Wearable Computing - Part I: What is Wearable Computing?

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Introduction to wearable computing, sensors and methods for activity recognition.

Introduction to wearable computing, sensors and methods for activity recognition.

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  • 1. Wearable Computing Part I What is wearable computing? Daniel Roggen 2011
  • 2. © Daniel Roggen www.danielroggen.net droggen@gmail.comSanxingdui
  • 3. © Daniel Roggen www.danielroggen.net droggen@gmail.com Naylor, G.: Modern hearing aids and future development trends, http://www.lifesci.sussex.ac.uk/home/Chris_Darwin/BSMS/Hearing%20Aids/Naylor.ppt
  • 4. © Daniel Roggen www.danielroggen.net droggen@gmail.com http://www.vuzix.com/consumer/products_wrap920ar.html
  • 5. © Daniel Roggen www.danielroggen.net droggen@gmail.com Mark Weiser: the visionary of ubiquitous computing "The computer for the 21st century", Scientific American, 1991 "Specialized elements of hardware and software, connected by wires, radio waves and infrared, will be so ubiquitous that no one will notice their presence." "The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it." "Ubiquitous computing in this context does not mean just computers that can be carried to the beach, jungle or airport." "My colleagues and I at PARC believe that what we call ubiquitous computing will gradually emerge as the dominant mode of computer access over the next 20 years." 1952 – 1992 Chief scientist at Xerox PARC invisible in the background enhancing the "reality" (not a virtual world!)
  • 6. © Daniel Roggen www.danielroggen.net droggen@gmail.com
  • 7. © Daniel Roggen www.danielroggen.net droggen@gmail.com Motivated by continous technological progress
  • 8. © Daniel Roggen www.danielroggen.net droggen@gmail.com From Weiser to today Weiser's SciAm paper (1991) Pervasive computing Pervasive: 2002 Ubiquitous computing Ubicomp: 1999 Mobile computing MobiSys: 2003 Wearable computing ISWC: 1997 "Ambient Intelligence (AmI)" Human-computer interaction CHI: 1982 Sensor net EWSN: 2004
  • 9. © Daniel Roggen www.danielroggen.net droggen@gmail.com The « founding fathers » of wearable computing • MIT mid 1990s • Steve Mann (Univ. Toronto) – Humanistic Computing: “WearComp” as a New Framework and Application for Intelligent Signal Processing, Proc of the IEEE 86(11), pp. 2123-2151, 1998 – Smart Clothing: The Shift to Wearable Computing, Communications of the ACM, 39(8), pp. 23-24, 1996 • Thad Starner (Georgia Tech) – Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video, IEEE Trans on Pattern Analysis and Machine Intelligence, 20(12), pp 1371-1375, 1998
  • 10. © Daniel Roggen www.danielroggen.net droggen@gmail.com “The watch long ago encountered many of the major issues confronting wearable computing today. This paper […] discusses how the locations where the watch was worn on the body have changed over time, examines a variety of user interfaces for watches, and looks at how the watch affected cultural concepts of time and time discipline.” “The lessons for wearable computing are that the physical wearability will be determined as much by fashion as by human anatomy, that the user interface will gradually become simplified as people become more acquainted with computers, and finally that the cultural impact will be a broadening of the definition of information, a rationalization of representing information, and an increasing synchronization of personal events.” Martin, Time and Time Again: Parallels in the Development of the Watch and the Wearable Computer. Proc. 6th International Symposium on Wearable Computers, 2002
  • 11. © Daniel Roggen www.danielroggen.net droggen@gmail.com http://www.research.ibm.com/WearableComputing/linuxwatch/linuxwatch.html ~2001
