Kennerly H. Digges

Bio: Kennerly H. Digges is an academic researcher from George Washington University. The author has contributed to research in topics: Poison control & Crash. The author has an hindex of 22, co-authored 142 publications receiving 1810 citations. Previous affiliations of Kennerly H. Digges include National Highway Traffic Safety Administration.

Side impact injury risk for belted far side passenger vehicle occupants

Abstract: In a side impact, the occupants on both the struck, or near side, of the vehicle and the occupants on the opposite, or far side, of the vehicle are at risk of injury. Since model year 1997, all passenger cars in the U.S. have been required to comply with FMVSS No. 214, a safety standard that mandates a minimum level of side crash protection for near side occupants. No such federal safety standard exists for far side occupants. The mechanism of far side injury is believed to be quite different than the injury mechanism for near side injury. Far side impact protection may require the development of different countermeasures than those which are effective for near side impact protection. This paper evaluates the risk of side crash injury for far side occupants as a basis for developing far side impact injury countermeasures. Based on the analysis of NASS/CDS 1993-2002, this study examines the injury outcome of over 4500 car, light truck, and van occupants subjected to far side impact. The analysis was restricted to 3-point belted occupants. The paper evaluates the risk of far side impact injury as a function of struck body type, collision partner, delta-V, crash direction (PDOF), occupant compartment intrusion, and injury contact source. Injury risk is evaluated using the maximum injury severity for each occupant, by injury severity for each body region, and by Harm, a social cost measure.

Characteristics of Crashes that Increase the Risk of Serious Injuries

Abstract: The advent of Automatic Crash Notification Systems (ACN) offers the possibility of immediately locating crashes and of determining the crash characteristics by analyzing the data transmitted from the vehicle. A challenge to EMS decision makers is to identify those crashes with serious injuries and deploy the appropriate rescue and treatment capabilities. The objective of this paper is to determine the crash characteristics that increase the risk of serious injury. Within this paper, regression models are presented which relate occupant, vehicle and impact characteristics to the probability of serious injury using the Maximum Abbreviated Injury Scale Level (MAIS). The accuracy of proposed models were evaluated using National Automotive Sampling System/ Crashworthiness Data System (NASS/CDS) and Crash Injury Research and Engineering Network (CIREN) case data. Cumulatively, the positive prediction rate of models identifying the likelihood of MAIS3 and higher injuries was 74.2%. Crash mode has a significant influence of injury risk. For crashes with 30 mph deltaV, the risk of MAIS3+ injury for each mode is 38.9%, 83.8%, 47.8% and 19.9% for frontal, near side, far side and rear impact crashes, respectively. In addition to deltaV, a number of crash variables were identified that assist in the accurate prediction of the probability of MAIS 3+ injury. These variables include occupant age, partial ejection, safety belt usage, intrusion near the occupant, and crashes with a narrow object. For frontal crashes, added crash variables include air bag deployment, steering wheel deformation, and multiple impact crashes. The quantitative relationship between each of these crash variables and injury risk has been determined and validated by regression analysis based on NASS/CDS and CIREN data.

Relationships between crash casualties and crash attributes

Abstract: This work addresses and evaluates the likelihood of human casualties in highway crashes, projected on the basis of field crash data that may become available electronically by sensors at crash time, and/or observed at the crash scene by emergency attendants. Termed collectively as a "crash signature", such data are treated as predictors and are selected from: crash severity, general area of damage, direction of force, occurrence of rollover, intrusion, vehicle crush and its specific horizontal location, collision partner, vehicle class and size, occupant age, gender, restraint use and type, seating position, and others. Crash signatures are converted into responses such as: (a) the likelihood of the most severe outcome, fatality or survived injury, by severity AIS per occupant; and (b) the same per vehicle. Cars are the vehicles selected for this investigation. A likelihood is quantified by a probability of occurrence, as a function of a string of predictors selected for maximum resolution and sensitivity, and minimum contribution to error. (A) For the covering abstract see IRRD 893297.

WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones

Abstract: Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and acoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awareness to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives.