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Robust Expert Systems for more Flexible Real-World Activity Recognition

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(PhD Thesis presentation)

The use of wearable or on-body sensors to monitor the human behavior is now on the forefront of human activity recognition. Nevertheless, the actual results for human activity recognition are fairly constrained and generally restricted to ideal or laboratory scenarios. Activity recognition systems are designed to comply with ideal conditions and are of limited utility in realistic domains. To become real-world applicable, activity recognition systems must satisfy operational and quality requirements that pose complex challenges, most of which have been sparsely and vaguely investigated to date.
Classic activity recognition systems assume that the sensor setup remains identical during the lifelong use of the system. However, in users' daily life, sensors may fail, run out of battery, be misplaced or experience topological variations. These changes may lead to significant variations in the sensor measurements with respect to the default case. Consequently, activity recognition systems devised for ideal conditions may react in an undesired manner to imperfect, unknown or anomalous sensor data. This potentially translates into a partial or total malfunctioning of the activity recognition system.
In this thesis, novel expert systems are proposed to address the challenges of making activity recognition systems functional in real-world scenarios.
An innovative methodology, the hierarchical weighted classifier, that leverages the potential of multi-sensor configurations, is defined to overcome the effects of sensor failures and faults. This approach proves to be as valid as other standard activity recognition models in ideal conditions while outperforming them in terms of robustness to sensor failure and fault-tolerance. This methodology also shows outstanding capabilities to assimilate sensor deployment anomalies motivated by the user self-placement of the sensors. Furthermore, a novel multimodal transfer learning method that operates at runtime, with low overhead and without user or system designer intervention is developed. This approach serves to automatically translate activity recognition capabilities from an existing system to an untrained system even for different sensor modalities. This is of key interest to support sensor replacements as part of equipment maintenance, sensor additions in system upgrades and to benefit from sensors that happen to be available in the user environment. The potential of these advanced expert models leads to new research directions such as autonomous systems self-configuration, auto-adaptation and evolvability in activity recognition. Thus, this thesis opens-up a new range of opportunities for activity recognition systems to operate in real-world scenarios.

Work described in the following dissertation:

Banos, O.: Robust Expert Systems for more Flexible Real-World Activity Recognition. Ph.D. Thesis, University of Granada, Granada (SPAIN) (2014)

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Robust Expert Systems for more Flexible Real-World Activity Recognition

  1. 1. Robust Expert Systems for more Flexible Real-World Activity Recognition Granada, Friday, April 25, 2014 Presented by: Oresti Baños Supervised by: Miguel Damas, Héctor Pomares and Ignacio Rojas Department of Computer Architecture and Computer Technology, CITIC-UGR, University of Granada, SPAIN
  2. 2. Human Activity 2 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  3. 3. Health Abnormal behavior detection Proactive Assistance Labour risk prevention Wellness Sports Gaming Human Activity • Why is identifying human activity interesting? 3 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  4. 4. Activity Recognition (AR) • Activity recognition concept “Recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions” • Activity recognition process 4 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Phenomena Human activity (body motion) Measurement Sensing (ambient/wearables) Processing Data adequation and knowledge inference Recognized Activity
  5. 5. Wearable Activity Recognition 5 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS • Wearable activity recognition systems are ready! The first system capable of fully recognize your daily routine. AtlasWearables (2014) The simplest way to understand your day and night. Jawbone Up (2014) The best activity tracker on the market. Fitbit Force (2014) The device that tracks your active life and measures all kind of activities. Nike Fuel (2014)
  6. 6. Wearable Activity Recognition 6 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS • But… do wearable activity recognition systems meet people’s expectance?
  7. 7. Challenges for Real-World Activity Recognition • Actively investigated: – Reliability – Simplicity – Latency • Barely addressed: – Privacy – Fault-tolerance – Usability – Unobtrusiveness – Fashionability – Self-configuration – Auto-adaptation – Evolvability 7 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  8. 8. Challenges for Real-World Activity Recognition 8 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS   • Actively investigated: – Reliability – Simplicity – Latency • Barely addressed: – Privacy – Fault-tolerance – Usability – Unobtrusiveness – Fashionability – Self-configuration – Auto-adaptation – Evolvability
  9. 9. Thesis Motivation and Objectives • Motivation: “Create more advanced systems capable of handling real-world AR issues as well as to incorporate more intelligent capabilities to transform experimental prototypes into actual usable applications” • Objectives: – O1: “Investigate the tolerance of standard AR systems to unforeseen sensor failures and faults, as well as contribute with an alternate approach to cope with these technological anomalies”  Fault-tolerance – O2: “Research the robustness of standard AR systems to unforeseen variations in the sensor deployment, as well as contribute with an alternate approach to cope with these practical anomalies”  Usability, Unobtrusiveness – O3: “Study the capacity of standard AR systems to support unforeseen changes in the sensor network, as well as contribute with an alternate approach to cope with these topological variations”  Self-configuration, auto-adaptation, evolvability 9 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  10. 10. Activity Recognition Process 10 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Phenomena Human activity (body motion) Measurement Sensing (ambient/wearables) Processing Data adequation and knowledge inference Recognized Activity How does it work exactly?
