Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease
Publication Year: 2020 / Journal: Sensors
Machine Learning
A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials
Publication Year: 2016 / Journal: Medical & Biological Engineering & Computing
Prediction of individual progression rate in Parkinson’s disease using clinical measures and biomechanical measures of gait and postural stability
Publication Year: 2019 / Journal: Electrical Engineering and Systems Science
Discriminating Progressive Supranuclear Palsy from Parkinson’s Disease using wearabletechnology andmachine learning.
Publication Year: 2019 / Journal: Gait & Posture
Decomposition of Complex Movements into Primitives for Parkinson’s Disease Assessment
Publication Year: 2018 / Journal: IBM Journal of Research and Development
Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection
Publication Year: 2022 / Journal: Journal of NeuroEngineering and Rehabilitation
Comparison of Machine Learning approaches for Classifying Upper Extremity Tasks in Individuals Post-Stroke
Publication Year: 2020 / Journal: IEEE
iGait: Vision-based Low-Cost, Reliable Machine Learning Framework for Gait Abnormality Detection
Publication Year: 2017 / Journal: Dissertation
Objective Diagnosis of ADHD Using IMUs
Publication Year: 2014 / Journal: Medical Engineering & Physics
Transparent Quality Optimization for Machine Learning-Based Regression in Neurology
Publication Year: 2022 / Journal: MDPI Journal of Personalized Medicine