Business Cases

• Observer evaluation support software using item response theory - development

  We developed an observational assessment support software that can take into account the rater's level of severity by using item response theory.
  Severity refers to the strength of the rater's tendency to rate the performance strictly (or leniently).

  This software consists of an evaluation tool and a management tool.

  Assessment tools are software used to assess a subject, which takes into account the severity of the evaluator's own severity and allows for a common scale that is independent of the evaluator.

  Management tools can create evaluation tools with parameters tuned to take into account the severity of the evaluator, using a statistical model that takes severity into account. It can also quantitatively determine whether each evaluator is able to evaluate from the same perspective as the other evaluators. Using the results of this analysis, licences can be issued to give access to the evaluation tool to evaluators whose evaluations meet the criteria for quality.

  >

  Technologies

  • Python
  • Item Response Theory
  • Rasch Model
  • Social Psychology
  • Psychometric Scales
  • Bayesian Models
  • Multiple OS
  • JAX
  • AMPS

• High-speed detection of obstacles when traveling with moving objects - R&D

  As an assistive technology to help moving objects avoid hazards during automatic driving, we have developed an algorithm to detect obstacles at high speed from images acquired by a camera in the direction of travel.

  The algorithm had two requirements

  (1) High speed processing
  (2) Ability to adapt to changing daylight and lighting conditions
  

  The algorithm used was the grid partitioning pattern matching method.
  The pattern matching algorithm implements logic to find robust correlation coefficients for illumination change, shape deformation, and magnification change.

  This has confirmed that obstacles can be detected at high speed even in situations where daylight and proving conditions change.

  

  Technologies

  • Zero Mean Normalized Cross-correlation
  • Incremental Code Correlation
  • C/C++
  • C#

• BMI framework - development

  We have developed a brain-machine interface (BMI) framework that detects targeted brain activity from brain signals such as electroencephalogram (EEG) in real time and outputs feedback signals to operate external devices.

  Since real-time performance is important for BMI, a C++ multi-threading mechanism was used to minimize the delay time to feedback. The framework is designed to be flexible enough to incorporate MATLAB or Python scripts which implemented the algorithms for brain signal preprocessing and brain activity detection, depending on the experiment. The BMI also uses the lab streaming layer communication technology to provide multimodal input from multiple devices (e.g., eye-tracking devices) in addition to brain signals.

  

  Technologies

  • Brain-machine interface (BMI)
  • Electroencephalogram (EEG)
  • C++
  • MATLAB
  • Python
  • Lab streaming layer (LSL)

See more cases