  • 12. © Daniel Roggen www.danielroggen.net droggen@gmail.com Another look behind « 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 » [Marion, Heinsen, Chin, Helmso. Wrist instrument opens new dimension in personal information, Hewlett-Packard Journal, 1977]
  • 13. © Daniel Roggen www.danielroggen.net droggen@gmail.com More than computing on the body: context-aware assistance A modern definition of « wearable computing » • Augmenting senses, cognition, communication • Eminently personnal • Continuously available • Implicit interaction, usable despite cognitive load • Adapts to the environment, to the user • Proactive support • At the right moment, with the right modality Sense user's context Infer user's needs Provide assistance User reacts, system adapts
  • 14. © Daniel Roggen www.danielroggen.net droggen@gmail.com Context awareness • Merriam-Webster's Collegiate Dictionary: – The word ``context'' is defined as ``the interrelated conditions in which something exists or occurs • Chen, Kotz: – Context is the set of environmental states and settings that either determines an application's behavior or in which an application event occurs and is interesting to the user • Dey (Understanding and Using Context, PUC, 2001): – Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves – A system is context-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user’s task • Any knowledge about the user and his environment that enables applications to be "smarter"
  • 15. © Daniel Roggen www.danielroggen.net droggen@gmail.com Physical Social Mental Mental • Emotion awareness – Own/Others – Sadness, joy – Depression • Cognitive awareness – Cognitive load – Attention, concentration – Stress • … Social • Social interactions – Detect known people • Social network – Information exchange – Optimization of organizations • Crowd / collective behavior • … Dimensions of context Physical • User location – Absolute, relative • User activity – Manipulative gestures, pointing movements, modes of locomotion, posture, composite and hierarchical activities • … Environment • Map of surrounding services • Environment characteristics – Temperature, light, humidity • Radio fingerprints • … Environment Most known so far
  • 16. © Daniel Roggen www.danielroggen.net droggen@gmail.com Mann, Smart Clothing: The Shift to Wearable Computing, Comm. of the ACM, 1996 Roggen et al., Wearable Computing: Designing and Sharing Activity-Recognition Systems Across Platforms, IEEE Robotics&Automation Magazine, 2011 Campbell, A survey of mobile phone Sensing, IEEE Communications Magazine, 2010
  • 17. © Daniel Roggen www.danielroggen.net droggen@gmail.com Nowadays, a typical wearable • Sensors: to infer the user’s context – On the body (wearable) – In objects, environment ( « wearable »-« ambient » convergence) • Wearable computer – Mobile phone • Context recognition – Map sensor data to user context, needs – User experience • Feedback – Display – Vibrotactile – Audio • Integration in a Body/Personal Area Network Sense user's context Infer user's needs Provide assistance User reacts, system adapts
  • 18. © Daniel Roggen www.danielroggen.net droggen@gmail.com User Experimental method User studies Interaction design / UX HW Textile engineering Sensor Communication Processing Healthcare Sports Entertainment Industry Assistedliving Augmentedhuman Multidisciplinary research DSP ML AI Reasoning Modelling Context/Activity awareness
  • 19. © Daniel Roggen www.danielroggen.net droggen@gmail.com Smart Assistants v.s. Data Logging • Smart Assistants – Context is recognized online / real-time – Intelligence on body – Sense and reacts to the user’s context • Data Logging – Data are locally stored or transmitted – Offline analysis – Often human intervention – E.g. for medical purposes