  11. 11. Activity Recognition Process 11 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS The Activity Recognition Chain (ARC) Phenomena Human activity (body motion) Measurement Sensing (ambient/wearables) Processing Data adequation and knowledge inference Recognized Activity
  12. 12. Activity Recognition Chain (ARC) 12 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  13. 13. Activity Recognition Chain (ARC) 13 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  14. 14. Activity Recognition Chain (ARC) 14 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  15. 15. Activity Recognition Chain (ARC) 15 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  16. 16. Activity Recognition Chain (ARC) 16 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  17. 17. Activity Recognition Chain (ARC) 17 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  18. 18. Activity Recognition Chain (ARC) 18 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  19. 19. Tolerance of AR systems to sensor faults and failures INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Objective: “Investigate the tolerance of standard AR systems to unforeseen sensor failures and faults, as well as contribute with an alternate approach to cope with these technological anomalies”
  20. 20. Problem Statement 20 SENSOR ERRORS Are standard activity recognition systems prepared to cope with sensor technological anomalies? Is it possible to keep the systems functioning under the effects of sensor errors? Activity recognition process INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Body motion sensing Signal processing and reasoning Recognition of activities
  21. 21. Signal effects Sensor Technological Anomalies 21 • Faults (overheating, environmental changes, decalibration) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS • Failures (outages, breakdowns, disconnection, battery depletion)
  22. 22. Sensor Technological Anomalies in AR: Related Work • Detection of sensor anomalies – Sensor query (Rost06) – Neighborhood data correlation • Signal level (Yao10) • Feature level (Ramanathan09) • Reasoning (Rajasegarar07, Ganeriwal08) • Counteraction of sensor anomalies – Data imputation (Uchida13) – Sensor fusion (Sagha13) S. Rost and H. Balakrishnan. Memento: A health monitoring system for wireless sensor networks. In 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks, volume 2, pp. 575-584, 2006. Y. Yao, A. Sharma, L. Golubchik, and R. Govindan. Online anomaly detection for sensor systems: A simple and efficient approach. Performance Evaluation, 67(11):1059-1075, November 2010. N. Ramanathan, T. Schoellhammer, E. Kohler, K. Whitehouse, T. Harmon, and D. Estrin. Suelo: human-assisted sensing for exploratory soil monitoring studies. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pp. 197-210, 2009. S. Rajasegarar, C. Leckie, M. Palaniswami, and J. C. Bezdek. Quarter sphere based distributed anomaly detection in wireless sensor networks. In IEEE International Conference on Communications, pp. 3864-3869, June 2007. S. Ganeriwal, L. K. Balzano, and M. B. Srivastava. Reputationbased framework for high integrity sensor networks. ACM Transaction on Sensor Networks, 4(3):1-37, June 2008. R. Uchida, H. Horino, and R. Ohmura. Improving fault tolerance of wearable wearable sensor-based activity recognition techniques. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, pp. 633-644, 2013. H. Sagha, H. Bayati, J. del R. Millan, and R. Chavarriaga. On-line anomaly detection and resilience in classifier ensembles. Pattern Recognition Letters, 34(15):1916-1927, 2013 22 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  23. 23. Sensor Failures in Classic AR Systems • Single-sensor ARC (SARC) 23 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  24. 24. • Single-sensor ARC (SARC) 24 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS If the sensor fails, the complete system fails Solution Use more sensors for redundancy (multi-sensor ARC or MARC) Sensor Failures in Standard AR Systems
  25. 25. • Feature fusion multi-sensor ARC (FFMARC) 25 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Sensor Failures in Standard AR Systems
  26. 26. • Feature fusion multi-sensor ARC (FFMARC) 26 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS If a sensor fails, the complete system fails Solution Independent ARCs + decision fusion Sensor Failures in Standard AR Systems
  27. 27. • Decision fusion multi-sensor ARC (DFMARC) 27 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Sensor Failures in Standard AR Systems
  28. 28. • Decision fusion multi-sensor ARC (DFMARC) 28 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS If a sensor fails, the system is still capable of functioning but… Is it capable of recognition? Sensor Failures in Standard AR Systems
  29. 29. • Hierarchical decision (HD) – Information from some sensors more valuable than from others (e.g., body part for a certain activity)  Ranking of decisions – Decisions mainly made on top (recognition relies on a sensor or few sensors)  Problem when top-ranked sensors get unavailable • Majority voting (MV) – Equality scheme (all sensors have the same importance)  Fairness, decisiveness – A plurality of weak decisors may prevail over the rest  Tyranny of the majority 29 Sensor Failures in Standard AR Systems INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS … c11,c12,…,c1k c1=φ(c11,c21,…, cM1), c2=φ(c12,c22,…, cM2), … ck=φ(c1k,c2k,…, cMk) c21,c22,…, c2k cM1,cM2,…,cMk … … … … … … c11,c12,…,c1k c1=φ(c11,c21,…, cM1), c2=φ(c12,c22,…, cM2), … ck=φ(c1k,c2k,…, cMk) c21,c22,…, c2k cM1,cM2,…,cMk …
  30. 30. A Novel Method: Hierarchical Weighted Classifier 30 Sensor M Sensor 2 SM S2 S1 α11 Ψ C12 C1N C11 Ψ C21 C22 C2N Ψ CM1 CM2 CMN Ψ Decision Activity level (base classifier) Sensor level (sensor classifier) Network level (sensor fusion) β11 α12 β12 α1N β1N α21 β21 α22 β22 α2N β2N αM1 βM1 αM2 βM2 αMN βMN γ11,…,1N δ11,…,1N γ21,…,2N δ21,…,2N γM1,…,MN δM1,…,MN Sensor 1 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  31. 31. 31 Sensor M Sensor 2 SM S2 S1 α11 Ψ C12 C1N C11 Ψ C21 C22 C2N Ψ CM1 CM2 CMN Ψ Decision Activity level (base classifier) Sensor level (sensor classifier) Network level (sensor fusion) β11 α12 β12 α1N β1N α21 β21 α22 β22 α2N β2N αM1 βM1 αM2 βM2 αMN βMN γ11,…,1N δ11,…,1N γ21,…,2N δ21,…,2N γM1,…,MN δM1,…,MN Sensor 1 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS A Novel Method: Hierarchical Weighted Classifier N activities & M sensors
