset of muscle fatigue under different work/rest arrangements during manual demolition tasks and the offset of fatigue over time after the tasks were performed. An experiment, including a muscle fatigue test and a muscle fatigue recovery test, was performed. Seventeen male adults without experience in demolition hammer operation were recruited as human participants. Two demolition hammers (large and small) were adopted. The push force was either 20 or 40 N. The posture mimicked that of a demolition task on a wall. In the muscle fatigue test, the muscle strength (MS) before and after the demolition task, maximum endurance time (MET), and the Borg category-ratio-10 (CR-10) ratings of perceived exertion after the demolition task were measured. In the muscle fatigue recovery test, MS and CR-10 at times 1, 2, 3, 4, 5, and 6 min were recorded. Statistical analyses were performed to explore the influence of push force and the weight of the tool on MS, MET, and CR-10. Both muscle fatigue models and muscle fatigue recovery models were established and validated. The results showed that push force affected MET significantly (p < 0.05). The weight of the tool was significant (p < 0.05) only on the CR-10 rating after the first pull. During the muscle fatigue recovery test, the MS increase and the CR-10 decrease were both significant (p < 0.05) after one or more breaks. Models of MET and MS prediction were established to assess muscle fatigue recovery, respectively. The absolute (AD) and relative (RD) deviations of the MET model were 1.83 (±1.94) min and 34.80 (±31.48)%, respectively. The AD and RD of the MS model were 1.39 (±0.81) N and 1.9 (±1.2)%, respectively. These models are capable of predicting the progress and recovery of muscle fatigue, respectively, and may be adopted in work/rest arrangements for novice workers performing demolition tasks.

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A gait experiment was performed. The participants were tested under shoes, floors, surface and lighting conditions. They gave floor slipperiness ratings before and after a gait trial. The perceived sense of slip (PSOS) was collected. It was found that the perceived floor slipperiness (PFS) before walking was affected significantly by the lighting, floor and surface conditions. Relative low PFS values were recorded under wet and detergent-contaminated conditions in the normal daylight condition as compared with those in the dimmed condition. The PFS after the gait was significantly affected by the floor and surface conditions. The PSOS was highly correlated with the PFS after trial. The regression analyses results indicated that both the coefficient of friction (COF) of the floor and lighting were primary predictors of the PFS before a gait. The COF and walking speed were the primary predictors of the PFS after a gait.

A field study was conducted to investigate the sensitivity of human participants in detecting the invasion of a drone in the airspace. A Phantom 4 quadcopter was remotely controlled to hovering at air locations inside or outside of a stadium. Twenty participants were requested to determine whether the drone has invaded in the test field or not on a five‐point scale. The participants also responded whether they have heard the sound of the drone. The nonparametric measures of the sensitivity of drone invasion detection, or P(A), were calculated. The results indicated that the distance between the drone and the boundary of the airspace significantly affected the P(A) while the effects of drone altitude were not significant. The participants were not unbiased detectors. They tended to respond “probably yes,” in general, when they spotted a drone near the airspace. The hearing of the sound of the drone provided partial cues in drone invasion detection.

Pulling is one of the manual material handling activities that could lead to work-related musculoskeletal disorders. The objectives of this study were to explore the development of muscular fatigue when performing intermittent pulling tasks and to establish models to predict the pull strength decrease due to performing the tasks. A simulated truck pulling experiment was conducted. Eleven healthy male adults participated. The participants pulled a handle with a load of 40 kg, which resulted in a pulling force of approximately 123 N. The pulling tasks lasted for 9 or 12 min with one, two, or three pauses embedded. The total time period of the embedded pauses was 3 min. The pull strength after each pull and rest was measured. Ratings of the perceived exertion on body parts after each pull were also recorded. The results showed insignificant differences regarding the development of muscular fatigue related to rest frequency. We found that the development of muscular fatigue for pulling tasks with embedded pauses was significantly slower than that for continuous pulls. The forearm had a higher CR-10 score than the other body parts indicating that the forearm was the body part suffering early muscle fatigue. An exponential model was developed to predict the pull strength of the pulling tasks with embedded pauses. This model may be used to assess the developing of muscular fatigue for pulling tasks.