  • 20. © Daniel Roggen www.danielroggen.net droggen@gmail.com Examples of wearable computing applications 1. Pervasive gaming 2. WearIT@Work: Supporting industrial workers with wearables 3. Parkinson's disease assistant: helping patients with freezing of gait 4. SMASH: sensing garment for rehabilitation
  • 21. © Daniel Roggen www.danielroggen.net droggen@gmail.com Pervasive gaming video
  • 22. Industrial manufacturingIndustrial manufacturing supported by wearablesupported by wearable computingcomputing
  • 23. Industrial Manufacturing Supported by On-body Computing Wearable Computing in a work environment European Union Project 23 Mio. €, 54 months 14 countries, 36 partners (Microsoft, SAP, EADS, Zeiss, HP, Siemens, Tekniker, …) Four pilot applications Clinical ward round Fire fighter Aircraft maintenance Car production Stiefmeier et al., Wearable Activity Tracking in Car Manufacturing, IEEE Pervasive Computing Magazine, 2008
  • 24. © Daniel Roggen www.danielroggen.net droggen@gmail.com Training of Unskilled Workers • Training process: • 1st step: Theory • 2nd step: Hands-on training of trainee • Merge 1st and 2nd step with a wearable system capable of activity tracking 1 2 • Despite automated production there remains a considerable amount of manual production steps • Training of unskilled workers is time consuming and costly
  • 25. © Daniel Roggen www.danielroggen.net droggen@gmail.com Case Study: Front Lamp Assembly • "Real life" application of Wearable Computing • Front lamp assembly for demonstration of activity tracking • Attachment parts: lamp, plastic bar, 3 screws • Tools: 2 cordless screwdrivers, alignment tester Klick Klick
  • 26. © Daniel Roggen www.danielroggen.net droggen@gmail.com Sensing Wearable sensors – Inertial sensors [A] – Force sensitive resistors [B] – RFID reader [D] – Ultrasonic receivers – Using wireless links [E] • Environmental sensors/infrastructure – Mounted on car body • Magnetic switches (Reed) • Force sensitive resistors (FSR) – Mounted on tools • RFID-Tags [C] – Ultrasonic beacons ~15 sensors are required for task tracking
  • 27. © Daniel Roggen www.danielroggen.net droggen@gmail.com Sensing
  • 28. © Daniel Roggen www.danielroggen.net droggen@gmail.com Sensor Calibration, Selection and Acquisition
  • 29. © Daniel Roggen www.danielroggen.net droggen@gmail.com Assembly task modeling and tracking State0 State1 0 bar screws State2 1 bar screw State3 2 bar screws State4 3 bar screws State5 1 lamp scr. State6 2 lamp scr. State8 State11 State13 State7 State10 State9 State12 D1 / 0 /D1 / 1 A1*RF1*V / 2 A2*RF1*V / 3 A3*RF1*V / 4 A1*A2*RF1*V / 6 A1*A3*RF1*V / 7 A3*A3*RF1*V / 8 A1*A2*A3*RF1*V / 12 A4*RF2*V / 16 A5*RF2 / 17 A4*A5*RF2 / 19 /A1*/A2*/A3 / 5 /A1*/A2*A3 / 9 /A1*A2*/A3 / 10 A1*/A2*/A3 / 11 A1*A2*/A3 / 13 A1*/A2*A3 / 14 /A1*A2*A3 / 15 /A4*/A5 / 18 /A4*A5 / 20 A4*/A5 / 21 /A1*/A2*/A3 / 42 /A1*/A2*A3 / 43 /A1*A2*/A3 / 44 A1*/A2*/A3 / 45 /A1*/A2*/A3 / 46 /A4*/A5 / 47 /D2*D3*/D4 / 23 /D2*/D3*D4 / 24D2*/D3*/D4 / 22 D3*/D4 / 29 /D2*D3 / 36 D3 / 39 D2 / 40 D2*D3 / 37 D3*D4 / 31 D4 / 38 D2*D3*/D4 / 25 /D2*D3*D4 / 27 /D2*D4 / 33 D2*/D4 / 32 /D3*D4 / 30 D2*/D3 / 35 D2*D3*/D4 / 26 D2*D4 / 34 D2*D3*D4 / 28 /A4*/A5 / 50 /A4*/A5 / 53 /A4*/A5 / 49 /A4*/A5 / 48 /A4*/A5 / 51 /A4*/A5 / 52 /A4*/A5 / 41 State 3: Screw B tightened
  • 30. © Daniel Roggen www.danielroggen.net droggen@gmail.com Motion Jacket • Jacket-integrated sensors • 7 inertial sensor modules • orientation data • accelerometer and gyroscope data
  • 31. © Daniel Roggen www.danielroggen.net droggen@gmail.com Motion Model
  • 32. © Daniel Roggen www.danielroggen.net droggen@gmail.com • Contextual support in the assembly line Stiefmeier et al., Wearable Activity Tracking in Car Manufacturing, IEEE Pervasive Computing Magazine, 2008