  32. 32. 32 Sensor M Sensor 2 SM S2 S1 α11 Ψ C12 C1N C11 Ψ C21 C22 C2N Ψ CM1 CM2 CMN Ψ Decision Activity level (base classifier) Sensor level (sensor classifier) Network level (sensor fusion) β11 α12 β12 α1N β1N α21 β21 α22 β22 α2N β2N αM1 βM1 αM2 βM2 αMN βMN γ11,…,1N δ11,…,1N γ21,…,2N δ21,…,2N γM1,…,MN δM1,…,MN Sensor 1 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS A Novel Method: Hierarchical Weighted Classifier N activities & M sensors S1 α11 Ψ C12 C1N C11 β11 α12 β12 α1N β1N 𝑞 𝑚 𝑥 𝑚 𝑘 = 𝑎𝑟𝑔𝑚𝑎𝑥 𝑞 𝑂 𝑚 𝑥 𝑚 𝑘 𝑂 𝑚 𝑥 𝑚 𝑘 = 𝑊𝐷 𝑚𝑛 𝑥 𝑚 𝑘 𝑁 𝑛=1 𝛼 𝑚𝑛 = 𝑇𝑃 𝑚𝑛 𝑇𝑃 𝑚𝑛 + 𝐹𝑁 𝑚𝑛 𝛽 𝑚𝑛 = 𝑇𝑁 𝑚𝑛 𝑇𝑁 𝑚𝑛 + 𝐹𝑃 𝑚𝑛 𝑊𝐷 𝑚𝑛 𝑥 𝑚 𝑘 = 𝛼 𝑚𝑛, 𝑥 𝑚 𝑘 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 0, 𝑥 𝑚 𝑘 𝑛𝑜𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 ∀𝑞 = 𝑛 𝛽 𝑚𝑛, 𝑥 𝑚 𝑘 𝑛𝑜𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 0, 𝑥 𝑚 𝑘 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 ∀𝑞 ≠ 𝑛
  33. 33. 33 Sensor M Sensor 2 SM S2 S1 α11 Ψ C12 C1N C11 Ψ C21 C22 C2N Ψ CM1 CM2 CMN Ψ Decision Activity level (base classifier) Sensor level (sensor classifier) Network level (sensor fusion) β11 α12 β12 α1N β1N α21 β21 α22 β22 α2N β2N αM1 βM1 αM2 βM2 αMN βMN γ11,…,1N δ11,…,1N γ21,…,2N δ21,…,2N γM1,…,MN δM1,…,MN Sensor 1 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS A Novel Method: Hierarchical Weighted Classifier N activities & M sensors S1 α11 Ψ C12 C1N C11 β11 α12 β12 α1N β1N S1 γ11,…,1N δ11,…,1N 𝑊𝐷 𝑚𝑛 𝑞 𝑚 𝑥 𝑚 𝑘 = 𝛾 𝑚𝑛, 𝑞 𝑚 𝑥 𝑚 𝑘 = 𝑛 𝛿 𝑚𝑛, 𝑞 𝑚 𝑥 𝑚 𝑘 ≠ 𝑛 𝛾 𝑚 = 𝛾 𝑚1, 𝛾 𝑚2,… , 𝛾 𝑚𝑛 = 𝑇𝑃 𝑚1 𝑇𝑃 𝑚1 + 𝐹𝑁 𝑚1 , 𝑇𝑃 𝑚2 𝑇𝑃 𝑚2 + 𝐹𝑁 𝑚2 , … , 𝑇𝑃𝑚𝑛 𝑇𝑃𝑚𝑛 + 𝐹𝑁 𝑚𝑛 𝑞 𝑚 𝑥 𝑚 𝑘 = 𝑎𝑟𝑔𝑚𝑎𝑥 𝑞 𝑂 𝑚 𝑥 𝑚 𝑘 𝑂 𝑚 𝑥 𝑚 𝑘 = 𝑊𝐷 𝑚𝑛 𝑥 𝑚 𝑘 𝑁 𝑛=1 𝛼 𝑚𝑛 = 𝑇𝑃 𝑚𝑛 𝑇𝑃 𝑚𝑛 + 𝐹𝑁 𝑚𝑛 𝛽 𝑚𝑛 = 𝑇𝑁 𝑚𝑛 𝑇𝑁 𝑚𝑛 + 𝐹𝑃 𝑚𝑛 𝑊𝐷 𝑚𝑛 𝑥 𝑚 𝑘 = 𝛼 𝑚𝑛, 𝑥 𝑚 𝑘 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 0, 𝑥 𝑚 𝑘 𝑛𝑜𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 ∀𝑞 = 𝑛 𝛽 𝑚𝑛, 𝑥 𝑚 𝑘 𝑛𝑜𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 0, 𝑥 𝑚 𝑘 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 ∀𝑞 ≠ 𝑛 𝛿 𝑚 = 𝛿 𝑚1, 𝛿 𝑚2,… , 𝛿 𝑚𝑛 = 𝑇𝑁 𝑚1 𝑇𝑁 𝑚1 + 𝐹𝑃 𝑚1 , 𝑇𝑁 𝑚2 𝑇𝑁 𝑚2 + 𝐹𝑃 𝑚2 , … , 𝑇𝑁 𝑚𝑛 𝑇𝑁 𝑚𝑛 + 𝐹𝑃𝑚𝑛 ∀𝑛 = 1, … , 𝑁
  34. 34. 34 Sensor M Sensor 2 SM S2 S1 α11 Ψ C12 C1N C11 Ψ C21 C22 C2N Ψ CM1 CM2 CMN Ψ Decision Activity level (base classifier) Sensor level (sensor classifier) Network level (sensor fusion) β11 α12 β12 α1N β1N α21 β21 α22 β22 α2N β2N αM1 βM1 αM2 βM2 αMN βMN γ11,…,1N δ11,…,1N γ21,…,2N δ21,…,2N γM1,…,MN δM1,…,MN Sensor 1 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS A Novel Method: Hierarchical Weighted Classifier S1 α11 Ψ C12 C1N C11 β11 α12 β12 α1N β1N S1 γ11,…,1N δ11,…,1N Ψ Decision N activities & M sensors 𝑞 = 𝑎𝑟𝑔𝑚𝑎𝑥 𝑞 𝑂 𝑥 𝑚 𝑂 𝑥 𝑚 = 𝑂 𝑥1 𝑘 , 𝑥2 𝑘 , … , 𝑥 𝑀 𝑘 = 𝑊𝐷 𝑝 𝑞 𝑝 𝑥 𝑝 𝑘 𝑀 𝑝=1 𝑊𝐷 𝑚𝑛 𝑞 𝑚 𝑥 𝑚 𝑘 = 𝛾 𝑚𝑛, 𝑞 𝑚 𝑥 𝑚 𝑘 = 𝑛 𝛿 𝑚𝑛, 𝑞 𝑚 𝑥 𝑚 𝑘 ≠ 𝑛 𝛾 𝑚 = 𝛾 𝑚1, 𝛾 𝑚2,… , 𝛾 𝑚𝑛 = 𝑇𝑃 𝑚1 𝑇𝑃 𝑚1 + 𝐹𝑁 𝑚1 , 𝑇𝑃 𝑚2 𝑇𝑃 𝑚2 + 𝐹𝑁 𝑚2 , … , 𝑇𝑃𝑚𝑛 𝑇𝑃𝑚𝑛 + 𝐹𝑁 𝑚𝑛 𝑞 𝑚 𝑥 𝑚 𝑘 = 𝑎𝑟𝑔𝑚𝑎𝑥 𝑞 𝑂 𝑚 𝑥 𝑚 𝑘 𝑂 𝑚 𝑥 𝑚 𝑘 = 𝑊𝐷 𝑚𝑛 𝑥 𝑚 𝑘 𝑁 𝑛=1 𝛼 𝑚𝑛 = 𝑇𝑃 𝑚𝑛 𝑇𝑃 𝑚𝑛 + 𝐹𝑁 𝑚𝑛 𝛽 𝑚𝑛 = 𝑇𝑁 𝑚𝑛 𝑇𝑁 𝑚𝑛 + 𝐹𝑃 𝑚𝑛 𝑊𝐷 𝑚𝑛 𝑥 𝑚 𝑘 = 𝛼 𝑚𝑛, 𝑥 𝑚 𝑘 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 0, 𝑥 𝑚 𝑘 𝑛𝑜𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 ∀𝑞 = 𝑛 𝛽 𝑚𝑛, 𝑥 𝑚 𝑘 𝑛𝑜𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 0, 𝑥 𝑚 𝑘 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑎𝑠 𝑞 ∀𝑞 ≠ 𝑛 𝛿 𝑚 = 𝛿 𝑚1, 𝛿 𝑚2,… , 𝛿 𝑚𝑛 = 𝑇𝑁 𝑚1 𝑇𝑁 𝑚1 + 𝐹𝑃 𝑚1 , 𝑇𝑁 𝑚2 𝑇𝑁 𝑚2 + 𝐹𝑃 𝑚2 , … , 𝑇𝑁 𝑚𝑛 𝑇𝑁 𝑚𝑛 + 𝐹𝑃𝑚𝑛 ∀𝑛 = 1, … , 𝑁
  35. 35. Evaluation of the Tolerance to Sensor Technological Anomalies • Model validation – Performance in ideal circumstances – Tolerance to sensor failures – Tolerance to sensor faults 35 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  36. 36. • Benchmark dataset: – MIT Activities of Daily Living Dataset* • 9 acts • 5 biaxial accelerometers • 20 subjects (17-48 years old) • Out-of-lab • Experimental setup: 36 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 5 biaxial accelerometers (limbs and trunk) LP Elliptic Filter (Fc = 20Hz) 6 seconds sliding window Mean, STD, kurtosis, MCR, (...) DT, NB, KNN, SVM (as base classifiers) Evaluation of the Tolerance to Sensor Technological Anomalies * http://architecture.mit.edu/house_n/data/Accelerometer/BaoIntille.htm
  37. 37. • Performance in ideal circumstances: 37 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Evaluation of the Tolerance to Sensor Technological Anomalies Evaluated models: - SARC (≡ S) - FFMARC (≡ FF) - HWC Parameters: - Feature sets: 1, 5, 10, 20 feat. - Base classifiers: DT, NB, KNN, SVM Evaluation procedure: - 10-fold CV - 100 iterations
  38. 38. • Performance in ideal circumstances: 38 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Evaluation of the Tolerance to Sensor Technological Anomalies Evaluated models: - SARC (≡ S) - FFMARC (≡ FF) - HWC Parameters: - Feature sets: 1, 5, 10, 20 feat. - Base classifiers: DT, NB, KNN, SVM Evaluation procedure: - 10-fold CV - 100 iterations
  39. 39. • Performance in ideal circumstances: 39 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Evaluation of the Tolerance to Sensor Technological Anomalies Evaluated models: - SARC (≡ S) - FFMARC (≡ FF) - HWC Parameters: - Feature sets: 1, 5, 10, 20 feat. - Base classifiers: DT, NB, KNN, SVM Evaluation procedure: - 10-fold CV - 100 iterations
  40. 40. • Tolerance to sensor failures: 40 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Evaluation of the Tolerance to Sensor Technological Anomalies Evaluated model: - HWC Parameters: - Feature set: 10 feat. - Classifier: KNN Evaluation procedure: - 10-fold CV - 100 iterations Legend: H (hip), W (wrist), A (arm), K (ankle), T (thigh) Baseline Accuracy (Ideal conditions) = 96.34%
  41. 41. • Tolerance to sensor failures: 41 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Evaluation of the Tolerance to Sensor Technological Anomalies 1 missing sensor Evaluated model: - HWC Parameters: - Feature set: 10 feat. - Classifier: KNN Evaluation procedure: - 10-fold CV - 100 iterations Legend: H (hip), W (wrist), A (arm), K (ankle), T (thigh) Baseline Accuracy (Ideal conditions) = 96.34%
  42. 42. • Tolerance to sensor failures: 42 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Evaluation of the Tolerance to Sensor Technological Anomalies 1 missing sensor 2 missing sensors Evaluated model: - HWC Parameters: - Feature set: 10 feat. - Classifier: KNN Evaluation procedure: - 10-fold CV - 100 iterations Legend: H (hip), W (wrist), A (arm), K (ankle), T (thigh) Baseline Accuracy (Ideal conditions) = 96.34%
  43. 43. • Tolerance to sensor failures: 43 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Evaluation of the Tolerance to Sensor Technological Anomalies 1 missing sensor 2 missing sensors 3 missing sensors 4 missing sensors Evaluated model: - HWC Parameters: - Feature set: 10 feat. - Classifier: KNN Evaluation procedure: - 10-fold CV - 100 iterations Legend: H (hip), W (wrist), A (arm), K (ankle), T (thigh) Baseline Accuracy (Ideal conditions) = 96.34%
