Trunk sway changes in professional bus drivers during actual shifts on long-distance routes

Arippa, Federico; Leban, Bruno; Fadda, Paolo; Fancello, Gianfranco

doi:10.1080/00140139.2021.1991002

Cited by 8 publications

(6 citation statements)

References 71 publications

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“…However, no cause-effect relationship can be inferred from these case-control studies. To obtain information on mechanistic relationships between specific occupational workloads and LBP, previous studies have examined trunk mechanical and neuromuscular characteristics before and after work shifts [ 18 , 19 ] or simulations thereof [ 20 , 21 ]. Of particular interest was prolonged work in a flexed posture, such as seen in crane operators [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

The Effects of Isometric Fatigue on Trunk Muscle Stiffness: Implications for Shear-Wave Elastography Measurements

Vatovec

Kozinc

Voglar

2022

Sensors

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Muscle stiffness has been implicated as a possible factor in low back pain risk. There are few studies on the effects of isometric fatigue on the shear modulus of trunk muscles. This study aimed to investigate the effects of trunk isometric fatigue on the passive and active (during low and high-level contractions) shear moduli of the erector spinae (ES) and superficial and deep multifidus (MF) muscles. We assessed passive and active shear modulus using shear-wave elastography in healthy young participants (n = 22; 11 males, 11 females), before and after an isometric trunk extension fatigue protocol. Maximal voluntary force decreased from 771.2 ± 249.8 N before fatigue to 707.3 ± 204.1 N after fatigue (−8.64%; p = 0.003). Passive shear modulus was significantly decreased after fatigue in the MF muscle (p = 0.006–0.022; Cohen’s d = 0.40–46), but not the ES muscle (p = 0.867). Active shear modulus during low-level contraction was not affected by fatigue (p = 0.697–0.701), while it was decreased during high-level contraction for both muscles (p = 0.011; d = 0.29–0.34). Sex-specific analysis indicated the decrease in ES shear modulus was significant in males (p = 0.015; d = 0.31), but not in females (p = 0.140). Conversely, the shear modulus in superficial MF had a statistically significant decrease in females (p = 0.002; d = 0.74) but not in males (p = 0.368). These results have important implications for further investigations of the mechanistic interaction between physical workloads, sex, muscle stiffness (and other variables affecting trunk stability and neuromuscular control), and the development/persistence of low back pain.

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Section: Introductionmentioning

confidence: 99%

The Effects of Isometric Fatigue on Trunk Muscle Stiffness: Implications for Shear-Wave Elastography Measurements

Vatovec

Kozinc

Voglar

2022

Sensors

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“…Among the different variables that can be measured as indicators of ergonomic performance and biomechanical risk when standing, trunk oscillations, which directly reflect information about postural sway (Reynard et al, 2019 ) represent valuable variables that can be associated with fatigue emergence, and, as a result, risk increases (Dupuis et al, 2021 ). Postural sway has been frequently used for the characterization of ergonomics (Padula and Coury, 2003 ; Leban et al, 2017 ; Arippa et al, 2022 ). Stability and regularity of trunk oscillations is of key importance for the characterization of the motor performance during a biomechanical task (Granata and England, 2006 ); following this perspective, different methodological approaches, such as the extraction of the maximum Lyapunov exponents (Granata and England, 2006 ) or the computation of the mutual information (Anagnostou, 2022 ) have been introduced to capture stability- or regularity-related indicators from trunk movements or from the Center of Pressure (CoP) displacement; given the rather complex nature of the mechanisms of controlling balance while standing, which involves nonlinear effects.…”

Section: Introductionmentioning

confidence: 99%

Modalities of sequential human robot collaboration trigger different modifications of trunk oscillations

et al. 2023

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IntroductionHuman robot collaboration is quickly gaining importance in the robotics and ergonomics fields due to its ability to reduce biomechanical risk on the human operator while increasing task efficiency. The performance of the collaboration is typically managed by the introduction of complex algorithms in the robot control schemes to ensure optimality of its behavior; however, a set of tools for characterizing the response of the human operator to the movement of the robot has yet to be developed.MethodsTrunk acceleration was measured and used to define descriptive metrics during various human robot collaboration strategies. Recurrence quantification analysis was used to build a compact description of trunk oscillations.Results and discussionThe results show that a thorough description can be easily developed using such methods; moreover, the obtained values highlight that, when designing strategies for human robot collaboration, ensuring that the subject maintains control of the rhythm of the task allows to maximize comfort in task execution, without affecting efficiency.

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“…Previous studies [16,17,20,21] reported that it is theoretically possible to estimate driver distraction and fatigue levels by measuring body pressure distribution to detect driver posture and behavior. Driver posture was reported to be correlated with driving comfort, and a decrease in comfort could result in driver distraction [17].…”

Section: Introductionmentioning

confidence: 99%

“…Drivers were found to frequently re-sit or tilt back and forth when drowsiness and fatigue levels increased [22,23]. However, current pressure sensors include a combination of electrodes on a flexible printed circuit board (PCB) or other plastic, rubber, or fabric substrate for detecting capacitance change under pressure [21,23,24]. This type of pressure sensor is stiffer than urethane foam and cannot deform to follow the shape of the human hip, giving the user a sense of discomfort when sitting.…”

Section: Introductionmentioning

confidence: 99%

Urethane-Foam-Embedded Silicon Pressure Sensors including Stress-Concentration Packaging Structure for Driver Posture Monitoring

Takamatsu

Sato

Itoh

2022

Sensors

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We propose urethane-foam-embedded silicon pressure sensors, including a stress-concentration packaging structure, for integration into a car seat to monitor the driver’s cognitive state, posture, and driving behavior. The technical challenges of embedding silicon pressure sensors in urethane foam are low sensitivity due to stress dispersion of the urethane foam and non-linear sensor response caused by the non-uniform deformation of the foam. Thus, the proposed package structure includes a cover to concentrate the force applied over the urethane foam and frame to eliminate this non-linear stress because the outer edge of the cover receives large non-linear stress concentration caused by the geometric non-linearity of the uneven height of the sensor package and ground substrate. With this package structure, the pressure sensitivity of the sensors ranges from 0 to 10 kPa. The sensors also have high linearity with a root mean squared error of 0.049 N in the linear regression of the relationship between applied pressure and sensor output, and the optimal frame width is more than 2 mm. Finally, a prototype 3 × 3 sensor array included in the proposed package structure detects body movements, which will enable the development of sensor-integrated car seats.

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Trunk sway changes in professional bus drivers during actual shifts on long-distance routes

Cited by 8 publications

References 71 publications

The Effects of Isometric Fatigue on Trunk Muscle Stiffness: Implications for Shear-Wave Elastography Measurements

The Effects of Isometric Fatigue on Trunk Muscle Stiffness: Implications for Shear-Wave Elastography Measurements

Modalities of sequential human robot collaboration trigger different modifications of trunk oscillations

Urethane-Foam-Embedded Silicon Pressure Sensors including Stress-Concentration Packaging Structure for Driver Posture Monitoring

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