  • 33. © Daniel Roggen www.danielroggen.net droggen@gmail.comStiefmeier et al., Wearable Activity Tracking in Car Manufacturing, IEEE Pervasive Computing Magazine, 2008
  • 34. © Daniel Roggen www.danielroggen.net droggen@gmail.com Benefits of Wearable Computing • Medium-term – Reduce training costs (time and manpower) – Enable training at worker's own pace – Increase efficiency ('productivity') during training • Long-term – Increase productivity of assembly – Improve quality management (online documentation, work flow monitoring) – Increase security
  • 35. © Daniel Roggen www.danielroggen.net droggen@gmail.com Assistant for Parkinson’s disease patients with freezing of gait Funded by EC grant Nr FP6-018474-2 Bächlin et al. Wearable Assistant for Parkinson's Disease Patients With the Freezing of Gait Symptom, IEEE Transactions on Information Technology in Biomedicine, 14(2), 2010
  • 36. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 36Daniel Roggen ETH Zürich / Wearable Computing Lab. Parkinson disease patients with Freezing of Gait
  • 37. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 37Daniel Roggen ETH Zürich / Wearable Computing Lab. Freezing of Gait (FOG) • Parkinson’s disease (PD) is a common neurological disorder leading to impaired motor skills • When legs affected: FOG – Developed by 50% of PD patients: 10% of those with mild syndrome, 80% of severely affected [1] • FOG is common cause of falls [1] Macht et. al; Predictors of freezing in Parkinson’s Disease: a survey of 6620 patients; Mov. Disord. 2007;22:953-6
  • 38. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 38Daniel Roggen ETH Zürich / Wearable Computing Lab. State of the art • Drug treatment (levodopa) – FoG resistant to medication, difficult dosage, side effect, habituation • Patients imagine... – ... marching to command – ... stepping over cracks in the floor – or walk to music or a beat • First studies with external rythmic cueing[2-5] – Permanent cueing, in the laboratory [2] Thaut et al. “Rhythmic auditory stimulation in gait training for parkinsons disease patients,” Movement Disorders, vol. 11, no. 2, pp. 193–200, 1996. [3] Rubinstein et al. “The power of cueing to circumvent dopamine deficits: a review of physical therapy treatment of gait disturbances in parkinson’s disease,” Movement Disorders, vol. 17, no. 6, 2002. [4] Lim et al. “Effects of external rhythmical cueing on gait in patients with parkinson’s disease: a systematic review,” Clinical Rehabilitation, vol. 19, no. 7, pp. 695–713, 2005. [5] van Wegen et al. “The effect of rhythmic somatosensory cueing on gait in patients with parkinson’s disease,” Journal of the Neurological Sciences, vol. 248, no. 1-2, pp. 210–214, 2006.
  • 39. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 39Daniel Roggen ETH Zürich / Wearable Computing Lab. Objective: context-aware external auditive cueing • Provide auditive rhythmic cueing only when necessary • Online FOG detection from body worn sensors • Analyse patients' and physiotherapists' perspective
  • 40. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 40Daniel Roggen ETH Zürich / Wearable Computing Lab. 40 Wearable FOG assistant • 3-D acceleration sensor 27x47x12mm3 ; 22grams fSR=64Hz, Bluetooth Operation time 10h • Wearable computer Intel XScale processor Linux system, CRN Toolbox[2] 132x82x30mm3 ; 231grams, Operation time ~6h • Earphones 1Hz ticking sound thigh sensor shank sensor trunk sensor earphones wearable computer ] Bannach et. al; Distributed modular toolbox for multi- modal context recognition. ARCS 2006; pp 99-113
  • 41. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 41Daniel Roggen ETH Zürich / Wearable Computing Lab. FOG detection from acceleration signal Power ratio in “freeze” and “locomotion” frequency range Freeze band Locomotor band
