Four subjective workload scales were compared along four dimensions: sensitivity, operator acceptance, resource requirements, and special procedures. The scales were the Modified Cooper-Harper scale, the National Aeronautics and Space Administration Task Load Index (TLX), the Overall Workload (OW) scale, and the Subjective Workload Assessment Technique. Three U.S. Army systems were studied for potential workload concerns. Data from five different studies on the three systems were compared along the aforementioned four dimensions. Results indicate that all four scales are acceptable tools and are sensitive to different levels of workload. However, TLX and OW are consistently superior when considering sensitivity, as measured by factor validity, and operator acceptance. This research is an example of a systematic approach for examining human factors measurement tools.

Four empirical operator workload (OWL) scales were applied to ground control operations of the Aquila RPV during a recent field test: NASA TLX, SWAT, Overall Workload (OW), and the Modified Cooper-Harper (MCH). Seventeen sets of individual assessments of mission segments were made by the four members of each of four crews and one replacement crewman. Jackknife factor analysis revealed the presence of only a single factor and indicated that the mean factor loadings formed a consistent ordering ( F(3,48) = 503.5, p < .00005): TLX (.910) > SWAT (.893) > OW (.869) > MCH (.833). ANOVAs also examined the effects of various variables on the composite workload factor scores; significant findings were found which reflected both upon the system and its test. These findings as well as informal lessons learned are discussed in the context of the development and validation of a methodology for assessing OWL.

Operator workload (OWL) scales were used to obtain ratings of generic mission scenarios and tasks for a mobile air defense missile system (LOS-F-H) following a candidate-selection field evaluation. NASA TLX, SWAT, Overall Workload (OW), and the Modified Cooper-Harper (MCH) ratings were obtained from both crew and Subject Matter Experts (SMEs). Jackknife factor analysis revealed the presence of only a single "OWL" factor for both crew and SMEs (explaining 75.9% and 82.6% of the respective total variances) and indicated a significant (p c 0.00005) ordering of the mean factor loadings: TLX (0.924) was significantly greater than OW (0.905) and MCH (0.904), which were greater than SWAT (0.778). Subsequent analysis of OWL factor scores indicated that the crew and SMEs yielded essentially equivalent evaluations of OWL for the system variables investigated. This analysis also indicated that the highest levels of OWL were obtained for the track-to-intercept task during dual Rotary-Wing (RW) and Fixed-Wing (FW) attacks, although the ID/IFF task during a dual RW attack was almost as high. These findings are discussed in the context of a methodology for assessing OWL.

The Ocular Attention-Sensing Interface System (OASIS) is an innovative human-computer interface which utilizes eye movement and voice commands to communicate messages between the operator and the system. This report initially describes some technical issues relevant to the development of such an interface. The results of preliminary experiments which evaluate alternative eye processing algorithms and feedback techniques are presented. Candidate interface applications are also discussed.

Four operator workload (OWL) scales were retrospectively applied to crewmembers of a mobile air defense missile system, LOS-F(H), following a candidate-selection field evaluation: NASA TLX, SWAT, Overall Workload (OW), and the Modified Cooper-Harper (MCH). Jackknife factor analysis revealed the presence of only a single factor (explaining 79.6% of the total variation) and indicated a significant ( p<.0075) ordering of the mean factor loadings: TLX (.935) and OW (.927) were significantly greater than MCH (.862) and SWAT(.860). Comparison with an earlier field test of a remotely piloted vehicle revealed a significant ( p<.00005) interaction of test and ordering of the OWL scales, but TLX and MCH consistently had the respectively highest and lowest loadings across the two field tests. Multiple correlation also revealed a significant ( p < .0001) relationship, R = 0.66, between system performance and TLX. These findings and lessons learned are discussed in the context of the development and validation of a methodology for assessing OWL.