Cindy Xiong, Chase Stokes, Yea-Seul Kim, Steven Franconeri
Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation
IEEE transactions on visualization and computer graphics
goal parkinsons disease pd lead gait impairment freezing gait fog recent advances cueing technologies enhanced mobility pd patients sensor technology machine learning offer realtime detection ondemand cueing existing systems limited usage smartphones sensors cueing devices data processing avoiding aim improving usability robustness detection delay methods present new technical solution runs detection cueing algorithms directly sensing cueing devices bypassing smartphone solution relies edge computing devices hardware wearable system consists single inertial sensor control stimulator enables machinelearningbased fog detection classifying foot motion phases either normal fogaffected demonstrate systems functionality safety ondemand gaitsynchronous electrical cueing two patients performing freezing gait assessments references motion phases fog episodes videoannotated results analysis confirms adequate gait phase fog detection performance mobility assistant detected foot motions rate 94 classified accuracy 84 normal fogaffected fog detection delay mainly defined footmotion duration delay existing slidingwindow approaches conclusions direct computing sensor cueing devices ensures robust detection fogaffected motions demand cueing synchronized gait proposed solution easily adopted sensor cueing modalities