  • 42. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 42Daniel Roggen ETH Zürich / Wearable Computing Lab. Online FOG detection: freeze/loco power ratio
  • 43. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 43Daniel Roggen ETH Zürich / Wearable Computing Lab. FOG detection algorithm Body motion Acceleration Sensor acc samples Windowing + Freq analysis PDS Energy sum ‘Loco’ band Energy sum ‘Freeze’ band Eloco Efreeze Etotal FI* Freeze index thresholding FOG detection Total Energy thresholding FI 0/1
  • 44. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 44Daniel Roggen ETH Zürich / Wearable Computing Lab. Protocol (~1h30/patient) • Instruction on experiment • 2 recording sessions – without feedback (20mn) – with feedback (20mn) • Each session includes: a) straight line walking
  • 45. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 45Daniel Roggen ETH Zürich / Wearable Computing Lab. Protocol (~1h30/patient) • Instruction on experiment • 2 recording sessions – without feedback (20mn) – with feedback (20mn) • Each session includes: a) straight line walking b) random walk
  • 46. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 46Daniel Roggen ETH Zürich / Wearable Computing Lab. Protocol (~1h30/patient) • Instruction on experiment • 2 recording sessions – without feedback (20mn) – with feedback (20mn) • Each session includes: a) straight line walking b) random walk c) Activities of daily living • Medication intake • Debriefing with the therapist • Questionnaire
  • 47. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 47Daniel Roggen ETH Zürich / Wearable Computing Lab. Evaluation study
  • 48. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 48Daniel Roggen ETH Zürich / Wearable Computing Lab. Data recordings evaluation • 10 PD patients – 7 males / 3 females – 66.5 +- 4.8 years – 2.7+- 0.6 H&Y scale • 8h 20min of data recorded • 8 out of 10 experienced FOG • 235 FOG episodes occured
  • 49. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 49Daniel Roggen ETH Zürich / Wearable Computing Lab. Results: Online detection accuracy Overall: • Sensitivity = 73.1% • Specificity = 81.6%
  • 50. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 50Daniel Roggen ETH Zürich / Wearable Computing Lab. Video: Feedback ON
  • 51. © Daniel Roggen www.danielroggen.net droggen@gmail.com 2nd April 2009 51Daniel Roggen ETH Zürich / Wearable Computing Lab. Conclusion • First study with online FOG detection & cueing • Sensitivity 73.1%; Specificity 81.6% (global parameters) • Promising feedback from patients and therapists • PD patient may benefit from context-aware cueing • Only 10 patients - subjective reports must be taken carefully • Results support conducting a larger scale study, eventually outside of laboratory
  • 52. © Daniel Roggen www.danielroggen.net droggen@gmail.com Horizontal elongation Vertical elongation Lifting shoulders A Method to Measure Elongations of Clothing, C. Mattmann, T. Kirstein, G. Tröster. Proc. Ambience 05, 1st International Conference on Intelligent Ambience and Well-Being, Tampere, Finland, September 19-20 2005 Design Concept of Clothing Recognizing Back Postures; C. Mattmann, G. Tröster; Proc. 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors (ISSS-MDBS 2006), Boston, September 4-6, 2006. sensors Backmanager • Garment measuring upper body posture with strain sensors • Detection of "unhealthy" postures • Corrective feedback to the user
  • 53. © Daniel Roggen www.danielroggen.net droggen@gmail.com Backmanager Design Concept of Clothing Recognizing Back Postures; C. Mattmann, G. Tröster; Proc. 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors (ISSS-MDBS 2006), Boston, September 4-6, 2006.
