1. Myroslav Bachynskyi Gregorio Palmas Antti Oulasvirta Tino Weinkauf http://resources.mpi-inf.mpg.de/coactivationclustering Informing the Design of Novel Input Methods with Muscle Coactivation Clustering 2. Motivation 2 3. We inform novel input methods by muscle activation based clustering 3 Muscle usage 4. Input methods assume a 3D movement space 4 5. Fitts law assumes that the movement space is uniform 5 MT = + log2(1 + ) W D Non-uniformity is important for interfaces with large movement space 6. We define neuromechanical equivalence classes based on muscle co-activation 6 http://www.nature.com/nrn/journal/v5/n7/images/nrn1427-i1.jpg A B C A B A C 7. Research objectives 1. Identify equivalence classes of movements for any given movement space 2. Describe performance and ergonomics characteristics per movement cluster 3. Inform design of an input methods 7 All movements equivalent Every movement exclusiveDesired clustering: few clusters easily interpretable covers all statistically significant effects 8. Background Non-uniformity Performance models Muscle dynamics Movement location Movement direction Movement amplitude Speed-accuracy trade- off models: Uniform Non-uniform, but non-physiological Movement location Movement direction Trajectory profile Velocity profile impact Muscle recruitment impact Movement amplitude Movement direction Muscle activation pattern impact [Freund1978, Soechting1995, Adamovich1999, Gribble2003, Baud-Bovy1998, Caminity1990, etc.] [Fitts1954, MacKenzie1992, Grossman2004, Cha2013, Plamondon1997, etc.] [Gielen1985, Cooke1994, Koshland1994, Flanders1991, Wierzbicka1985, etc.] 8 9. Method 9 10. Muscle coactivation clustering approach 10 1. Motion capture Markers in space 2. Inverse Kinematics Skeletal coordinates 3. Static Optimization Muscle activations 4. Clustering Movement clusters 5. Analysis Clusters specification 11. 1. Motion capture study covers the whole movement space 11 12. 2 and 3: MoCap and biomechanical simulation yield muscle activations and ergonomics indices 12 13. Muscle activation pattern for a movement is a multidimensional vector of activation values 13 Time, ms Activation level Acceleration Deceleration Muscles 0 1 100 200 14. Results 14 15. Results of hierarchical clustering 15 16. Dendrogram branching of resulting clusters has semantic grounds 16 17. Each cluster is distinct with respect to location and orientation in the 3D space 17 18. Clustering improves fit of Fitts models on average by 2% 18 19. 1072 Performance differences between clusters reach up to 37% 19 0 1 2 3 4 5 6 7 All 1 3 4 5 6 8 9 11 Clusters Throughput 20. 2 7 10 The clusters exhibit up to 4-fold differences in total muscle activation 20 All 1 3 4 5 6 8 9 11 Total muscle activation Clusters 21. Depending on a cluster different muscle groups are recruited 21 22. How to apply the clusters to a design task 22 1. Identify movements involved 2. Map them to 3D space 3. Scope their direction and length 4. Identify the best- matching cluster 5. Look at ergonomics and performance of the selected cluster 6. Decide whether such properties suit your input method 23. An example: menu placement for public display 23 Muscle usage 24. Summary Graduating in 2015 More on the topic: Presentation: Hall 401, 16:30 Demo: Booth H3, morning break tomorrow