Fitness trackers have broadened the healthcare ecosystem and made self-tracking everyday physical activities possible. Features like heart rate monitoring can help detect health ailments, yet there is little evidence that suggests tracking health indicators and physical activities leads to long-term health behavior change. This proceeding analyzes areas of Human Factors that could be used to increase long-term user engagement. Feedback, information display, and specific design principles and case studies are discussed.

Prosthetics can offer endless benefits to people with limb amputations, yet their abandonment is high. Although technology has improved these tools in outstanding ways, their lack of simplicity prevents the user from acquiring a natural, fluent experience, which, in turn, has led to their rejection. In this review, we will explore the main reasons why people with upper limb amputations decrease their use or stop using their myoelectric prosthesis permanently while performing their daily activities.

Hospitals are where patients stay to improve their current condition and well-being. However, many studies demonstrate that noise has negative effects on patients during their hospital stays. Results from these studies show that areas such as sleep quality, speech processing, and various physiological functions are influenced by noise exposure. Most of the noise exposure that causes disturbances to the patients are generated by the staff and machines. This paper describes and discusses noise pollution in hospitals so that appropriate countermeasures can be applied to reduce noise. In addition to research on noise reduction strategies, recommendations regarding the improvement of human factors, such as staff training and hospital design, are discussed.

A systematic review of virtual reality (VR) systems for individuals diagnosed with autism spectrum disorder (ASD) is detailed. The aim of this proceeding is to determine which social skills focuses are most important and how their potential improvements should be measured. Following the PRISMA guidelines, 48 articles were identified, and a total of 12 articles that met the a priori criteria were given full review. All 12 studies aimed to train some social skill, but there was no agreement among which single theme was most essential. The ones that received the most attention were facial expression and emotion recognition, appropriate behaviors and responses, and initiating social interactions. Only 9 studies used direct measures to assess changes made as a result of the VR systems. The ones most commonly used were subjective measures and participants’ body movements and gestures. The collective impacts and limitations of the studies are presented, as well as implications for future work.

This work aims to summarize research methods for measuring personality in human-robot interaction. A systematic review was performed, resulting in 35 studies that were categorized by whether they assessed human personality, robot personality, or both. It was found that the five-factor model (Big Five) of personality (extraversion, agreeableness, conscientiousness, openness, and neuroticism) was a common theme as it was used to assess personality in 31% of studies, and the extraversion dimension alone was used to assess personality in an additional 26% of studies. The strengths, limitations, and the human factors influences of user expectations of the Big Five as well as the recommendation for its use are discussed.