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Wearable technologies: what's brewing in the lab?
1. Wearable technologies:
what's brewing in the lab?
http://www.sussex.ac.uk/strc/research/wearable
Dr. Daniel Roggen
Wearable Technologies Lab
Sensor Technologies Research Centre
University of Sussex
2. 500 London police officers will be equipped
with Taser wearable cameras
[1] http://thenextweb.com/uk/2014/05/08/500-london-police-officers-will-equipped-taser-wearable-
cameras-today/
3. Naylor, G.: Modern hearing aids and future development trends,
http://www.lifesci.sussex.ac.uk/home/Chris_Darwin/BSMS/Hearing%20Aids/Naylor.ppt
5. 1961: Thorp&Shannon’s wearable computer
Edward O. Thorp. The Invention of the First Wearable Computer, Proc Int Symp on Wearable Computers, 1998
+44% wins
• Cigarette pack size
• Toe-triggered timer
• Audio feedback
• 12 transistors
6. Marion, Heinsen, Chin, Helmso. Wrist instrument opens new dimension in personal information, Hewlett-Packard Journal, 1977
• « It’s a digital electronic wristwatch, a
personal calculator, an alarm clock, a
stopwatch, a timer, and a 200-year
calendar, and its functions can interact to
produce previously unavailable results »
• 38K transistors
• 20 uW / 36mW screen off/on
• Reliability
– « Shock and vibrations, temperature and
humidity changes, body chemicals,
abrasive dust, and constant friction
against clothing presented a challenge to
the designers »
• Design
– « Requirements for a small and visually
pleasing product imposed additional
difficulties rarely encountered at HP»
10. Flexible and stretchable electronics
(Munzenrieder et al. University of Sussex)
Bent finger Straight finger
Accordion-like electronics
for electronic skin
17. Vannevar Bush, As we may think. Life magazine, 1945
Cyclop camera
Speech recognition
Access to all human
knowledge (Memex)
“Let us project this trend ahead to a logical, if not inevitable,
outcome”
18. Wearable computer = smart assistant
* Augment and mediate interactions
+ No barrier between you and the world
* Constant access to information
• Self-contained / personal
× Micro-interactions
• Proactive / implicit interaction
* Sense and model context
* Adapt interaction modalities based on context
+ Starner, ISWC 2013 Closing Keynote, September 2013, Zürich
• Starner, The challenges of wearable computing: Part 1, IEEE Pervasive Computing Magazine, 2001
x Ashbrook, Enabling mobile microinteractions, PhD, 2010
19. •What did I do yesterday?.....
• What am I doing in the kitchen?....
• You went to the supermarket, and enjoyed a coffee with Lisa
• If you want to cook spaghettis, think of heating the water
Recognition of human activities and their context
Activity diarisation, memory augmentation
(e.g. memory assistant for dementia)
25. The OPPORTUNITY dataset for reproducible research
(avail. on UCI ML repository)
Activity of daily living
• 12 subjects
• > 30'000 interaction primitives
(object, environment)
Roggen et al., Collecting complex activity datasets in highly rich networked sensor environments, INSS 2010
http://opportunity-project.eu/challengeDataset
http://vimeo.com/8704668
Sensor rich
• Body, objects, environment
• 72 sensors (28 sensors in 2.4GHz band)
• 10 modalities
• 15 wired and wireless systems
26. Low-level activity
models (primitives)
Design-time: Training phase
Optimize
Sensor data
Annotations
High-level activity
models
Optimize
Context
Activity
Reasoning
Symbolic processing
Activity-aware
application
A1, p1, t1
A2, p2, t2
A3, p3, t3
A4, p4, t4
t
[1] Roggen et al., Wearable Computing: Designing and Sharing Activity-Recognition Systems Across Platforms, IEEE Robotics&Automation Magazine, 2011
Runtime: Recognition phase
FS2 P2
S1 P1
S0 P0
S3 P3
S4
P4
S0
S1
S2
S3
S4
F1
F2
F3
F0 C0
C1
C2
Preprocessing
Sensor sampling Segmentation
Feature extraction
Classification
Decision fusion
R
Null class
rejection
Subsymbolic processing
27. • Public challenge carried out in 2011
• Any method
• Any combination of 113 wearable channels
17 Gestures
• Open / close door 1
• Open / close door 2
• Open / close fridge
• Open / close dishwasher
• Open /close drawer 1
• Open / close drawer 2
• Open / close drawer 3
• Clean table
• Drink from cup
• Toggle light switch
28. Method Performance
LDA 0.25
QDA 0.24
NCC 0.19
1NN 0.55
3NN 0.56
UP 0.22
NStar 0.65
SStar 0.70
CStar 0.77
2011 results [1]
[1] Chavarriaga et al., The Opportunity challenge: A benchmark database for
on-body sensor-based activity recognition, Pattern recognition letters, 2013
[2] Ordones Morales et al., Deep LSTM recurrent neural networks for
multimodal wearable activity recognition, In preparation
ConvLSTM [2] 0.86 2015 results
+9%
“Deep learning”
32. M. Bächlin, M. Plotnik, D. Roggen, I. Maidan, J. M. Hausdorff, N. Giladi, and G. Tröster. Wearable Assistant for Parkinson's Disease
Patients With the Freezing of Gait Symptom. IEEE Transactions on Information Technology in Biomedicine, 14(2):436 - 446, 2010.
