Ajalon: Simplifying the Authoring of Wearable Cognitive Assistants

TL;DR

Ajalon is an authoring toolchain that simplifies the development of wearable cognitive assistance applications, making it accessible to more developers and reducing development effort.

Contribution

It introduces a novel toolchain that lowers the skill barrier and accelerates the creation of WCA applications compared to traditional methods.

Findings

Ajalon significantly reduces development effort for WCA applications.

The toolchain enables faster creation of WCA applications.

Evaluation shows improved accessibility and efficiency in WCA development.

Abstract

Wearable Cognitive Assistance (WCA) amplifies human cognition in real time through a wearable device and low-latency wireless access to edge computing infrastructure. It is inspired by, and broadens, the metaphor of GPS navigation tools that provide real-time step-by-step guidance, with prompt error detection and correction. WCA applications are likely to be transformative in education, health care, industrial troubleshooting, manufacturing, and many other areas. Today, WCA application development is difficult and slow, requiring skills in areas such as machine learning and computer vision that are not widespread among software developers. This paper describes Ajalon, an authoring toolchain for WCA applications that reduces the skill and effort needed at each step of the development pipeline. Our evaluation shows that Ajalon significantly reduces the effort needed to create new WCA applications.