16. Vision component Vision component (detection, classification, tracking): detect the person in the scene and to track his different movements over time.
17. R David E Mulin J Piano J Lee A Derreumeaux P Mallea F Bremont R Romdhane N Zouba V Joumier M Thonnat
25. Outdoor Experiment Setting Participants are asked to walk around the ring region in the NCKU campus. Examiner will walk with them and ask them the direction of the starting point in five fixed point on the way. During a straight path of forty meters, participants will wear non-invasive sensors to measure the gait information.
30. Outdoor Experiment Results Stride Detection Distance (m) Time (s) Number of Stride Mild AD (1) 40 42.53 34 Mild AD (2) 40 32.89 28 Mild AD (3) 40 43.26 34 Health Control (1) 40 36.47 32 Health Control (2) 40 36.25 31
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36. Outdoor Experiment Results Stride Frequency Single Stride Frequency (Hz) Walking Stride Frequency (Hz) Mild AD (1) 0.79 1.58 Mild AD (2) 0.84 1.68 Mild AD (3) 0.78 1.56 Health Control (1) 0.88 1.76 Health Control (2) 0.84 1.68
A physician or an examiner successively asks the participant to: Balance testing total score : /4
More than 8.7 seconds: 1 pt From 6.21 to 8.7 seconds: 2 pt From 4.82 to 6.2 seconds: 3 pt Less than 4.82 seconds: 4 pt  Speed of walk total score : /4 First chair stand: The participant stands up without help: Y/N  5 times chair stands: The participant stands up 5 times in a row without help: Y/N If the participant completed rises, time: sec  The participant hasn ’ t completed rises in less than 60 seconds: 0 pt 16.70 seconds or more: 1 pt From 13.70 to 16.69 seconds: 2 pt From 11.20 to 13.69 seconds: 3 pt 11.19 seconds or less: 4 pt  Transfer total score : /4  Short physical performance battery SUMMARY ORDINAL SCORE: /12
A physician or an examiner successively asks the participant to: Balance testing total score : /4
A physician or an examiner successively asks the participant to: Balance testing total score : /4
A physician or an examiner successively asks the participant to: Balance testing total score : /4
02/11/10 The proposed event recognition framework (the activity described on the previous paragraph) takes as input Video streams, A priori knowledge: This knowledge is composed of 3D geometric information (i.e. empty scene model, camera calibration) and pre-defined event - behavior models.