  44. 44. • Tolerance to sensor faults: 44 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Evaluation of the Tolerance to Sensor Technological Anomalies Ideal case Dynamic range shortening
  45. 45. • Tolerance to sensor faults: 45 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Evaluation of the Tolerance to Sensor Technological Anomalies Evaluated models: - SARC - FFMARC - HWC Parameters: - Feature set: 10 feat. - Classifier: KNN Evaluation procedure: - 10-fold CV - 100 iterations AR model/ #faulty sensors 0 1 2 3 4 5 New dynamic range= 30% original dynamic range  [-3g,3g] SARC (hip) 82±5 66±4 - - - - SARC (wrist) 88±5 54±6 - - - - SARC (arm) 80±3 58±7 - - - - SARC (ankle) 83±4 58±8 - - - - SARC (thigh) 89±2 72±4 - - - - FFMARC 97±2 88±4 76±5 61±8 42±11 39±13 HD 90±3 85±4 80±9 68±13 59±16 53±20 MV 82±6 79±5 67±7 43±10 36±14 31±19 HWC 96±2 96±2 93±3 86±5 73±8 65±14 New dynamic range= 10% original dynamic range  [-1g,1g] SARC (hip) 82±5 21±11 - - - - SARC (wrist) 88±5 18±9 - - - - SARC (arm) 80±3 26±14 - - - - SARC (ankle) 83±4 21±7 - - - - SARC (thigh) 89±2 20±6 - - - - FFMARC 97±2 70±5 41±8 17±15 21±11 18±9 HD 90±3 80±6 59±13 42±12 30±17 21±16 MV 82±6 77±6 46±11 38±10 27±13 26±8 HWC 96±2 94±2 87±6 53±2 27±17 25±19
  46. 46. Conclusions • Assuming a lifelong invariant sensor setup is unrealistic and may lead to a malfunctioning of the activity recognition system • Body-worn sensors are subject to faults (signal degradation) and failures (absence of signal) normally unforeseen at design and runtime • Classic activity recognition approaches (SARC, FFMARC) are not capable of dealing with sensor failures and are of limited utility under the effect of sensor faults • The proposed alternate model (HWC) renders similar performance to standard activity recognition models in ideal conditions, proves to be robust to sensor failures and a relevant tolerance to sensor faults 46 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  47. 47. Robustness of AR systems to sensor deployment variations INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Objective: “Research the robustness of standard AR systems to unforeseen variations in the sensor deployment, as well as contribute with an alternate approach to cope with these practical anomalies”
  48. 48. Problem Statement 48 SENSOR DEPLOYMENT CHANGES Are activity recognition systems flexible enough to allow users to wear the sensors on their own? Is it possible to keep the systems functioning under the effects of sensor displacement? Activity recognition process Body motion sensing Signal processing and reasoning Recognition of activities INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  49. 49. Sensor Displacement • Categories of sensor displacement – Static: position changes can remain static across the execution of many activity instances, e.g. when sensors are attached with a displacement each day – Dynamic: effect of loose fitting of the sensors, e.g. when embedded into clothes • Sensor displacement  new sensor position  signal space change • Sensor displacement effects depends on – Original/end position and body part – Activity/gestures/movements performed – Sensor modality 49 Sensor displacement = rotation + translation (angular displacement) (linear displacement) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  50. 50. Sensor Displacement Effects Changes in the signal space propagates through the activity recognition chain (e.g., variations in the feature space) RCIDEAL LCIDEAL= LCSELF 50 RCSELF ≠ RCIDEAL INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  51. 51. Sensor Displacement in AR: Related Work • Features invariant to sensor displacement – Heuristics (Kunze08) – Genetic algorithm for feature selection (Förster09a) • Feature distribution adaptation – Covariate shift unsupervised adaptation (Bayati09) – Online-supervised user-based calibration (Förster09b) • Classification (dis)similarity – Output classifiers correlation (Sagha11) K. Kunze and P. Lukowicz. Dealing with sensor displacement in motion-based onbody activity recognition systems. In 10th international conference on Ubiquitous computing, pp. 20–29, 2008. K. Förster, P. Brem, D. Roggen, and G. Tröster. Evolving discriminative features robust to sensor displacement for activity recognition in body area sensor networks. In Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2009 5th International Conference on, pp. 43–48, 2009. H. Bayati, J. del R Millan, and R. Chavarriaga. Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition. In Wearable Computers (ISWC), 2011 15th Annual International Symposium on, pp. 71–78, June 2011. K. Förster, D. Roggen, and G. Tröster. Unsupervised classifier self-calibration through repeated context occurrences: Is there robustness against sensor displacement to gain? In Proc. 13th IEEE Int. Symposium on Wearable Computers (ISWC), pp. 77–84, 2009. H. Sagha, J. R. del Millán, and R. Chavarriaga. Detecting and rectifying anomalies in Opportunistic sensor networks 8th Int. Conf. on Networked Sensing Systems, pp. 162-–167, 2011 51 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  52. 52. Approaches to Investigate on Sensor Displacement Synthetically Modeled Sensor Displacement Realistic Sensor Displacement INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 52
  53. 53. Approaches to Investigate on Sensor Displacement Synthetically Modeled Sensor Displacement INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 53
  54. 54. Synthetically Modeled Sensor Displacement • Sensor rotation  Rotational noise (RN) • Sensor translation  Additive noise (AN) • Examples: 54 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 𝑀 𝑅𝑁 = 𝑐 𝜃 𝑐 𝜓 − 𝑐 𝜙 𝑠 𝜓 + 𝑠 𝜙 𝑠 𝜃 𝑐(𝜓) 𝑠 𝜙 𝑠 𝜓 + 𝑐 𝜙 𝑠 𝜃 𝑐(𝜓) 𝑐 𝜃 𝑠 𝜓 𝑐 𝜙 𝑐 𝜓 + 𝑠 𝜙 𝑠 𝜃 𝑠(𝜓) −𝑠 𝜙 𝑐 𝜓 + 𝑐 𝜙 𝑠 𝜃 𝑠(𝜓) − 𝑠 𝜃 𝑠 𝜙 𝑐 𝜃 𝑐 𝜙 𝑐 𝜃 𝑥 𝑟𝑜𝑡 𝑦 𝑟𝑜𝑡 𝑧 𝑟𝑜𝑡 = 𝑀 𝑅𝑁 × 𝑥 𝑟𝑎𝑤 𝑦𝑟𝑎𝑤 𝑧 𝑟𝑎𝑤 𝑥𝑡𝑟 𝑦𝑡𝑟 𝑧𝑡𝑟 = 𝑇𝐴𝑁 + 𝑥 𝑟𝑎𝑤 𝑦𝑟𝑎𝑤 𝑧 𝑟𝑎𝑤 𝑇𝐴𝑁 = 𝜇 𝐴𝑁 + 𝜎𝐴𝑁 2 + 𝑟𝑎𝑛𝑑_𝑛𝑜𝑟𝑚𝑎𝑙_𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 (𝜇 𝐴𝑁=0) 0 1 2 3 -2 0 2 Acceleration(g) Time (s) Original 0 1 2 3 -2 0 2 Time (s) RN =15º 0 1 2 3 -2 0 2 Time (s) RN =90º 0 1 2 3 -2 0 2 Time (s) AN =0.1g 0 1 2 3 -2 0 2 Time (s) AN =0.5g Original RN =15º RN =90º AN =0.1g AN =0.5g 0 1 2 3 -2 0 2 Acceleration(g) Time (s) Original 0 1 2 3 -2 0 2 Time (s) RN =15º 0 1 2 3 -2 0 2 Time (s) RN =90º 0 1 2 3 -2 0 2 Time (s) AN =0.1g 0 1 2 3 -2 0 2 Time (s) AN =0.5g 0 1 2 3 -2 0 2 Acceleration(g) Time (s) Original 0 1 2 3 -2 0 2 Time (s) RN =15º 0 1 2 3 -2 0 2 Time (s) RN =90º 0 1 2 3 -2 0 2 Time (s) AN =0.1g 0 1 2 3 -2 0 2 Time (s) AN =0.5g Walking Sitting Proposed in: H. Sagha, J. R. del Millán, and R. Chavarriaga. Detecting and rectifying anomalies in Opportunistic sensor networks. 8th Int. Conf. on Networked Sensing Systems, pp. 162 – 167, 2011
