Galvanic vestibular stimulation to counteract leans illusion: comparing step and ramped waveforms.

Leans is a common type of Spatial Disorientation (SD) illusion that causes pilots to be confused about the position of the aircraft during a flight. This illusion could lead to serious adverse effects and even flight mishaps. Therefore, an effective means to deal with leans is crucial for flight safety. This study aims to investigate the effects of Galvanic Vestibular Stimulation (GVS) technology with different waveforms as a tool to mitigate the negative effects of leans. 20 Air Force pilots participated in leans-induced flight simulation experiment with three GVS conditions (without-GVS, step-GVS, ramped-GVS). Bank angle error, subjective SD, perceived strength, and annoyance were measured as the dependent variables. Analysis revealed that step-GVS and ramped-GVS yielded lower bank angle errors and subjective SD than without-GVS. In addition, annoyance ratings were lower for ramped-GVS than step-GVS. This study suggests that GVS has the potential to be utilised as a counteracting tool to cope with leans.Practitioner summary: Galvanic Vestibular Stimulation (GVS) can be utilised as a tool to counteract the detrimental effects of leans illusion, specifically the ramped style GVS, considering that it is less annoying and distracting for the pilots. In general, GVS induces a roll sensation that can offset the false sensation caused by the leans, which can potentially help maintain flight safety and avoid spatial disorientation-related accidents.Abbreviations: SD: spatial disorientation; GVS: galvanic vestibular stimulation; MSSQ: motion sickness susceptibility questionniare; SSQ: simulator sickness questionnaire; BLE: bluetooth low energy; PCB: printed circuit board; RPM: revolution per minute.

Multimodal interaction: Input-output modality combinations for identification tasks in augmented reality.

Multimodal interaction (MMI) is being widely implemented, especially in new technologies such as augmented reality (AR) systems since it is presumed to support a more natural, efficient, and flexible form of interaction. However, limited research has been done to investigate the proper application of MMI in AR. More specifically, the effects of combining different input and output modalities during MMI in AR are still not fully understood. Therefore, this study aims to examine the independent and combined effects of different input and output modalities during a typical AR task. 20 young adults participated in a controlled experiment in which they were asked to perform a simple identification task using an AR device in different input (speech, gesture, multimodal) and output (VV-VA, VV-NA, NV-VA, NV-NA) conditions. Results showed that there were differences in the influence of input and output modalities on task performance, workload, perceived appropriateness, and user preference. Interaction effects between the input and output conditions on the performance metrics were also evident in this study, suggesting that although multimodal input is generally preferred by the users, it should be implemented with caution since its effectiveness is highly influenced by the processing code of the system output. This study, which is the first of its kind, has revealed several new implications regarding the application of MMI in AR systems.

Mobile Health Application for Seizure Management: A Human-Systems Integration Approach.

OBJECTIVE: The study aims to develop a mHealth application for seizure management based on the human system integration (HSI) approach. BACKGROUND: Unmet healthcare needs among people with epilepsy continue to exist despite the advancement in healthcare technology. Current seizure management methods are found to be ineffective. Therefore, a more efficient strategy such as mHealth technology is necessary to aid seizure management. METHOD: The needs identification phase involved identifying the user requirements by interviewing 10 stakeholders and conducting thematic analysis and needs interpretation technique. In the solution identification phase, the system requirements were derived using various human-centered design and systems engineering approaches and were evaluated through quality function deployment to determine design targets. For the design and evaluation phase, the design targets were reflected in the app through the iterative prototyping process, and the interface and functional design were evaluated by seven human factors and ergonomics experts and four stakeholders, respectively. RESULTS: Three primary needs and ten user requirements were derived from the needs identification phase. Ten out of fifteen system requirements were selected as design targets to be included in the final prototype. Results of the evaluation showed that the interface design of the proposed app showed superior usability compared to a competitor app and that the app functions were beneficial for the stakeholders. CONCLUSION: The mHealth app designed through the HSI framework showed good potential in addressing the main issues in seizure management. APPLICATION: The mHealth app design methodology based on the HSI approach can be applied to the design of small-scale systems in various domains.