Wearable technologies:
what's brewing in the lab?
http://www.sussex.ac.uk/strc/research/wearable
Dr. Daniel RoggenWearable Technologies Lab
Sensor Technologies Research CentreUniversity of Sussex
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/
Naylor, G.: Modern hearing aids and future development trends, http://www.lifesci.sussex.ac.uk/home/Chris_Darwin/BSMS/Hearing%20Aids/Naylor.ppt
Wearable form factor dictated by an application
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
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-yearcalendar, 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 additionaldifficulties rarely encountered at HP»
Wearables driven by miniaturization
Flexible and stretchable electronics(Munzenrieder et al. University of Sussex)
Bent finger Straight finger
Accordion-like electronics for electronic skin
Processor
Battery
DisplaySensors:Touch Motion proximity camera
Bone-conductingspeaker
A new definition of wearables:« Smart assistant »
Payattention!
Augmenting the user• Sense from a first person perspective• Always with the user• Learns behaviors, habits, needs
Vannevar Bush, As we may think. Life magazine, 1945
Cyclop cameraSpeech recognitionAccess to all human knowledge (Memex)
“Let us project this trend ahead to a logical, if not inevitable,outcome”
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, 2001x Ashbrook, Enabling mobile microinteractions, PhD, 2010
•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)
Supporting behaviour change: Lab is on 4th floor
Stairs?Lift?
Sensing and recognisingactivities
Motion sensor (accelerometer)
Custom wearables• Flexible form factor• Application specific needs
– (e.g. 1KHz motion sensing)
• Sensor research• Low power reserch• Interaction research
Activities of daily living
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 2010http://opportunity-project.eu/challengeDatasethttp://vimeo.com/8704668
Sensor rich• Body, objects, environment• 72 sensors (28 sensors in 2.4GHz band)• 10 modalities• 15 wired and wireless systems
Low-level activity models (primitives)
Design-time: Training phase
Optim
ize
Sensor data
Annotations High-level activity models
Optim
ize
ContextActivity
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
PreprocessingSensor sampling Segmentation
Feature extractionClassification
Decision fusion
R
Null classrejection
Subsymbolic processing
• 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
Method PerformanceLDA 0.25QDA 0.24NCC 0.191NN 0.553NN 0.56UP 0.22NStar 0.65SStar 0.70CStar 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”
Parkinson’s assistance
EC grant Nr FP6-018474-2 EC grant FP7-288516
M. Bächlin, M. Plotnik, D. Roggen, I. Maidan, J. M. Hausdorff, N. Giladi, and G. Tröster. Wearable Assistant for Parkinson's DiseasePatients 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
wearablecomputer
• Sensitivity = 73.1%• Specificity = 81.6%
Glass & people with Parkinson’sWorkshop @ 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
Contextual support in the assembly line
Quality control in car manufacturing
Continuously, 8 hours/day!
Automatic electronic checklist
Inertial measurement unit (orientation sensor)
Motion capture
Stiefmeier et al., Wearable Activity Tracking in Car Manufacturing, Pervasive Computing Magazine, 2008
Automatic electronic checklist
• Advantages– Automatic documentation– Reproducibility– Guarantees quality– Improved usability
Learning
Micro-learning (Tin Man Labs, LLC)
Passive haptic learning for rehabilitation
Crowd behaviour analytics
Managing collective behaviors
Lord Mayor’s Show – November 12th, 2011, London
Lord Mayor’s Show – November 12th, 2011, London
Roggen et al., Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods, Networks and Heterogeneous Media 6(3), 2011Lukowicz et al, On-body sensing: from gesture-based input to activity-driven interactions, IEEE Computer, October 2010
Advanced behavioral analysis
Sports
Sports analysis
• Sensors on arm & hand
Low-power pattern recognition (template matching)
Atmel AVR8ATmega3248-bit w/o FPU
ARM Cortex M4STM32F40732-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-WLCSS
Roggen&Cuspinera Limited-Memory Warping LCSS for Real-Time Low-Power Pattern Recognition in Wireless Nodes, Proc. EWSN 2015
Beach volleyball serves from wrist-worn gyro
Removing sand Serve
Distinguish subtle pattern differences (e.g. serve styles)
Next steps: play and style analysisUetsuji et al., Wearable sensing and classification of beach volleyball styles, In preparation
Insight into research
www.opportunity-project.euEC grant n° 225938
pattern recognition in opportunistic configurations of sensors(problem of distributed signal processing and machine learning)
EU funding ~ 1.5M€ / 3yr
Walkthrough: knowledge discovery - using unknownsensors
• Static properties• “3D skeleton"
• ExperienceItems “HCI-VolumeUp”• ExperienceItems “HCI-VolumeDn”• ExperienceItems “HCI-Next”• ExperienceItems “HCI-Prev”
• Static properties• “Acceleration“
• Dynamic properties• “Wrist“
Physical and geometricalrelation 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
Translation performance
• Same limb translation: accuracy <4% below baseline (accuracy ~95%)• System identification: 3 seconds• Self‐spreading of recognition capabilities!
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”
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
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
Conclusion!
What is it that makes a device a "wearable"?
Always with the user
Personalised
Autonomous
Preempt needs
Augments our capabilities!
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