  55. 55. • Benchmark dataset: – MIT Activities of Daily Living Dataset* • 9 acts • 5 biaxial accelerometers • 20 subjects (17-48 years old) • Out-of-lab • Experimental setup: 55 Evaluation of the Robustness to Sensor Displacement (Synthetic) * http://architecture.mit.edu/house_n/data/Accelerometer/BaoIntille.htm INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 5 biaxial accelerometers (limbs and trunk) LP Elliptic Filter (Fc = 20Hz) 6 seconds sliding window 10 feat. set KNN (as base classifier)
  56. 56. HWC (multi-sensor)FFMARC (multi-sensor)SARC (single sensor) • Performance drop under the effects of sensor rotation and translation: 56 Evaluation of the Robustness to Sensor Displacement (Synthetic) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS RotationTranslation
  57. 57. • Performance drop under the effects of sensor rotation and translation: HWC (multi-sensor)FFMARC (multi-sensor)SARC (single sensor) 57 Evaluation of the Robustness to Sensor Displacement (Synthetic) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 30% RotationTranslation 23% 3% 8% 8% 20% 15% 25% 5% 15% 1% 4%
  58. 58. HWC (multi-sensor)FFMARC (multi-sensor)SARC (single sensor) • Performance drop under the effects of sensor rotation and translation: 58 Evaluation of the Robustness to Sensor Displacement (Synthetic) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 70% RotationTranslation 50% 20% 55% 6% 15%3% 8% 8% 20% 15% 45% 75% 5% 15% 43% 30% 1% 4% 4% 10%25% 30% 23%
  59. 59. Approaches to Investigate on Sensor Displacement Realistic Sensor Displacement INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 59
  60. 60. • No dataset for studying the effects of sensor displacement!  • Observe – Variability introduced with respect to the ideal setup when the sensors are self-placed by the users – Effects of large sensor displacements (extreme de-positioning) • Scenarios – Ideal-placement – Self-placement – Induced-displacement Implementing Realistic Sensor Displacement Ideal Self Induced 60 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS NEW DATASET* (REALDISP) *Freely available at: www.ugr.es/~oresti/datasets
  61. 61. REALDISP Dataset: Study Setup • Cardio-fitness room • 9 IMUs (9DoF)  ACC, GYR, MAG • Laptop  data storage and labeling* • Camera  offline data validation • 17 volunteers (22-37 years old) *Annotation tool: http://crnt.sourceforge.net/CRN_Toolbox/Home.html 61 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  62. 62. REALDISP Dataset: Activity Set • Activities intended for: – Body-general motion: Translation | Jumps | Fitness – Body-part-specific motion: Trunk | Upper-extremities | Lower-extremities 62 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  63. 63. • Experimental setup: • Studies: – AR systems: SARC, FFMARC, HWC – Settings: Ideal-placement, Self-placement, Induced-displacement – Scenarios: 10 activities, 20 activities, 33 activities (all) • Evaluation procedure – 10-fold CV, 100 iterations 63 Evaluation of the Robustness to Sensor Displacement (Realistic) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 9 triaxial accelerometers (all limbs and trunk) No preprocessing (raw data) 6 seconds sliding window FS1=mean FS2=mean,std FS3=mean,std, max,min,mcr DT, KNN, NB (as base classifiers)
  64. 64. HWC (multi-sensor)FFMARC (multi-sensor)SARC (single sensor) 64 Evaluation of the Robustness to Sensor Displacement (Realistic) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS IdealSelfInduced
  65. 65. HWC (multi-sensor)FFMARC (multi-sensor)SARC (single sensor) 65 Evaluation of the Robustness to Sensor Displacement (Realistic) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS IdealSelfInduced 13% 25% 25% 45% 3% 13%
  66. 66. HWC (multi-sensor)FFMARC (multi-sensor)SARC (single sensor) 66 Evaluation of the Robustness to Sensor Displacement (Realistic) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS IdealSelfInduced 13% 25% 15% 50% 25% 45% 30% 45% 3% 13% 5% 15%
  67. 67. HWC (multi-sensor)FFMARC (multi-sensor)SARC (single sensor) 67 Evaluation of the Robustness to Sensor Displacement (Realistic) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS IdealSelfInduced 13% 25% 15% 50% 50% 15%25% 45% 30% 45% 45% 30% 3% 13% 5% 15% 5% 25%
  68. 68. Conclusions • Classic activity-aware systems assume a predefined sensor deployment that further remains unchanged during runtime, which are not lifelike assumptions • Body-worn inertial sensors are subject to deployment changes (displacement) in real-world contexts, potentially leading to signal variations with respect to ideal patterns • Activity recognition systems proves to be more sensitive to sensor rotations than translations, specially when located on body parts of reduced mobility • Standard models (SARC,FFMARC) suffer from a critical performance worsening when the sensors are largely depositioned or self-placed by the users • The HWC significantly outperforms the tolerance of standard activity recognition models (up to 30%), effectively showing outstanding capabilities to assimilate the changes introduced during the self-placement of the sensors and to moderately overcome the situation of largely depositioned sensors 68 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  69. 69. Supporting AR systems network changes: instruction of newcomer sensors INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Objective: “Study the capacity of standard AR systems to support unforeseen changes in the sensor network, as well as contribute with an alternate approach to cope with these topological variations”
  70. 70. Problem Statement 70 SENSOR INFRASTRUCTURE CHANGES INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Collect a training dataset Train and test the model The AR system is “ready” Do we need to collecta new dataset each time the sensor topology changes? Is it possible to leverage the knowledge of a functional system to instructa system to operate on a newcomer sensor? Activity recognition system design
  71. 71. Infraestructure Changes: Newcomer Sensors 71 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Sensor replacement (repair/upgrade) Sensor addition (redundancy) Sensor discovery (opportunistic use)
  72. 72. Transfer learning Instruction of Newcomer Sensors 72 Teacher INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Classic approach Limitations: - Predefined setup and deployments - System designer involvement - User/s involvement Learner “Mechanism, ability or means to recognize and apply knowledge and skills learned in previous tasks or domains to novel tasks or domains” Collection of a new dataset for each possible scenario