Gloves are used at workplaces to protect hands and fingers from potential hazards. Three types of work gloves were assessed in terms of the strength of grip, carrying, and lifting. Thirty adults (14 males and 16 females) joined as human participants. The strength data were measured under bare hand and three gloved conditions. The grip spans in the grip strength measurements included 45 mm, 55 mm, 65 mm, and 75 mm. The carrying strength was measured for both dominant and non-dominant hands under leg straight and semi-squat postural conditions. The lifting strength was measured at a semi-squat posture. The results showed that glove (p < 0.0001), grip span (p = 0.001), gender (p < 0.0001), and handedness (p < 0.0001) all affected grip strength significantly. Wearing the gloves tested in this study led to a decrease of grip strength up to 22.9%, on average, depending on gender, grip span, and hand tested. Wearing the cotton gloves led to a decrease of one-handed carrying strength ranged from 3.5% to 9.7% for female participants. All the participants took advantages in carrying strength when wearing the cut-resistant gloves. The leg lifting strength data indicated that the effects of the gloves were insignificant. The information of this study is beneficial for practitioners in the design of manual materials handling tasks concerning the use of work gloves.

In the hope of reducing the air supply flow of the powered air-purifying respirator (PAPR) and extending the service life of the filter, a breath-following powered air-purifying respirator (BF-PAPR) that can dynamically adjust the air supply flow according to the breathing flow is proposed. The BF-PAPR changes the air supply flow by adjusting the speed of the variable-frequency centrifugal fan according to the air velocity at the half mask outlet (vhm) monitored by the modular wind speed transmitter. In the study, the air supply flow adjustment model of the BF-PAPR is developed. It is found that the filtration resistance barely influences vhm. In addition, under the same mean inhalation flow, the minimum outlet air velocity increases first and then decreases with the increase of the duty cycle variation coefficient (λ), while the maximum outlet air velocity decreases first and then increases. Moreover, the minimum air supply flow of the BF-PAPR is achieved when the standard value of the air velocity is 13.4 m/s and the value of λ is 1. The BF-PAPR can reduce the air supply flow by 6.5%-8.6% and the energy consumption by approximately 20% compared with the PAPR, which is beneficial for reducing the usage cost and extending the continuous working time.

Human–virtual target interactions are becoming more and more common due to the emergence and application of augmented reality (AR) devices. They are different from interacting with real objects. Quantification of movement time (MT) for human–virtual target interactions is essential for AR-based interface/environment design. This study aims to investigate the motion time when people interact with virtual targets and to compare the differences in motion time between real and AR environments. An experiment was conducted to measure the MT of pointing tasks on the basis of both a physical and a virtual calculator panel. A total of 30 healthy adults, 15 male and 15 female, joined. Each participant performed pointing tasks on both physical and virtual panels with an inclined angle of the panel, hand movement direction, target key, and handedness conditions. The participants wore an AR head piece (Microsoft Hololens 2) when they pointed on the virtual panel. When pointing on the physical panel, the participants pointed on a panel drawn on board. The results showed that the type of panel, inclined angle, gender, and handedness had significant (p < 0.0001) effects on the MT. A new finding of this study was that the MT of the pointing task on the virtual panel was significantly (p < 0.0001) higher than that of the physical one. Users using a Hololens 2 AR device had inferior performance in pointing tasks than on a physical panel. A revised Fitts’s model was proposed to incorporate both the physical–virtual component and inclined angle of the panel in estimating the MT. This model is novel. The index of difficulty and throughput of the pointing tasks between using the physical and virtual panels were compared and discussed. The information in this paper is beneficial to AR designers in promoting the usability of their designs so as to improve the user experience of their products.