Post on 28-Mar-2021
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Action Recognition on Distributed Body Sensor Networksvia Sparse Representation
Allen Y. Yang<yang@eecs.berkeley.edu>
June 28, 2008. CVPR4HB
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
New Paradigm of Distributed Pattern Recognition
Centralized Recognition
powerful processors(virtually) unlimited memory
(virtually) unlimited bandwidthobservations in controlled environment
simple sensor management
Distributed Recognition
mobile processorslimited onboard memory
band-limited communicationshigh-percentage of occlusion/outliers
complex sensor networks
Main constraints for sensor network applications
1 Conserve power consumption.
2 Adapt to network configuration changes.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
New Paradigm of Distributed Pattern Recognition
Centralized Recognition
powerful processors(virtually) unlimited memory
(virtually) unlimited bandwidthobservations in controlled environment
simple sensor management
Distributed Recognition
mobile processorslimited onboard memory
band-limited communicationshigh-percentage of occlusion/outliers
complex sensor networks
Main constraints for sensor network applications
1 Conserve power consumption.
2 Adapt to network configuration changes.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
d-WAR: Distributed Wearable Action Recognition
ArchitectureEight sensors on human body.
Locations are given and fixed.
Each sensor carries triaxial accelerometer and biaxial gyroscope.
Sampling frequency: 20Hz.
Figure: Readings from 8 x-axis accelerometers and x-axis gyroscopes for a stand-kneel-stand sequence.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Challenges
1 Simultaneous segmentation and classification.
2 Individual sensors not sufficient to classify full-body motions.Single sensors on the upper body can not recognize lower body motions.Vice Versa.
3 Simulate sensor failure and network congestion by different subsets of active sensors.
4 Identity independence:
Figure: Same actions performed by two subjects.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Challenges
1 Simultaneous segmentation and classification.2 Individual sensors not sufficient to classify full-body motions.
Single sensors on the upper body can not recognize lower body motions.Vice Versa.
3 Simulate sensor failure and network congestion by different subsets of active sensors.
4 Identity independence:
Figure: Same actions performed by two subjects.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Challenges
1 Simultaneous segmentation and classification.2 Individual sensors not sufficient to classify full-body motions.
Single sensors on the upper body can not recognize lower body motions.Vice Versa.
3 Simulate sensor failure and network congestion by different subsets of active sensors.
4 Identity independence:
Figure: Same actions performed by two subjects.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Challenges
1 Simultaneous segmentation and classification.2 Individual sensors not sufficient to classify full-body motions.
Single sensors on the upper body can not recognize lower body motions.Vice Versa.
3 Simulate sensor failure and network congestion by different subsets of active sensors.
4 Identity independence:
Figure: Same actions performed by two subjects.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Outline
1 Overview: Classification framework via `1-minimization.
2 Individual sensor: Obtains limited classification ability and becomes active only when certainevents are locally detected.
3 Global classifier: Adapts to the change of active sensors in network and boost multi-sensorrecognition.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparse Representation
Sparsity
A signal is sparse if most of its coefficients are (approximately) zero.
1 Sparsity in frequency domain
Figure: 2-D DCT transform.
2 Sparsity in spatial domain
Figure: Gene microarray data.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparse Representation
Sparsity
A signal is sparse if most of its coefficients are (approximately) zero.
1 Sparsity in frequency domain
Figure: 2-D DCT transform.
2 Sparsity in spatial domain
Figure: Gene microarray data.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparsity in human visual cortex [Olshausen & Field 1997, Serre & Poggio 2006]
1 Feed-forward: No iterative feedback loop.2 Redundancy: Average 80-200 neurons for each feature representation.3 Recognition: Information exchange between stages is not about individual neurons, but
rather how many neurons as a group fire together.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparsity in human visual cortex [Olshausen & Field 1997, Serre & Poggio 2006]
1 Feed-forward: No iterative feedback loop.2 Redundancy: Average 80-200 neurons for each feature representation.3 Recognition: Information exchange between stages is not about individual neurons, but
rather how many neurons as a group fire together.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparsity and `1-Minimization
1 “Black gold” age [Claerbout & Muir 1973, Taylor, Banks & McCoy 1979]
Figure: Deconvolution of spike train.