  73. 73. Transfer Learning in AR: Related Work • Transfer between wearable sensors – Translation of locomotion recognition capabilities (Calatroni11) • Model parameters • Labels • Transfer between ambient sensors – Translation among smart homes through meta-featuring (van Kasteren10) • Common meta-feature space • Limitations – Long time scales operation – Incomplete transfer – Difficult transfer across modalities A. Calatroni, D. Roggen, and G. Tröster, “Automatic transfer of activity recognition capabilities between body-worn motion sensors: Training newcomers to recognize locomotion,” in Proc. 8th Int Conf on Networked Sensing Systems, 2011. T. van Kasteren, G. Englebienne, and B. Kröse, “Transferring knowledge of activity recognition across sensor networks,” in Proc. 8th Int. Conf on Pervasive Computing, 2010. INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 73
  74. 74. Multimodal Transfer Methods • System identification (signal level) • Transfer methods (reasoning level) Ψ𝐴→𝐵 𝑡 Sensor Domain A Sensor Domain B0 1 2 3 -0.5 0 0.5 1 1.5 Time (s) Acceleration(G) 0 1 2 3 -1 0 1 2 Time (s) Position(m) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of activity models (features + labels, classification models) Transfer of activity templates (patterns + labels) 74
  75. 75. Transfer of Activity Templates 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) L1 L2 L3 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) • Transfer of the recognition capabilities of an existing source system (S) that operates on activity templates (patterns) to an untrained target system (T) that lacks from these capabilities INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 75
  76. 76. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) L1 L2 L3 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) Coexistence… (T) 0 20 40 -1 0 1 2 Time (s) Position(m) 0 20 40 -1 0 1 2 Time (s)Acceleration(G) Transfer of Activity Templates INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS (1) Both systems coexists during a certain period of time 76
  77. 77. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) L1 L2 L3 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS (2) A mapping function between source and target domains is discovered through system identification (MIMO model) Transfer of Activity Templates Ψ𝑆→𝑇 𝑡 : 𝑋𝑆(𝑡) → 𝑋 𝑇(𝑡) ≈ 𝑋 𝑇(𝑡) 77
  78. 78. System S (source domain) System T (target domain) Signal level Reasoning level 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) L1 L2 L3 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) Ψ𝑆→𝑇 𝑡 : 𝑋𝑆(𝑡) → 𝑋 𝑇(𝑡) ≈ 𝑋 𝑇(𝑡) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS (3) The activity templates are translated from source to target domain Transfer of Activity Templates 78
  79. 79. Signal level Reasoning level 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) L1 L2 L3 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) Ψ𝑆→𝑇 𝑡 : 𝑋𝑆(𝑡) → 𝑋 𝑇(𝑡) ≈ 𝑋 𝑇(𝑡) 0 1 2 3 -1 0 1 2 Time (s) Position(m) X Y Z INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Templates (3) The activity templates are translated from source to target domain System S (source domain) System T (target domain) L3 79
  80. 80. System S (source domain) System T (target domain) Signal level Reasoning level 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) L1 L2 L3 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) Ψ𝑆→𝑇 𝑡 : 𝑋𝑆(𝑡) → 𝑋 𝑇(𝑡) ≈ 𝑋 𝑇(𝑡) 0 1 2 3 -0.5 0 0.5 1 1.5 Time (s) Acceleration(G) ^X ^Y ^Z INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Templates (3) The activity templates are translated from source to target domain 0 1 2 3 -1 0 1 2 Time (s) Position(m) X Y Z L3 L3 80
  81. 81. System S (source domain) System T (target domain) Signal level Reasoning level L1 L2 L3 Ψ𝑆→𝑇 𝑡 : 𝑋𝑆(𝑡) → 𝑋 𝑇(𝑡) ≈ 𝑋 𝑇(𝑡) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Templates • System identification – 3) The activity templates are translated from source to target domain 0 0.5 1 1.5 Acceleration(G) X Y Z ^X ^Y ^Z 81
  82. 82. System S (source domain) System T (target domain) Signal level Reasoning level 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) L1 L2 L3 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) Ψ𝑆→𝑇 𝑡 : 𝑋𝑆(𝑡) → 𝑋 𝑇(𝑡) ≈ 𝑋 𝑇(𝑡) 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 5 10 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z L1 L2 L3 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 5 10 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 5 10 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Templates (4) Once the templates have been translated, the target system is ready for activity detection 82
  83. 83. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level L1 L2 L3 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 5 10 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z L1 L2 L3 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 5 10 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 5 10 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Templates (4) Once the templates have been translated, the target system is ready for activity detection Instruction completed! 83
  84. 84. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Models • Transfer of the recognition capabilities of an existing source system (S) that operates on activity models (features + classification model) to an untrained target system (T) that lacks from these capabilities 84
  85. 85. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level Coexistence… (T) 0 20 40 -1 0 1 2 Time (s) Position(m) 0 20 40 -1 0 1 2 Time (s) Acceleration(G) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Models (1) Both systems coexists during a certain period of time 85
  86. 86. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level Ψ 𝑇→𝑆 𝑡 : 𝑋 𝑇(𝑡) → 𝑋𝑆(𝑡) ≈ 𝑋𝑆(𝑡) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Models (2) A mapping function between target and source domains is discovered through system identification (MIMO model) 86
  87. 87. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level Ψ 𝑇→𝑆 𝑡 : 𝑋 𝑇(𝑡) → 𝑋𝑆(𝑡) ≈ 𝑋𝑆(𝑡) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Models (3) The source activity models are translated to the target domain so both use the same activity models 87
  88. 88. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level Ψ 𝑇→𝑆 𝑡 : 𝑋 𝑇(𝑡) → 𝑋𝑆(𝑡) ≈ 𝑋𝑆(𝑡) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Models (3) The source activity models are translated to the target domain so both use the same activity models; these activity models also define the target activity recognition system 88
