Deploying Feature Engineering and Machine Learning in Sensor-Based Assessments

WATCH-PD: Detecting Early-Stage Parkinson’s Status

Wearable and sensor technologies empower researchers to capture the full spectrum of patient health—formerly inaccessible, but now available through the computing technology of both clinical-grade as well as consumer-grade electronics.

Listen to Clinical ink Principal Scientist David Anderson review the e-Poster “WATCH-PD: Detecting Early-Stage Parkinson’s Disease Status Using Feature Engineering and Machine Learning in Sensor-Based Assessments.” The work is part of the International Congress of Parkinson’s Disease and Movement Disorders program, held in Madrid, Spain from September 15-18, 2022.

Walk through the research with Anderson, and learn how feature engineering and machine learning modeling allowed the team to accurately predict early Parkinson’s Disease (PD) status with 92.3% accuracy, 90% sensitivity, and 100% specificity, across environmental and temporal contexts.

You’ll learn more about:

  • WATCH-PD study design
  • The Clinical ink technology that allows for this innovative data collection, including a mobile app that integrates activities utilizing mobile, sensor and wearables technology
  • Feature engineering and machine learning modeling processes that allow for improved diagnostic accuracy and that could lead to promising future patient screening tools


David Anderson, Ph.D.
Principal Scientist,
Clinical ink

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