Browse Topic: Physical examination

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This document applies to safety observers or spotters involved with the use of outdoor laser systems. It may be used in conjunction with SAE Aerospace Standard (AS4970) “Human Factors Considerations for Outdoor Laser Operations in the Navigable Airspace.” Additional control measures may be applicable and are listed in ANSI Z136.6.
G10T Laser Safety Hazards Committee
This slash document collects general reference material related to gaseous oxygen system flow requirements and sizing calculations. This document will assist oxygen system equipment designers and operators to establish systems and equipment requirements. The document consists of charts, tables, system schematics, system requirements, and sample calculations for system sizing.
A-10 Aircraft Oxygen Equipment Committee
This standard is intended to apply to portable compressed gaseous oxygen equipment. When properly configured, this equipment is used either for the administration of supplemental oxygen, first aid oxygen or smoke protection to one or more occupants of either private or commercial transport aircraft. This standard is applicable to the following types of portable oxygen equipment: a Continuous flow 1 Pre-set 2 Adjustable 3 Automatic b Demand flow 1 Straight-demand 2 Diluter-demand 3 Pressure-demand c Combination continuous flow and demand flow.
A-10 Aircraft Oxygen Equipment Committee
Two qualified test pilots performed a target tracking flight task on a Bell 205 helicopter. Cooper-Harper handling quality ratings confirmed that pilot compensation was proportional to task difficulty. Pilot control activity was measured using the Dynamic Interface Modeling and Simulation System Product Metric (DIMSS-PM) to quantify the number and amplitude of control deflections during each trial. Inter-beat interval measures of heart rate and heart rate variability were also computed to evaluate the pilot's autonomic nervous system (ANS) response to task workload. The DIMSS-PM was positively correlated with task difficulty, as expected based on the dynamics of target motion for each difficulty level. By comparison, the mean and high-frequency (HF) variability of heart beat intervals were negatively correlated with task difficulty, suggesting an increase in ANS arousal with increased pilot workload. Pilot-specific differences were found in the time-dependent relationship between DIMSS-PM, mean heart beat interval, and HF variability, indicating that control activity and heart rate metrics provide asynchronous and complementary information about pilot workload during helicopter flight. NOTATION ANOVA Analysis of Variance ANS Autonomic Nervous System DIMMS-PM Dynamic Interface Modeling and Simulation System Product Metric ECG Electrocardiogram FBW Fly-By-Wire FRL Flight Research Laboratory HR Heart Rate HRV Heart Rate Variability HQR Handling Qualities Rating NRC National Research Council Canada RRI Inter-beat (R-R) Interval RMS Root Mean Square RMSSD RMS of Successive Differences in RRI SDNN Standard Deviation of RRI HF High Frequency (0.15 - 0.4 Hz) LF Low Frequency (0.04 - 0.15 Hz)
Law, AndrewJennings, SionEllis, Kris
Driver Workload in an Autonomous Vehicle2019-01-08724/2/2019
As intelligent automated vehicle technologies evolve, there is a greater need to understand and define the role of the human user, whether completely hands-off (L5) or partly hands-on. At all levels of automation, the human occupant may feel anxious or ill-at-ease. This may reflect as higher stress/workload. The study in this paper further refines how perceived workload may be determined based on occupant physiological measures. Because of great variation in individual personalities, age, driving experiences, gender, etc., a generic model applicable to all could not be developed. Rather, individual workload models that used physiological and vehicle measures were developed. Unlike some existing methods of workload estimation where one, or a few signals are used, such as electroencephalography (EEG), electrocardiography (ECG), we developed intelligent systems that use multiple physiological and vehicle signals based on an end-to-end deep neural learning architecture to make a robust estimation of workload. The deep neural learning system, MTS-CNN, is designed to learn workload patterns from synchronized, heterogeneous temporal signals. All data collected for training and testing are from real-world driving trips along the same route which comprised urban local roads and highways. Data from twenty participants whose driving experience ranged from a few months to several years were collected and analyzed. The experimental results indicate that the proposed driver workload estimation model is capable of learning well from the combined temporal physiological and vehicle signals and good performance was obtained on workload estimation.
Murphey, YiKochhar, Dev S.Xie, Yongquan
Analyzing the Limitations of the Rider and Electric Motorcycle at the Pikes Peak International Hill Climb Race2019-01-11254/2/2019
This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel. Even though the rider’s heart rate implied a vigorous exercise intensity level, throttle values indicated that the rider wanted to go faster ~33% of the time. The motor reached a steady-state temperature that was approximately 30°C below the maximum allowable temperature and thus could have handled more current. By analyzing additional thermal and current data, it was concluded that the motor controller was likely a limiting factor but not the battery capacity since only ~2/3 of the total available battery energy was consumed. A model that estimates the optimal sprocket ratio was derived and validated; It was determined that using the optimal sprocket ratio of 62/12 would have decreased the finishing time by approximately 2 seconds.
Rodgers, LennonJeunnette, MarkBiffard, RyanMöller, BjörnWu, EricMatthys, Koen
Sensations Associated with Motion Sickness Response during Passenger Vehicle Operations on a Test Track2019-01-06874/2/2019
Motion sickness in road vehicles may become an increasingly important problem as automation transforms drivers into passengers. The University of Michigan Transportation Research Institute has developed a vehicle-based platform to study motion sickness in passenger vehicles. A test-track study was conducted with 52 participants who reported susceptibility to motion sickness. The participants completed in-vehicle testing on a 20-minute scripted, continuous drive that consisted of a series of frequent 90-degree turns, braking, and lane changes at the U-M Mcity facility. In addition to quantifying their level of motion sickness on a numerical scale, participants were asked to describe in words any motion-sickness-related sensations they experienced. Prior to in-vehicle testing, participants were shown a list of sensations that are commonly experienced during motion sickness: head sensations, body temperature change, drowsiness, dizziness, mouth sensations, nausea, or other sensations, which refer to difficulty focusing, irritability, eyestrain, or difficulty concentrating. Participants were instructed not to limit themselves to the list, but rather to report in their own words how they felt throughout the drive. For each sensation, they were also asked to describe the level of the sensation they experienced as mild, moderate, or severe. Chi-square analysis demonstrated that the sensations experienced were associated with in-vehicle test conditions and participant’s motion sickness susceptibility. This study is the first to continuously quantify the type, incidence, intensity, and timeline progression of self-reported sensations associated with motion sickness response during passenger vehicle operations on a test track. Sensations were multidimensional and highly variable across individuals indicating that motion sickness is a multi-faceted response that extends beyond nausea.
Jones, Monica Lynn HaumannEbert, SheilaReed, Matthew
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