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:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 62 ID: Use Case Name: A judging support system for gymnastics using 3D sensing
Application
Domain
ICT
Deployment
Model
On-premise systems
StatusPoC
ScopeSkeleton recognition for gymnastics
Objective(s)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.
Complete Description 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.
StakeholdersFederation International Gymnastics(FIG)
Stakeholders'
Assets, Values
Systems'
Threats &
Vulnerabilities
Key
Performance
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives




AI Features Task(s)Recognition
Method(s)Deep learning
Hardware
TopologyCNN
Terms &
Concepts Used
Deep learning, Convolution neural network, training, training data set
Standardization
Opportunities
Requirements
Challenges
& Issues
Challenges: Recognize skeleton of all gymnastics element. Issues: Recognize 3D skeleton in gymnastics that are complex movements from depth image.
Societal Concerns Description Positive: Fairness of scoring, reducing burden of referee, and technical improvement of gymnast. Negative:
SDGs to
be achieved
Industry, Innovation, and Infrastructure
Data Characteristics
Description Depth images, 2D data of skeleton
Source Motion capture
Type Images
Volume (size)
Velocity Non-real time
Variety Single dataset
Variability
(rate of change)
Static
Quality High
Scenario Conditions
No. Scenario
Name
Scenario
Description
Triggering Event Pre-condition Post-Condition
1 Training Train a model with training data set Evaluation
2 Evaluation Evaluate whether the trained model can be deployed cg data Training/Retraining Execution
3 Execution Recognize real data gained 3D laser sensor Evaluation Retraining
4 Retraining Retrain a model with added training data set. Execution
Training Scenario Name:
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Process/Activity
Primary
Actor
Description of
Process/Activity
Requirement






Specification of retraining data
References
No. Type Reference Status Impact of
use case
Originator
Organization
Link
1 Press release Fujitsu Link
2 News article 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. Fujitsu Link

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • 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:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] 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.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] 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