2 Basis pursuit [Chen & Donoho 1999]: Given y = Ax and x unknown,
x∗ = arg min ‖x‖1, subject to y = Ax
3 The Lasso (least absolute shrinkage and selection operator) [Tibshirani 1996]
x∗ = arg min ‖x‖1, subject to ‖y − Ax‖2 ≤ ε
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparsity and `1-Minimization
1 “Black gold” age [Claerbout & Muir 1973, Taylor, Banks & McCoy 1979]
Figure: Deconvolution of spike train.
2 Basis pursuit [Chen & Donoho 1999]: Given y = Ax and x unknown,
x∗ = arg min ‖x‖1, subject to y = Ax
3 The Lasso (least absolute shrinkage and selection operator) [Tibshirani 1996]
x∗ = arg min ‖x‖1, subject to ‖y − Ax‖2 ≤ ε
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparse Representation Encodes Membership
1 NotationTraining: For K classes, collect training samples {v1,1, · · · , v1,n1
}, · · · , {vK,1, · · · , vK,nK} ∈ RD .
Test: Present a new y ∈ RD , solve for label(y) ∈ [1, 2, · · · , K ].
2 Subspace model: Assume y belongs to Class i :
y = αi,1vi,1 + αi,2vi,2 + · · ·+ αi,n1vi,ni
,= Aiαi ,
where Ai = [vi,1, vi,2, · · · , vi,ni].
3 Nevertheless, Class i is the unknown variable we need to solve:
Sparse representation y = [A1,A2, · · · ,AK ]
α1α2
...αK
= Ax.
Sparse representation encodes membership!
x0 = [ 0 ··· 0 αTi 0 ··· 0 ]T ∈ Rn
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparse Representation Encodes Membership
1 NotationTraining: For K classes, collect training samples {v1,1, · · · , v1,n1
}, · · · , {vK,1, · · · , vK,nK} ∈ RD .
Test: Present a new y ∈ RD , solve for label(y) ∈ [1, 2, · · · , K ].
2 Subspace model: Assume y belongs to Class i :
y = αi,1vi,1 + αi,2vi,2 + · · ·+ αi,n1vi,ni
,= Aiαi ,
where Ai = [vi,1, vi,2, · · · , vi,ni].
3 Nevertheless, Class i is the unknown variable we need to solve:
Sparse representation y = [A1,A2, · · · ,AK ]
α1α2
...αK
= Ax.
Sparse representation encodes membership!
x0 = [ 0 ··· 0 αTi 0 ··· 0 ]T ∈ Rn
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparse Representation Encodes Membership
1 NotationTraining: For K classes, collect training samples {v1,1, · · · , v1,n1
}, · · · , {vK,1, · · · , vK,nK} ∈ RD .
Test: Present a new y ∈ RD , solve for label(y) ∈ [1, 2, · · · , K ].
2 Subspace model: Assume y belongs to Class i :
y = αi,1vi,1 + αi,2vi,2 + · · ·+ αi,n1vi,ni
,= Aiαi ,
where Ai = [vi,1, vi,2, · · · , vi,ni].
3 Nevertheless, Class i is the unknown variable we need to solve:
Sparse representation y = [A1,A2, · · · ,AK ]
α1α2
...αK
= Ax.
Sparse representation encodes membership!
x0 = [ 0 ··· 0 αTi 0 ··· 0 ]T ∈ Rn
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparse Representation Encodes Membership
1 NotationTraining: For K classes, collect training samples {v1,1, · · · , v1,n1
}, · · · , {vK,1, · · · , vK,nK} ∈ RD .
