Researchers develop a cervical myelopathy screening tool using non-contact sensor and machine learni …

researchers develop a cervical myelopathy screening tool using non contact sensor and machine learning

Sumary of Researchers develop a cervical myelopathy screening tool using non-contact sensor and machine learning:

  • Cervical myelopathy (CM) results from compression of the spinal cord in the neck and causes difficulty moving the fingers and unsteady gait.
  • Koji Fujita, a lecturer at Tokyo Medical and Dental University, and Yuta Sugiura, an associate professor at Keio University, combined a finger motion analysis technique using a non-contact sensor and machine learning to develop a simple screening tool for CM.
  • Related StoriesIn this study, the team focused on changes in finger motion caused by CM.
  • The test simply measures the number of grip and release actions and does not focus on changes in finger movements characteristic for patients with CM, such as wrist movements to compensate for difficulty moving the finger.
  • Leap Motion (Ultraleap Ltd.), a sensor capable of real-time measurement of finger movements, can be used to extract such movements more precisely.
  • The researchers expected that CM can be predicted using machine learning combined with the Leap Motion sensor.
  • A subject sitting in front of Leap Motion connected to a laptop computer with arms extended was instructed to grip and release the fingers 20 times as rapidly as possible.
  • Finger movements during this test were captured by the Leap Motion sensor, displayed on its screen in real time, and recorded as data.

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