  89. 89. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level Ψ 𝑇→𝑆 𝑡 : 𝑋 𝑇(𝑡) → 𝑋𝑆(𝑡) ≈ 𝑋𝑆(𝑡) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Models (4) The target system continuously translate its signals into the source domain to operate on the transferred recognition system 0 1 2 3 4 -0.5 0 0.5 1 1.5 X Y Z 89
  90. 90. 𝑋𝑆(𝑡) 𝑋 𝑇(𝑡) System S (source domain) System T (target domain) Signal level Reasoning level Ψ 𝑇→𝑆 𝑡 : 𝑋 𝑇(𝑡) → 𝑋𝑆(𝑡) ≈ 𝑋𝑆(𝑡) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Transfer of Activity Models (4) The target system continuously translate its signals into the source domain to operate on the transferred recognition system; since then it is ready for activity detection Instruction completed! 0 1 2 3 4 -1 0 1 2 ^X ^Y ^Z 90
  91. 91. Evaluation of Multimodal Transfer • Models validation – Transfer between IMU and IMU (Identical Domain Transfer) – Transfer between Kinect and IMU (Cross Domain Transfer) 91 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) L1 L2 L3 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) 0 2 4 6 -1 0 1 2 Time (s) Position(m) X Y Z 0 2 4 -1 0 1 2 Time (s) Position(m) X Y Z 0 1 2 3 -1 0 1 2 Time (s) Position(m) 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 5 10 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z L1 L2 L3 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 5 10 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 5 10 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z 0 2 4 -1 0 1 2 Time (s) Acceleration(G) ^X ^Y ^Z Transfer of Activity Templates Transfer of Activity Models 0 1 2 3 4 -1 0 1 2 ^X ^Y ^Z
  92. 92. Multimodal Kinect-IMU Dataset: Study Setup INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS *Freely available at: www.ugr.es/~oresti/datasets 92
  93. 93. Multimodal Kinect-IMU Dataset: Study Setup INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS MTx XSENS IMUs - 3D ACC + (3D GYR, 3D MAG, 4D QUA) - Sampling rate 30Hz Applications 93Xsens data logger  http://crnt.sourceforge.net/CRN_Toolbox/References.html
  94. 94. Multimodal Kinect-IMU Dataset: Study Setup INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS MICROSOFT KINECT - RGB cam + IR cam + IR led - Depth map (0.5-6m) - 15 joints skeleton tracking - 3D position - Tracking range (1.2-3.5m) - Sampling rate 30Hz Applications 94 Kinect data logger  http://code.google.com/p/qtkinectwrapper/
  95. 95. Multimodal Kinect-IMU Dataset: Scenarios Geometric Gestures (HCI) 48 instances per gesture Other scenarios were also collected as part of this dataset (more info at www.ugr.es/~oresti/datasets) 95
  96. 96. Multimodal Kinect-IMU Dataset: Scenarios Geometric Gestures (HCI) Idle (Background) ~5 min of data48 instances per gesture Other scenarios were also collected as part of this dataset (more info at www.ugr.es/~oresti/datasets) 96
  97. 97. Transfer between IMU and IMU • Analyzed transfers – Transfer of Activity Templates and Activity Models from: • RLA (3D acceleration) to RUA (3D acceleration) • RUA (3D acceleration) to RLA (3D acceleration) • RUA (3D acceleration) to BACK (3D acceleration) • BACK (3D acceleration) to RUA (3D acceleration) • RLA (3D acceleration) to BACK (3D acceleration) • BACK (3D acceleration) to RLA (3D acceleration) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 97
  98. 98. Evaluation of Transfer between IMU and IMU • Mapping: – Model  MIMO3x3 mapping with 10 tap delay – Types • Problem-domain mapping (PDM) • Gesture-specific mapping (GSM) • Unrelated-domain mapping (UDM) – Learning  100 samples (~3.3s) • Activity recognition model: INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS Triaxial acceleration (IMU) No preprocessing (raw data) Instance based segmentation FS=max,min KNN (standard classifier) 98
  99. 99. Evaluation of Transfer between IMU and IMU • Transfer of Activity Templates: INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 99BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping LA=lower arm UA=upper arm B=back
  100. 100. Evaluation of Transfer between IMU and IMU • Transfer of Activity Templates: INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 100BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping LA=lower arm UA=upper arm B=back <1% <3%
  101. 101. Evaluation of Transfer between IMU and IMU • Transfer of Activity Templates: INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 101BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping LA=lower arm UA=upper arm B=back <1% <3% 35% 15% 17% 28% 35% 28% 10% 10%
  102. 102. Evaluation of Transfer between IMU and IMU • Transfer of Activity Templates: INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 102BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping LA=lower arm UA=upper arm B=back <1% <3% 12% 20% 35% 55% 15% 17% 28% 35% 60% 28% 30% 50% 10% 10%
  103. 103. Evaluation of Transfer between IMU and IMU • Transfer of Activity Models: INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 103BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping LA=lower arm UA=upper arm B=back
  104. 104. Evaluation of Transfer between IMU and IMU • Transfer of Activity Models: INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 104BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping LA=lower arm UA=upper arm B=back
  105. 105. Transfer between Kinect and IMU • Analyzed transfers – Transfer of Activity Templates (Kinect to IMU) : • HAND (3D position)  RLA (3D acceleration) • HAND (3D position)  RUA (3D acceleration) • HAND (3D position)  BACK (3D acceleration) – Transfer of Activity Models (IMU to Kinect): • RLA (3D acceleration)  HAND (3D position) • RUA (3D acceleration)  HAND (3D position) • BACK (3D acceleration)  HAND (3D position) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 105
  106. 106. Evaluation of Transfer between Kinect and IMU • Mapping: – Model  MIMO3x3 mapping with 10 tap delay – Types • Problem-domain mapping (PDM) • Gesture-specific mapping (GSM) • Unrelated-domain mapping (UDM) – Learning  100 samples (~3.3s) • Activity recognition model: INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 106 Triaxial acceleration (IMU) / Triaxial position (KINECT) No preprocessing (raw data) Instance based segmentation FS=max,min KNN (standard classifier)
  107. 107. Evaluation of Transfer between Kinect and IMU • Transfer of Activity Templates (From Kinect to IMU) • Transfer of Activity Models (From IMU to Kinect) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 107BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping RLA= right lower arm RUA= right upper arm BACK=back KINECT=hand