Test: Present a new y ∈ RD , solve for label(y) ∈ [1, 2, · · · , K ].
2 Subspace model: Assume y belongs to Class i :
y = αi,1vi,1 + αi,2vi,2 + · · ·+ αi,n1vi,ni
,= Aiαi ,
where Ai = [vi,1, vi,2, · · · , vi,ni].
3 Nevertheless, Class i is the unknown variable we need to solve:
Sparse representation y = [A1,A2, · · · ,AK ]
α1α2
...αK
= Ax.
Sparse representation encodes membership!
x0 = [ 0 ··· 0 αTi 0 ··· 0 ]T ∈ Rn
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Distributed Action Recognition
1 Training samples: manually segment and normalize to duration h.
2 On each sensor node i , stack training actions into vector form
vi = [x(1), · · · , x(h), y(1), · · · , y(h), z(1), · · · , z(h), θ(1), · · · , θ(h), ρ(1), · · · , ρ(h)]T ∈ R5h
3 Full body motion
Training sample: v =
( v1
...v8
)Test sample: y =
y1
...y8
∈ R8·5h
4 Action subspace: If y is from Class i ,
y =
y1
...y8
= αi,1
( v1
...v8
)1
+ · · ·+ αi,ni
( v1
...v8
)ni
= Aiαi .
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Distributed Action Recognition
1 Training samples: manually segment and normalize to duration h.
2 On each sensor node i , stack training actions into vector form
vi = [x(1), · · · , x(h), y(1), · · · , y(h), z(1), · · · , z(h), θ(1), · · · , θ(h), ρ(1), · · · , ρ(h)]T ∈ R5h
3 Full body motion
Training sample: v =
( v1
...v8
)Test sample: y =
y1
...y8
∈ R8·5h
4 Action subspace: If y is from Class i ,
y =
y1
...y8
= αi,1
( v1
...v8
)1
+ · · ·+ αi,ni
( v1
...v8
)ni
= Aiαi .
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Distributed Action Recognition
1 Training samples: manually segment and normalize to duration h.
2 On each sensor node i , stack training actions into vector form
vi = [x(1), · · · , x(h), y(1), · · · , y(h), z(1), · · · , z(h), θ(1), · · · , θ(h), ρ(1), · · · , ρ(h)]T ∈ R5h
3 Full body motion
Training sample: v =
( v1
...v8
)Test sample: y =
y1
...y8
∈ R8·5h
4 Action subspace: If y is from Class i ,
y =
y1
...y8
= αi,1
( v1
...v8
)1
+ · · ·+ αi,ni
( v1
...v8
)ni
= Aiαi .
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Distributed Action Recognition
1 Training samples: manually segment and normalize to duration h.
2 On each sensor node i , stack training actions into vector form
vi = [x(1), · · · , x(h), y(1), · · · , y(h), z(1), · · · , z(h), θ(1), · · · , θ(h), ρ(1), · · · , ρ(h)]T ∈ R5h
3 Full body motion
Training sample: v =
( v1
...v8
)Test sample: y =
y1
...y8
∈ R8·5h
4 Action subspace: If y is from Class i ,
y =
y1
...y8
= αi,1
( v1
...v8
)1
+ · · ·+ αi,ni
( v1
...v8
)ni
= Aiαi .
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparse Representation
1 Nevertheless, label(y) = i is the unknown membership function to solve:
Sparse Representation: y =[A1 A2 · · · AK
]α1
α2
...αK
= Ax ∈ R8·5h.
2 One solution: x = [0, 0, · · · , αTi , 0, · · · , 0]T .
Sparse representation encodes membership.
3 Two problems:Directly solving the linear system is intractable.Seeking the sparsest solution.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparse Representation
1 Nevertheless, label(y) = i is the unknown membership function to solve:
Sparse Representation: y =[A1 A2 · · · AK
]α1
α2
...αK
= Ax ∈ R8·5h.
