ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications 12/06/2019
Editor's comments and enhancements are shown in green. [✓ Reviewed]
The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:
 Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
 Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
 Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
 Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
 Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
 Functional aspects, trustworthiness, and societal concerns
 AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.
Use Case Name:
A judging support system for gymnastics using 3D sensing
To support judgement of difficult element by high-level and high-speed.
Short Description (up to 150 words)
We have been developing a judging support system for artistic gymnastics to enhance accuracy and fairness in judging. We developed a skeleton recognition technique using the learned model that we trained using a large amount of depth images of gymnastics created from CG in advance. With this technology, it is possible to recognize a human 3D skeleton from depth image.
In gymnastics, wrong scoring is a problem, when it is difficult to judge by high-level and high-speed. Therefore, 3D sensing technology is required to reduce burden of referee by recognizing skeleton of gymnast. We developed a technique to recognize heatmaps of body parts using the learned model that we trained using a large amount of depth images of gymnastics created from CG in advance. We calculate 3D skeleton position using heatmaps of body parts. With this technology, it is possible to recognize a human 3D skeleton from depth image.
Louise Radnofsky. The Robots Are Coming (to Judge Gymnastics). The international gymnastics federation may add new technology in time for the 2020 Olympics—and pave the way for artificial intelligence in judged sports. The Wall Street Journal, Aug. 23, 2019.
Peer-reviewed scientific/technical publications on AI applications (e.g. ).
Patent documents describing AI solutions (e.g. , ).
Technical reports or presentations by renowned AI experts (e.g. )
High quality company whitepapers and presentations
Publicly accessible sources with sufficient detail
This list is not exhaustive. Other credible sources may be acceptable as well.
Examples of credible sources:
 B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.
 S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.
 M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.
 J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013