  • 54. © Daniel Roggen www.danielroggen.net droggen@gmail.com SMASH: garment for posture sensing • Applications: – Practicing rehabilitation movements – Posture reminders Harms et al., Rapid prototyping of smart garments for activity-aware applications. Journal of Ambient Intelligence and Smart Environments, 2009
  • 55. © Daniel Roggen www.danielroggen.net droggen@gmail.com SMASH: garment for posture sensing Harms et al., Rapid prototyping of smart garments for activity-aware applications. Journal of Ambient Intelligence and Smart Environments, 2009 Postures recognition for shoulder rehabilitation Recognition of back angle for posture reminder
  • 56. © Daniel Roggen www.danielroggen.net droggen@gmail.com Commercial potential or toy research? Amft&Lukowicz, From Backpacks to Smartphones: Past, Present, and Future of Wearable Computers, IEEE Pervasive Computing Magazine, 2009 Zypad http://www.zypad.com WT4000 Wearable Terminal Motorola Guinness World Records “fastest-selling consumer device” 133’333 units/day between November and January Nike+ SportBand
  • 57. © Daniel Roggen www.danielroggen.net droggen@gmail.com • Location-based reminders – E.g. « GeoMemo App » for Android • « Bump App » for iPhone/Android – Bump phones together to share data between phones – Example of motion recognition • Place recommendations – E.g. foursquare – Location-awareness + friend network Context aware applications
  • 58. © Daniel Roggen www.danielroggen.net droggen@gmail.com Multidisciplinary • Wearable computing • Robotics, Sensors, IT, Networks • Medicine • (Neuro-)Psychology • Cognitive science, Social sciences Shift towards richer "human-like" devices or smart assistants. They know what we need, when, and how we want it. Better awareness of the user's needs, state and internal motivations (compared to personal computing). Applications / Users • Healthcare • Social / behavioral sciences • Psychology • Gaming • HCI • Robotics Some last remarks…. Adapts and reacts to the user’s needs
  • 59. © Daniel Roggen www.danielroggen.net droggen@gmail.com
  • 60. © Daniel Roggen www.danielroggen.net droggen@gmail.com For further reading Founding fathers and background • Weiser, Computer for the 21st Century, IEEE PCM, reprint, 2002 (original: Scientific American, 1991) • Weiser, Hot Topics - Ubiquitous computing, IEEE Computer, 1993 • Mann, Wearable Computing as Means for Personal Empowerment, ISWC, 1998 • Mann, Humanistic Computing-WearComp as a New Framework and Application for Intelligent Signal Processing • Mann, Smart Clothing-The Shift to Wearable Computing, Communications of the ACM, 1996) • Starner, Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video, IEEE Trans on Pattern Analysis and Machine Intelligence, 20(12), pp 1371-1375, 1998 Looking back • Bush, As We May Think, The Atlantic Monthly, 1945 • Marion, Heinsen, Chin, Helmso. Wrist instrument opens new dimension in personal information, Hewlett-Packard Journal, 1977 • Martin, Time and Time Again: Parallels in the Development of the Watch and the Wearable Computer. Proc. 6th International Symposium on Wearable Computers, 2002 • Thorp, The Invention of the First Wearable Computer, IEEE Int.Symp. on Wearable Computer 1998 Context and activity-aware computing • Dey, Understanding and Using Context, Personal and Ubiquitous Computing,2001 • Lukowicz et al., On-Body Sensing: From Gesture-Based Input to Activity-Driven Interaction, IEEE Computer, 43(10), pp. 92-96, 2010 • N. Davies, D. Siewiorek, R. Sukthankar, Special Issue: Activity-Based Computing, IEEE Pervasive Computing, 7(2), pp. 20-21, 2008 Challenges • Starner, The Challenges of Wearable Computing-Part 1&2, IEEE Pervasive Computing Magazine, 2001 Applications • Stiefmeier et al., Wearable Activity Tracking in Car Manufacturing, IEEE Pervasive Computing Magazine, 2008 • Bächlin et al. Wearable Assistant for Parkinson's Disease Patients With the Freezing of Gait Symptom, IEEE Transactions on Information Technology in Biomedicine, 14(2), 2010 • Harms et al., Rapid prototyping of smart garments for activity-aware applications. Journal of Ambient Intelligence and Smart Environments, 2009 • Paradiso, Gips, Laibowitz, Sadi, Merrill, Aylward, Maes, Pentland, Identifying and facilitating social interaction with a wearable wireless sensor network, Personal and Ubiquitous Computing, 4(2), pp.137-152, 2010 • Pentland, Healthwear: Medical Technology Becomes Wearable, Computer, 37(5), pp. 42-49, 2004 • Eagle, Pentland, Lazer, Inferring friendship network structure by using mobile phone data, Proc Natl Acad Sci, 2009 Mobile phones • Lane, Miluzzo, Lu, Peebles, Choudhury, Campbell, A Survey of Mobile Phone Sensing, IEEE Communications Magazine, 48(9), pp. 140- 180, 2010
  • 61. © Daniel Roggen www.danielroggen.net droggen@gmail.com