2 One solution: x = [0, 0, · · · , αTi , 0, · · · , 0]T .
Sparse representation encodes membership.
3 Two problems:Directly solving the linear system is intractable.Seeking the sparsest solution.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Sparse Representation
1 Nevertheless, label(y) = i is the unknown membership function to solve:
Sparse Representation: y =[A1 A2 · · · AK
]α1
α2
...αK
= Ax ∈ R8·5h.
2 One solution: x = [0, 0, · · · , αTi , 0, · · · , 0]T .
Sparse representation encodes membership.
3 Two problems:Directly solving the linear system is intractable.Seeking the sparsest solution.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Dimensionality Reduction
1 Construct Fisher/LDA features Ri ∈ R10×5h on each node i :
yi = Ri yi = RiAi x = Ai x ∈ R10
2 Globally y1
...y8
=
R1 ··· 0
.... . .
...0 ··· R8
y1
...y8
= RAx = Ax ∈ R8·10
3 During the transformation, the data matrix A and x remain unchanged.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Dimensionality Reduction
1 Construct Fisher/LDA features Ri ∈ R10×5h on each node i :
yi = Ri yi = RiAi x = Ai x ∈ R10
2 Globally y1
...y8
=
R1 ··· 0
.... . .
...0 ··· R8
y1
...y8
= RAx = Ax ∈ R8·10
3 During the transformation, the data matrix A and x remain unchanged.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Dimensionality Reduction
1 Construct Fisher/LDA features Ri ∈ R10×5h on each node i :
yi = Ri yi = RiAi x = Ai x ∈ R10
2 Globally y1
...y8
=
R1 ··· 0
.... . .
...0 ··· R8
y1
...y8
= RAx = Ax ∈ R8·10
3 During the transformation, the data matrix A and x remain unchanged.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Seeking Sparsest Solution: `1-Minimization
1 Ideal solution: `0-minimization
(P0) x∗ = arg minx‖x‖0 s.t. y = Ax.
where ‖ · ‖0 simply counts the number of nonzero terms.However, such solution is generally NP-hard.
2 Compressed sensing: under mild condition, equivalence relation
(P1) x∗ = arg minx‖x‖1 s.t. y = Ax.
3 `1-Ball
`1-Minimization is convex.
Solution equal to `0-minimization.
Reference: Robust face recognition via sparse representation. (in press) PAMI, 2008.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Seeking Sparsest Solution: `1-Minimization
1 Ideal solution: `0-minimization
(P0) x∗ = arg minx‖x‖0 s.t. y = Ax.
where ‖ · ‖0 simply counts the number of nonzero terms.However, such solution is generally NP-hard.
2 Compressed sensing: under mild condition, equivalence relation
(P1) x∗ = arg minx‖x‖1 s.t. y = Ax.
3 `1-Ball
`1-Minimization is convex.
Solution equal to `0-minimization.
Reference: Robust face recognition via sparse representation. (in press) PAMI, 2008.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Seeking Sparsest Solution: `1-Minimization
1 Ideal solution: `0-minimization
(P0) x∗ = arg minx‖x‖0 s.t. y = Ax.
where ‖ · ‖0 simply counts the number of nonzero terms.However, such solution is generally NP-hard.
2 Compressed sensing: under mild condition, equivalence relation
(P1) x∗ = arg minx‖x‖1 s.t. y = Ax.
3 `1-Ball
`1-Minimization is convex.
Solution equal to `0-minimization.
Reference: Robust face recognition via sparse representation. (in press) PAMI, 2008.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Distributed Recognition Framework
1 Distributed Sparse Representation y1
...y8
= R
( v1
...v8
)1
, · · · ,( v1
...v8
)n
x⇔
y1=R1(v1,1,··· ,v1,n)x
...y8=R8(v8,1,··· ,v8,n)x
2 Local classifier: For each note i , solve
yi = Ri
(vi,1, · · · , vi,n
)x ∈ R10
3 Adaptive global classifier: Suppose only first L sensors are available (L ≤ 8): y1
...yL
=
R1 ··· 0
.... . .
