Wearable technologies are being developed for a variety of applications in both research labs and commercial settings. Some key areas of focus include flexible and stretchable electronics; custom wearables for specific sensing needs; activity recognition for tasks like healthcare monitoring and sports analysis; and developing wearables as "smart assistants" that can augment users by constantly sensing their context. Research challenges include miniaturizing components, developing low-power sensing and recognition, and enabling wearables to self-adapt over time through techniques like online user adaptation.

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

4. Wearable form factor
dictated by an application

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»

7. Wearables driven by
miniaturization

10. Flexible and stretchable electronics
(Munzenrieder et al. University of Sussex)
Bent finger Straight finger
Accordion-like electronics
for electronic skin

11. Processor
Battery
Display
Sensors:
Touch Motion proximity camera
Bone-conducting
speaker

15. A new definition of wearables:
« Smart assistant »

16. Pay
attention!
Augmenting the user
• Sense from a first person perspective
• Always with the user
• Learns behaviors, habits, needs

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)

20. Supporting behaviour change: Lab is on 4th floor
Stairs?Lift?

21. Sensing and recognising
activities

22. Motion sensor (accelerometer)

23. Custom wearables
• Flexible form factor
• Application specific needs
– (e.g. 1KHz motion sensing)
• Sensor research
• Low power reserch
• Interaction research

24. Activities of daily living

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”

30. Parkinson’s assistance

31. EC grant Nr FP6-018474-2 EC grant FP7-288516

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

34. Contextual support in the
assembly line

35. Quality control in car manufacturing
Continuously, 8 hours/day!

36. Automatic electronic checklist
Inertial measurement unit
(orientation sensor)
Motion capture

37. Stiefmeier et al., Wearable Activity Tracking in Car Manufacturing, Pervasive Computing Magazine, 2008
Automatic electronic checklist
• Advantages
– Automatic documentation
– Reproducibility
– Guarantees quality
– Improved usability

38. Learning

39. Micro-learning (Tin Man Labs, LLC)

40. Passive haptic learning for rehabilitation

41. Crowd behaviour analytics

42. Managing collective behaviors

43. Lord Mayor’s Show – November 12th, 2011, London

44. Lord Mayor’s Show – November 12th, 2011, London

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

46. Sports

47. Sports analysis
• Sensors on arm & hand

48. Low-power pattern recognition (template matching)
Atmel AVR8
ATmega324
8-bit w/o FPU
ARM Cortex M4
STM32F407
32-bit w/ FPU
• Real-time
• High-speed: 67 (AVR), 140 (M4) motifs w/ 8mW, 10mW @8MHz
• Low-power: single gesture spotter (AVR) w/ 135uW @ 120KHz
• Tunable tradeoffs: power/performance, sensitivity/specificity
• Suitable for hardware implementation
LM-W
LCSS
Roggen&Cuspinera Limited-Memory Warping LCSS for Real-Time Low-Power Pattern Recognition in Wireless
Nodes, Proc. EWSN 2015

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

50. Insight into research

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

52. Walkthrough: knowledge discovery - using unknown
sensors
• Static properties
• “3D skeleton"
• ExperienceItems “HCI-VolumeUp”
• ExperienceItems “HCI-VolumeDn”
• ExperienceItems “HCI-Next”
• ExperienceItems “HCI-Prev”
• Static properties
• “Acceleration“
• Dynamic properties
• “Wrist“
Physical and geometrical
relation between sensors
readings!
• Static properties
• "Acceleration"
• Dynamic properties
• “Wrist“
• ExperienceItems “HCI-VolumeUp”
• ExperienceItems “HCI-VolumeDn”
• ExperienceItems “HCI-Next”
• ExperienceItems “HCI-Prev”
Baños et al, Kinect=IMU? Learning MIMO Models to Automatically Translate Activity Recognition Models Across Sensor Modalities, ISWC 2012

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

57. Conclusion!

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