Driving-induced lower back pain: Investigation of causes and recommendations with TRIZ

Background: Driving-induced lower back pain (DLBP) is associated with long driving times and awkward postures. Nonetheless, its actual causes and solutions remain unclear due to intervening causes from activities of daily living and traumatic injuries. This study investigated the causes and recommendations for DLBP using the theory of inventive problem solving (TRIZ). Methods: A cause-and-effect chain analysis (CECA) was conducted based on discussions with 19 ergonomics experts from Malaysia. Engineering contradictions were formulated according to the causes and associated with the parameters of the TRIZ system. These parameters were then intersected in the contradiction matrix to extract the inventive principles. Finally, recommendations were made based on these principles. Results: CECA uncovered the design- and posture-related causes of DLBP. It was implied that missing seat adjustment controls might cause drivers to sit with their knees positioned higher than their hips. This issue causes an excessive posterior pelvic tilt, resulting in DLBP. To address this issue, an inert atmosphere involving the addition of inflatable bubble wraps to elevate the posterior position was recommended. Conclusion: While there have been studies on DLBP, the present study demonstrated originality by using TRIZ to preliminarily but systematically investigate and resolve DLBP. Further triangulations, prototyping, experimentations, and verifications were not possible due to time and budgetary constraints. Nevertheless, this research uncovered the TRIZ-integrated perspectives on ergonomic solutions to DLBP that are more cost-effective than medical treatments or design overhauls.

Driving-induced lower back pain: Investigation of causes and recommendations with TRIZ

Background: Driving-induced lower back pain (DLBP) is associated with long driving times and awkward postures. Nonetheless, its actual causes and solutions remain unclear due to intervening causes from activities of daily living and traumatic injuries. This study investigated the causes and recommendations for DLBP using the theory of inventive problem solving (TRIZ). Methods: A cause-and-effect chain analysis (CECA) was conducted based on discussions with 19 ergonomics experts from Malaysia. Engineering contradictions were formulated according to the causes and associated with the parameters of the TRIZ system. These parameters were then intersected in the contradiction matrix to extract the inventive principles. Finally, recommendations were made based on these principles. Results: CECA uncovered the design- and posture-related causes of DLBP. It was implied that missing seat adjustment controls might cause drivers to sit with their knees positioned higher than their hips. This issue causes an excessive posterior pelvic tilt, resulting in DLBP. To address this issue, an inert atmosphere involving the addition of inflatable bubble wraps to elevate the posterior position was recommended. Conclusion: While there have been studies on DLBP, the present study demonstrated originality by using TRIZ to preliminarily but systematically investigate and resolve DLBP. Further triangulations, prototyping, experimentations, and verifications were not possible due to time and budgetary constraints. Nevertheless, this research uncovered the TRIZ-integrated perspectives on ergonomic solutions to DLBP that are more cost-effective than medical treatments or design overhauls.

Researches on the ergonomic design of the workplace for the car driver profesion

Ergonomic design of the workplace is a matter of utmost importance. Ergonomics should consider reducing the high number of accidents as well as the high amounts paid for insurance. In recent years there have been numerous injuries due to cumulative trauma or due to psychological, sociological and ethical factors. Operators often adapt to improper work conditions, but the number of accidents increases and productivity decreases. The driver of the vehicle must be provided with a suitable space and position so that his posture is physiologically comfortable, does not cause excessive fatigue and illness, there is freedom of movement to drive the steering wheel, steering levers and pedals, which must be accessible and placed in such a way that the driver’s requirements are minimal and the visibility is ensured. In the paper there will be presented elements for the ergonomic design of the workplace for the motor vehicle occupation and will be taken into account several factors involved among which we mention the correct positioning of the seat adjustment while driving a car, adjusting the backrest and seat, adjusting the lumbar support, adjusting the steering position, adjusting the headrests to ensure that in the event of an accident, the risk of a head injury is very small.

Development and Evaluation of a Computational Human Performance Model of In-vehicle Manual and Speech Interactions

Usability evaluation traditionally relies on costly and time-consuming human-subject experiments, which typically involve developing physical prototypes, designing usability experiment, and recruiting human subjects. To minimize the limitations of human-subject experiments, computational human performance models can be used as an alternative. Human performance models generate digital simulations of human performance and examine the underlying psychological and physiological mechanisms to help understand and predict human performance. A variety of in-vehicle information systems (IVISs) using advanced automotive technologies have been developed to improve driver interactions with the in-vehicle systems. Numerous studies have used human subjects to evaluate in-vehicle human-system interactions; however, there are few modeling studies to estimate and simulate human performance, especially in in-vehicle manual and speech interactions. This paper presents a computational human performance modeling study for a usability test of IVISs using manual and speech interactions. Specifically, the model was aimed to generate digital simulations of human performance for a driver seat adjustment task to decrease the comfort level of a part of driver seat (i.e., the lower lumbar), using three different IVIS controls: direct-manual, indirect-manual, and voice controls. The direct-manual control is an input method to press buttons on the touchscreen display located on the center stack in the vehicle. The indirect-manual control is to press physical buttons mounted on the steering wheel to control a small display in the dashboard-cluster, which requires confirming visual feedback on the cluster display located on the dashboard. The voice control is to say a voice command, “ deflate lower lumbar” through an in-vehicle speaker. The model was developed to estimate task completion time and workload for the driver seat adjustment task, using the Queueing Network cognitive architecture (Liu, Feyen, & Tsimhoni, 2006). Processing times in the model were recorded every 50 msec and used as the estimates of task completion time. The estimated workload was measured by percentage utilization of servers used in the architecture. After the model was developed, the model was evaluated using an empirical data set of thirty-five human subjects from Chen, Tonshal, Rankin, & Feng (2016), in which the task completion times for the driver seat adjustment task using commercial in-vehicle systems (i.e., SYNC with MyFord Touch) were recorded. Driver workload was measured by NASA’s task load index (TLX). The average of the values from the NASA-TLX’s six categories was used to compare to the model’s estimated workload. The model produced results similar to actual human performance (i.e., task completion time, workload). The real-world engineering example presented in this study contributes to the literature of computational human performance modeling research.

Development of Statistical Models for Predicting a Driver’s Hip and Eye Locations

Regression equations for estimation of a driver’s hip location (HL) and eye location (EL) using the driver’s anthropometric and posture variables have been developed for US drivers. However, those equations are limited to US drivers and do not include seat adjustment variables (e.g., cushion angle) that may affect a driver’s HL and EL. The present study developed statistical models for prediction of a driver’s HL and EL using seat configurations including (1) fore-aft seat position, (2) seat height, (3) seat back recline angle, and (4) seat cushion angle. Driving postures of 23 Korean drivers (10 females and 13 males) were measured in a seating buck after adjusting seat configurations according to their preferences. The seat configurations, HLs, ELs, and joint angles of the participants were collected by a motion capture system. HL and EL prediction models based on the seat configurations and driving postures were developed by stepwise regression. The proposed models showed high accuracy (adj. R2 = .83 ± .13, RMSE = 19.1 ± 4.2 mm) in prediction of HL and EL. The performance difference between the seat configuration- and posture-based models was not statistically significant. The proposed seat configuration-based models can be used for accurate estimation of a driver’s HL and EL for occupant packaging layout design.