Simulating dynamic facial expressions of pain from visuo-haptic interactions with a robotic patient.

Medical training simulators can provide a safe and controlled environment for medical students to practice their physical examination skills. An important source of information for physicians is the visual feedback of involuntary pain facial expressions in response to physical palpation on an affected area of a patient. However, most existing robotic medical training simulators that can capture physical examination behaviours in real-time cannot display facial expressions and comprise a limited range of patient identities in terms of ethnicity and gender.

Toward Mass Customization Through Additive Manufacturing: An Automated Design Pipeline for Respiratory Protective Equipment Validated Against 205 Faces.

Respiratory protective equipment (RPE) is traditionally designed through anthropometric sizing to enable mass production. However, this can lead to long-standing problems of low-compliance, severe skin trauma, and higher fit test failure rates among certain demographic groups, particularly females and non-white ethnic groups. Additive manufacturing could be a viable solution to produce custom-fitted RPE, but the manual design process is time-consuming, cost-prohibitive and unscalable for mass customization.