Digital Twin City for Age-friendly Communities: Crowd-biosensing of Environmental Distress for Older Adults
Population aging is a global concern, and it demands smarter and more connected cities for independent mobility and healthy aging of older adults. However, traditional urban planning and design practices that target the “average person” have failed to meet the special needs of older adults experiencing multiple physiological and psychological declines associated with various stages of aging. To address this grand challenge, this project aims to construct a digital twin city (DTC) model with bio-signals (i.e., physiological sensing data from older adults’ wearable devices) and photos (i.e., visual sensing data of infrastructure defects and neighborhood disorder from smartphones); this model will serve as a digital replica of the city that shows older adults’ collective distress—detected from bio-signals—and associated environmental conditions, thereby allowing us to continuously identify where, why, and to what extent older adults experience distress in their daily routine. The DTC model will be leveraged to design and simulate stress-aware interventions to promote older adults’ mobility and healthy behaviors (e.g., identify the least stressful first-and-last mile trip path to access transit).