...0 ··· RL
v1
...vL
1
, · · · ,
v1
...vL
n
x ∈ R10L
4 The representation x and training matrix A remain invariant.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Distributed Recognition Framework
1 Distributed Sparse Representation y1
...y8
= R
( v1
...v8
)1
, · · · ,( v1
...v8
)n
x⇔
y1=R1(v1,1,··· ,v1,n)x
...y8=R8(v8,1,··· ,v8,n)x
2 Local classifier: For each note i , solve
yi = Ri
(vi,1, · · · , vi,n
)x ∈ R10
3 Adaptive global classifier: Suppose only first L sensors are available (L ≤ 8): y1
...yL
=
R1 ··· 0
.... . .
...0 ··· RL
v1
...vL
1
, · · · ,
v1
...vL
n
x ∈ R10L
4 The representation x and training matrix A remain invariant.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Distributed Recognition Framework
1 Distributed Sparse Representation y1
...y8
= R
( v1
...v8
)1
, · · · ,( v1
...v8
)n
x⇔
y1=R1(v1,1,··· ,v1,n)x
...y8=R8(v8,1,··· ,v8,n)x
2 Local classifier: For each note i , solve
yi = Ri
(vi,1, · · · , vi,n
)x ∈ R10
3 Adaptive global classifier: Suppose only first L sensors are available (L ≤ 8): y1
...yL
=
R1 ··· 0
.... . .
...0 ··· RL
v1
...vL
1
, · · · ,
v1
...vL
n
x ∈ R10L
4 The representation x and training matrix A remain invariant.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Distributed Recognition Framework
1 Distributed Sparse Representation y1
...y8
= R
( v1
...v8
)1
, · · · ,( v1
...v8
)n
x⇔
y1=R1(v1,1,··· ,v1,n)x
...y8=R8(v8,1,··· ,v8,n)x
2 Local classifier: For each note i , solve
yi = Ri
(vi,1, · · · , vi,n
)x ∈ R10
3 Adaptive global classifier: Suppose only first L sensors are available (L ≤ 8): y1
...yL
=
R1 ··· 0
.... . .
...0 ··· RL
v1
...vL
1
, · · · ,
v1
...vL
n
x ∈ R10L
4 The representation x and training matrix A remain invariant.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Rejection of Invalid Actions (Segmentation)
1 Multiple segmentation hypothesis
2 Outlier rejection
Outlier Rejection
When `1-solution is not sparse or concentrated to one subspace, the test sample is invalid.
Sparsity Concentration Index: SCI(x).
=K ·maxi ‖δi (x)‖1/‖x‖1 − 1
K − 1∈ [0, 1].
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Rejection of Invalid Actions (Segmentation)
1 Multiple segmentation hypothesis
2 Outlier rejection
Outlier Rejection
When `1-solution is not sparse or concentrated to one subspace, the test sample is invalid.
Sparsity Concentration Index: SCI(x).
=K ·maxi ‖δi (x)‖1/‖x‖1 − 1
K − 1∈ [0, 1].
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Rejection of Invalid Actions (Segmentation)
1 Multiple segmentation hypothesis
2 Outlier rejection
Outlier Rejection
When `1-solution is not sparse or concentrated to one subspace, the test sample is invalid.
Sparsity Concentration Index: SCI(x).
=K ·maxi ‖δi (x)‖1/‖x‖1 − 1
K − 1∈ [0, 1].