Freezing of gait (transient motor block)
thigh sensor
shank sensor
trunk sensor
earphones
wearable
computer
• Sensitivity = 73.1%
• Specificity = 81.6%
33. Glass & people with Parkinson’s
Workshop @ Newcastle University (28.08.2013)
• Accept positive “Benefit – privacy” tradeoffs
• “Sharing under my control to whom I choose”
• “Same as a phone / computer”, “just another interaction”
• “Gives me confidence back, that is what I need”
• “I cannot use a phone with shopping bags and a stick, Glass
would be always ready”
• “Everybody is different – interface should be customizable”
McNaney et al. Exploring the Acceptability of Google Glass as an Everyday Assistive Device for People with Parkinson’s, CHI 2014
45. Roggen et al., Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods, Networks and
Heterogeneous Media 6(3), 2011
Lukowicz et al, On-body sensing: from gesture-based input to activity-driven interactions, IEEE Computer, October 2010
Advanced behavioral analysis
49. Beach volleyball serves from wrist-worn gyro
Removing sand Serve
Distinguish subtle pattern differences
(e.g. serve styles)
Next steps: play and style analysis
Uetsuji et al., Wearable sensing and classification of beach volleyball styles, In preparation
51. www.opportunity-project.eu
EC grant n° 225938
pattern recognition in opportunistic configurations of sensors
(problem of distributed signal processing and machine learning)
EU funding ~ 1.5M€ / 3yr
53. Translation performance
• Same limb translation: accuracy <4% below baseline (accuracy ~95%)
• System identification: 3 seconds
• Self‐spreading of recognition capabilities!
54. Walkthrough: self-adaptation to gradual changes
Förster, Roggen, Tröster, Unsupervised classifier self-calibration through repeated context occurences: is
there robustness against sensor displacement to gain?, Proc. Int. Symposium Wearable Computers, 2009
Calibration dynamics
Self-calibration to displaced
sensors increases accuracy:
• by 33.3% in HCI dataset
• by 13.4% in fitness dataset
“expectation maximization”
55. Walkthrough: minimally user-supervised self-adaptation
• Adaptation leads to:
• Higher accuracy in the adaptive case v.s. control
• Higher input rate
• More "personalized" gestures
Förster et al., Online user adaptation in gesture and activity recognition - what’s the benefit? Tech Rep.
Förster et al., Incremental kNN classifier exploiting correct - error teacher for activity recognition, ICMLA 2010
56. Förster et al., On the use of brain decoded signals for online user adaptive gesture recognition systems, Pervasive 2010
Walkthrough: brain-guided self-adaptation
• ~9% accuracy increase with perfect brain signal recognition
• ~3% accuracy increase with effective brain signal recognition accuracy
•Adaptation guided by the user’s own perception of the system
• User in the loop
58. What is it that makes a device a "wearable"?
Always with the user
Personalised
Autonomous
Preempt needs
Augments our capabilities!
59. Acknowledgements
Sakura Uetsuji Dr Luis Ponce
Cuspinera
Former colleagues at ETHZ: Dr Alberto Calatroni, Dr Kilian Foerster, Dr Michael
Hardegger, Dr Martin Wirz, Dr Long-Van Nguyen-Dinh and others
Dr Francisco Javier
Ordones Morales