Presentation outline (maybe for report/paper too?)

#1: Hook (X stroke rate, Y death rate. Obviously rehab is important)

#2: Need (Current method relies on time only. Can we do better [qualitative]?)
#2a: show video of select exercises

#3: Approach
#3a: Use classification algorithms, but choose such that they give similarity scores
#3b: Obvious choices: DTW (cost) and HMM (log likelyhood). Others: ANN, decision tree: no score just classify. Naive Bayes: might work (probability)
#3c: This is basically gesture recognition
#3c-1: Problems: usually related to orientation and speed/amplitude
#3d: But we're solving a different problem because
#3d-1: We know the action a-priori, the amplitude is fixed (you have to perform the action as specified in the test) and we can still use time.
#3e: So this lets us work with the path data not just the acceleration
#3f: DTW on the path
#3g: HMM on the path - novel state space
#3g-1: and most importantly we can fix the orientation

#4: Implementation details
#4a: application
#4b: remote control
#4c: algorithms implemented on phone: DTW and HMM (log-likelyhood)
#4c-1: and 'energy' (current method used)

#5: Results
#5a: similar actions = similar scores
#5b: Show DTW and HMM graphs
#5c: Experiments with weights and 'poor motion'
#5d: W/ weights, not conclusion, with 'poor motion' pretty good results
#5d-1: Energy is inconclusive even for 'poor motion'
#5e: Ex. #5 added after other work to show the method wasn't tuned to ex. 1-4
#5f: 'High-box' tested to look at similar actions. Scores are similar, but we're not classifying, similar actions get similar scores.

#6: Conclusion Future work
#6a: We do a more quantitative measurement of the actions similarity
#6a-1: On an easy to use, inexpensive system (with remote control)
#6b: But our experiment is a bit un-scientific
#6c: So future directions are:
#6c-1: Can existing kinematic captures of patients be used to test our system
#6c-2: And of course if we could test it on a few actual patients.