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Experiment
Precision vs Recall:
Sensors 2 7 2,7 1,2,7 1- 3, 7,8 1- 8
Prec [%] 89.8 94.6 94.4 92.8 94.6 98.8Rec [%] 65 61.5 82.5 80.6 89.5 94.2
Segmentation results using all 8 sensors:
(a) Stand-Sit-Stand (b) Sit-Lie-Sit
(c) Rotate-Left (d) Go-Downstairs
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Experiment
Precision vs Recall:
Sensors 2 7 2,7 1,2,7 1- 3, 7,8 1- 8
Prec [%] 89.8 94.6 94.4 92.8 94.6 98.8Rec [%] 65 61.5 82.5 80.6 89.5 94.2
Segmentation results using all 8 sensors:
(a) Stand-Sit-Stand (b) Sit-Lie-Sit
(c) Rotate-Left (d) Go-Downstairs
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Confusion Tables
(a) 1- 8
(b) 7
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Conclusion
1 Mixture subspace model: 10-D action spaces suffice for data communication.
2 Accurate recognition via sparse representationFull-body network: 99% accuracy with 95% recall.Keep one on upper body and one on lower body: 94% accuracy and 82% recall.Reduce to single sensors: 90% accuracy.
3 ApplicationsBeyond Wii controllers and iPhones.
Eldertech: falling detection, mobility monitoring.
Energy Expenditure: lifestyle-related chronic diseases.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Conclusion
1 Mixture subspace model: 10-D action spaces suffice for data communication.2 Accurate recognition via sparse representation
Full-body network: 99% accuracy with 95% recall.Keep one on upper body and one on lower body: 94% accuracy and 82% recall.Reduce to single sensors: 90% accuracy.
3 ApplicationsBeyond Wii controllers and iPhones.
Eldertech: falling detection, mobility monitoring.
Energy Expenditure: lifestyle-related chronic diseases.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Conclusion
1 Mixture subspace model: 10-D action spaces suffice for data communication.2 Accurate recognition via sparse representation
Full-body network: 99% accuracy with 95% recall.Keep one on upper body and one on lower body: 94% accuracy and 82% recall.Reduce to single sensors: 90% accuracy.
3 ApplicationsBeyond Wii controllers and iPhones.
Eldertech: falling detection, mobility monitoring.
Energy Expenditure: lifestyle-related chronic diseases.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Conclusion
1 Mixture subspace model: 10-D action spaces suffice for data communication.2 Accurate recognition via sparse representation
Full-body network: 99% accuracy with 95% recall.Keep one on upper body and one on lower body: 94% accuracy and 82% recall.Reduce to single sensors: 90% accuracy.
3 ApplicationsBeyond Wii controllers and iPhones.
Eldertech: falling detection, mobility monitoring.
Energy Expenditure: lifestyle-related chronic diseases.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Conclusion
1 Mixture subspace model: 10-D action spaces suffice for data communication.2 Accurate recognition via sparse representation
Full-body network: 99% accuracy with 95% recall.Keep one on upper body and one on lower body: 94% accuracy and 82% recall.Reduce to single sensors: 90% accuracy.
3 ApplicationsBeyond Wii controllers and iPhones.
Eldertech: falling detection, mobility monitoring.
Energy Expenditure: lifestyle-related chronic diseases.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
http://www.eecs.berkeley.edu/~yang/software/WAR/index.html
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition
Introduction Sparse Representation Distributed Pattern Recognition Conclusion
Acknowledgments
Collaborators
Berkeley: Ruzena Bajcsy, Shankar Sastry, Sameer Iyengar
Cornell: Philip Kuryloski
UT-Dallas: Roozbeh Jafari
Funding Support
NSF TRUST Center
ARO MURI: Heterogeneous Sensor Networks (HSN)
Telecom Italia Lab at Berkeley
References
Robust face recognition via sparse representation. (in press) PAMI, 2008.
Donoho. For most large underdetermined systems of equations, the minimal `1-normnear-solution approximates the sparsest near-solution. 2004.
Candes. Compressive sampling. 2006.
Donoho. Neighborly polytopes and sparse solution of underdetermined linear equations.2004.
Allen Y. Yang <yang@eecs.berkeley.edu> Wearable Action Recognition