Venkatesh Balasubramanian

Cervical spine is a mobile area of spine with high susceptibility to injury like sport injuries, pilot ejection, falls, vehicular accidents, etc. Use of finite element (FE) model in understanding the kinematics will provide an insight into the mechanism of neck injuries. A three dimensional (3D) anatomically detailed FE model of cervical spine was developed using computer tomography (CT) images. The model consisted of 39,419 elements with 10,411 nodes. Important anatomical features viz., cortical bone, cancellous bone, transverse process, spinous process, laminae, intervertebral disc, spinal canal were clearly defined for each spinal segment. Material properties were obtained from literature and boundary conditions were simulated similar to the in vitro experiment against which it is validated. Intervertebral range of motion of FE model was compared with previous in vitro and in vivo studies. These comparisons substantiated that, this FE model can effectively reflect physiological motions of human cervical spine and can be used further to study cervical spine injury and dysfunction. © 2006 Research Publishing Services.

Pedestrian involvement is a major subset of road crashes. It is estimated that pedestrian road crash is about 22% of all road traffic related deaths. Since pedestrians share road space and traffic, they are susceptible to the crashes especially where a large number of pedestrians are seen on roads. In this study, we have estimated the pedestrian’s response and time taken to estimate the correct recognition of the vehicle approaching them while crossing the road. EEG analysis has been performed to estimate cognitive response of pedestrians. Thirty volunteers participated in this study. Six scenarios were presented as were shown to the participants. Analysis was performed based on EEG acquired activity. It was observed that less beta activity (p > 0.0%) was estimated when participants were shown the video of “low beam car with active light source” and “high beam car with active light source”. This clearly indicates that active light source in addition to headlights of car make pedestrians less confused about the oncoming traffic. This helps them to cross especially in low illumination allies.

Reducing the injury severity from traffic accidents is most important step in mitigating accidents occurring in developing economies like India where two way roads are more common in cities. The number of deaths due to accidents has rose from 83,491 in 2005 to 1,36,071 in 2016 as per the latest reports of ministry of road transport and highways, government of India (MoRTH, 2016). To explore the factors contributing to injury severity in such roads, non parametric classification tree is used since it does not assume any underlying assumption between target variable and the predictors. CART (Classification and Regression tree), a classification tree establishes empirical relation between injury severity outcomes and variables including driver, vehicle, crash and environmental factors. The present study analyzed traffic crash data of single lane two way roads of Chennai city pertaining to period from January 2015 to December 2016. The final dataset included a total of 5271 crash information after excluding incomplete and missing data. This finalized dataset was split into two subsets, training and testing data and the classification models reported an accuracies of 63.4% and 61.5% for the training and testing data. The results indicated that collision type and vehicle type were the two important variables affecting the severity of injury. The findings of this study will help in determining influential factors so that countermeasures to reduce the severity of injury in urban cities can be developed.

According to the reports of NITI (National Institute of Transforming India) Aayog Freight 2018, Road freight is the prime mode (59%) of transport in India with the highest per ton-mile cost than rail or water freight (NITI Aayog, 2018). This road freight usually uses heavy-duty vehicles to transmit voluminous goods and services to the destination in time. Due to this, the heavy-duty vehicle population increased on the Indian roads. Heavy-duty vehicle crashes cause a substantial economic burden to the nation and result in more severity to the involved because of differences in weight, speed, and size. Among heavy-duty vehicle crashes, a significant proportion of crashes are heavy-duty single-vehicle crashes. Single-vehicle crashes are those crashes where the vehicle drivers either involve in self-skidding or hit a stationary object (like a tree). The purpose of this study is to investigate the injury pattern in heavy-duty single-vehicle crashes. For this study, the data is extracted from the RADMS (Road Accident Database Management System) database and linked with hospital data. This data includes demographic information, road, environmental and injury characteristics. Later, descriptive statistics performed on the dataset to analyse all heavy-duty single-vehicle crashes between January 2013 and December 2018. Overall, 4704 single heavy-duty vehicle crashes occurred during this period, among which 1244 were fatal crashes. Results show that male drivers aged 26 to 64 years old suffered more fatalities (88%), followed by the 18-25 age group (8%). Examination of injury information found that heavy-duty vehicle drivers mostly sustained multiple injuries (9.05%), head injuries (5.05%), followed by leg injuries (4.29%). The results showed that specific road and environmental factors increase the chance of fatal crashes among heavy-duty vehicle drivers. Furthermore, the proposed study gives insight into the injury characteristics and key contributing factors causing heavy-duty single-vehicle crashes. Finally, this study provides appropriate countermeasures and techniques that can mitigate heavy-duty single-vehicle collisions.

Prolonged standing during monotonous tasks such as assembly line jobs may lead to musculoskeletal disorders including increased fatigue, pain and stiffness in active muscles. This study evaluated the efficacy of a dynamic standing posture over the stationary standing posture in reducing physical stress using sEMG and psychophysical analysis. From the sEMG study, it was found that the fatigue rates in leg and lower back muscles were significantly higher (p < 0.05) in the stationary standing posture as compared to the dynamic standing posture. This finding was corroborated by the results of psychophysical test. Ergonomic design of the dynamic posture that has been proposed in this study can be easily absorbed into most shop-floors to decrease the incidence of musculoskeletal disorders. Relevance to Industry: The proposed dynamic standing can decrease fatigue and risk of acquiring lower extremity disorders than a stationary standing during shop-floor and assembly line duties. Dynamic standing is simple to implement and can be incorporated in existing assembly line. This can improve occupational safety by reducing work-related musculoskeletal disorders. © 2008 Elsevier B.V. All rights reserved.

Introduction: Bicyclists are vulnerable users in the shared asset like roadways. However, people still prefer to use bicycles for environmental, societal, and health benefits. In India, the bicycle plays a role in supporting the mobility to more people at lower cost and are often associated with the urban poor. Bicyclists represents one of the road user categories with highest risk of injuries and fatalities. According to the report by the Ministry of Road Transport and Highways (Accidents, 2017) in India, there is a sharp increase in the number of fatal victims for bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2,585 in 2016 to 3,559 in 2017, a 37.7% increase. Method: Few studies have only investigated the crash risk perceived by the bicyclists while interacting with other road users. The present paper investigates the injury severity of bicyclists in bicycle-vehicle crashes that occurred in the state of Tamilnadu, India during the nine year period (2009–2017). The analyses demonstrate that dividing bicycle-vehicle collision data into five clusters helps in reducing the systematic heterogeneity present in the data and identify the hidden relationship between the injury severity levels of bicyclists and cyclists demographics, vehicle, environmental, temporal cause for the crashes. Results: Latent Class Clustering (LCC) approach was used in the present study as a preliminary tool for the segmentation of 9,978 crashes. Later, logistic regression analysis was used to identify the factors that influence bicycle crash severity for the whole dataset as well as for the clusters that were obtained from the LCC model. Results of this study show that combined use of both techniques reveals further information that wouldn't be obtained without prior segmentation of the data. Few variables such as season, weather conditions, and light conditions were significant for certain clusters that were hidden in the whole dataset. This study can help domain experts or traffic safety researchers to segment traffic crashes and develop targeted countermeasures to mitigate injury severity.

Psychophysiological studies have illustrated the role of stress in altering the reaction time in an individual. However, studies relating to driving stress and changes in driver’s reaction time are scarce. This study’s importance stems from the observation that driving stress is a critical causal factor of risky driving and on-road crashes. The study attempted to quantify the role of driving stress in altering the reaction time in drivers. Thirty subjects (Mean age = 24.56 ± 1.46 years, 18 males) volunteered to the study. The driving stress was induced by a highly congested urban simulated driving experiment. The reaction time before and after the simulated driving correlated with violations made during the simulated driving. The results suggest that risky behavior in driving stress could be attributed to impaired reaction time in drivers. The work highlights the importance of driving stress in congested roads and its implications of risky driving.

The objective of this study is to understand fatigue of leg muscles (tibialis anterior and gastrocnemius) using surface electromyography (sEMG) when the feet are supported on the floor and when they are unsupported. Twelve healthy male volunteers participated in the study. Chair height was changed to two positions, i.e. (A) suit subject height perfectly - with feet supported on the floor, and (B) height higher than A and feet were suspended in air. Each seated position was tested for 20 minutes. Surface EMG signals were recorded bilaterally from the two groups of leg muscles for 60 seconds at 5 minutes intervals from 0th to 20th minute. These signals were filtered using a 4th order Butterworth filter with a pass band of 20-250 Hz and a 4th order notch filter with a stop band of 47-49 Hz. RMS values and mean power frequency (MPF) of the signals were calculated. Linear regression analysis was performed on MPF values and the RMS Values. Mann-Whitney 'U' test was performed on the slope of the regression lines and the RMS values to determine if there was any significant difference in fatigue. Results indicated a higher muscle fatigue for height B. This suggests that while adjusting height of chair, identifying optimal height should be taken into consideration. In places where adjustable chairs are not available, provision for foot rest would be advisable, to avoid muscle fatigue.

Risky driving has been one among the major causes of accidents on road. Motivation behind the study was to identify if emotional intelligence influences impetuous driving in highly congested roads. Volunteers answered a 4-point online survey of questions sampled from Indian license test, driving skill and behavior questionnaires. Cronbach alpha was acceptably high validating the questions. Spearman correlation values indicated a strong correlation between driving hours and driving skill and behavior. K-means clustering was used to cluster the subjects into 4 categories based on driving skill and driving behavior scores. The cluster with highest number of subjects consisted of people who drive every day with high risky driving scores. The results suggest that people who drive everyday have poor emotional intelligence which impedes a safe driving. The study proposes that educating drivers with emotional regulation could help in safer roads.

Safe driving places importance on cognitive aspects, such as perception, vigilance, reasoning, judgement as well as efficient motor skills. Cognitive fatigue brings about a loss of attentiveness in drivers, which could be detrimental; decrease in attentiveness can be measured using electroencephalogram (EEG) signals. The principal objective of this study was to analyse and determine cognitive fatigue within subjects during a short duration of driving in a simulated environment using EEG recordings. Six male volunteers participated in this study in which each of them drove for 15 min in the simulator. EEG signals were collected from seven characteristic locations on the cranium using surface electrodes. Mean alpha activities corresponding to the 4th and 12th min were computed for all channels. Mean power alpha activity was significantly high (p < 0.028) in the 12th min when compared to 4th min. This is symptomatic of cognitive fatigue in the volunteers. Our study effectively demonstrated that cognitive fatigue in drivers can be determined using EEG. Copyright © 2011 Inderscience Enterprises Ltd.