  108. 108. Evaluation of Transfer between Kinect and IMU • Transfer of Activity Templates (From Kinect to IMU) • Transfer of Activity Models (From IMU to Kinect) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 108BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping RLA= right lower arm RUA= right upper arm BACK=back KINECT=hand <4% <4% <8%<6%
  109. 109. Evaluation of Transfer between Kinect and IMU • Transfer of Activity Templates (From Kinect to IMU) • Transfer of Activity Models (From IMU to Kinect) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 109BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping RLA= right lower arm RUA= right upper arm BACK=back KINECT=hand <4% <4% <8%<6% 30% 45% 35% 60%
  110. 110. Evaluation of Transfer between Kinect and IMU • Transfer of Activity Templates (From Kinect to IMU) • Transfer of Activity Models (From IMU to Kinect) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS 110BS=baseline source | BT=baseline target | PDM=problem-domain mapping | GSM=gesture-specific mapping | UDM=unrelated-domain mapping RLA= right lower arm RUA= right upper arm BACK=back KINECT=hand <4% 35% 50% <4% <8%<6% 30% 45% 35% 60% 55% 30%35%35%
  111. 111. Evaluation of Transfer between Kinect and IMU • Transfer of Activity Templates (From Kinect to IMU) • Transfer of Activity Models (From IMU to Kinect) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS From Kinect to IMU (RLA) From IMU (RLA) to Kinect FS1=mean FS2=max,min 11130 samples = 1 s
  112. 112. Evaluation of Transfer between Kinect and IMU • Transfer of Activity Templates (From Kinect to IMU) • Transfer of Activity Models (From IMU to Kinect) INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS From Kinect to IMU (RLA) From IMU (RLA) to Kinect FS1=mean FS2=max,min 11230 samples = 1 s 25% 20%
  113. 113. Conclusions • Classical training procedures are not practical to instruct newcomer sensors in dynamically varying and evolvable activity recognition setups • A novel multimodal transfer learning model is proposed to translate the recognition capabilities of an existing system to a new untrained system, at runtime and without expert or user intervention • As few as a single gesture (≈3 seconds) of data is enough to learn a mapping model that captures the underlying relation between systems of identical or different modality • The transfer between IMUs across close-by limbs achieves a recognition accuracy superior to 97% (>2% below baseline), and 95% (>4% below baseline) for the transfer between Kinect and IMU, independently of the direction of the transfer • Low-variance data unrelated to the activities of interest can be also used to learn a mapping, albeit with more data 113 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  114. 114. Conclusions and future work INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  115. 115. Contributions • Identification of the requirements and challenges posed by AR systems in real- world conditions • Evaluation of the tolerance of standard AR systems to sensor technological anomalies, particularly sensor failures and faults • Definition and development of a novel model, so-called HWC, to overcome the effects of sensor failures and faults. Evaluation of the robustness of the proposed HWC model to the effects of sensor failures and faults • Evaluation of the tolerance of standard AR systems to sensor deployment variations, particularly static and dynamic sensor displacements • Evaluation of the robustness of the proposed HWC model to the effects of sensor displacements • Definition, development and validation of a novel multimodal transfer learning method that operates at runtime, with low overhead and without user or system designer intervention 115 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  116. 116. Contributions • Collection and curation of an innovative benchmark dataset to investigate the effects of sensor displacement, introducing the concept of ideal-placement, self-placement and induced-displacement. This dataset includes a wide range of physical activities, sensor modalities and participants. Apart from investigating sensor displacement, the dataset lend itself for benchmarking activity recognition techniques in ideal conditions. The dataset is publicly available to the research community at http://www.ugr.es/~oresti/datasets • Collection and curation of a novel multimodal dataset to investigate transfer learning among ambient sensing and wearable sensing systems. The dataset could be also used for gesture spotting and continuous activity recognition. The dataset is publicly available to the research community at http://www.ugr.es/~oresti/datasets 116 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  117. 117. Selected Publications • International Journals (SCI-indexed) – Banos, O., Toth M. A., Damas, M., Pomares, H., Rojas, I. Dealing with the effects of sensor displacement in wearable activity recognition. Sensors, MDPI (2014) [Under review] – Banos, O., Damas, M., Guillen, A., Herrera, L.J., Pomares, H., Rojas, I. Multi-sensor fusion based on asymmetric decision weighting for robust activity recognition. Neural Processing Letters, Springer (2014) [Under review] – Banos, O., Galvez, J. M., Damas, M., Pomares, H., Rojas, I. Window size impact in activity recognition. Sensors, MDPI, vol. 14, no. 4, pp. 6474-6499 (2014) – Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B., Valenzuela, O. Human activity recognition based on a sensor weighting hierarchical classifier. Soft Computing, Springer, vol. 17, pp. 333-343 (2013) – Banos, O., Damas, M., Pomares, H., Rojas, I. On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition. Sensors, MDPI, vol. 12, no. 6, pp. 8039-8054 (2012) – Banos, O., Damas, M., Pomares, H., Prieto, A., Rojas, I.: Daily Living Activity Recognition based on Statistical Feature Quality Group Selection. Expert Systems with Applications, Elsevier, vol. 39, no. 9, pp. 8013-8021 (2012) 117 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  118. 118. Selected Publications • Book chapters – Banos, O., Toth M. A., Damas, M., Pomares, H., Rojas, I. Amft, O.: Evaluation of inertial sensor displacement effects in activity recognition systems. Science and Supercomputing in Europe (Information & Communication Technologies), HPC-Europe 2 (2013) ISBN: 978-84-338-5400-1 • Conference papers – Banos, O., Damas, M., Pomares, H., Rojas, I.: Handling displacement effects in on-body sensor- based activity recognition. In: Proceedings of the 5th International Work-conference on Ambient Assisted Living an Active Ageing (IWAAL 2013), San Jose, Costa Rica, December 2-6, (2013) [BEST PAPER AWARD] – Banos, O., Damas, M., Pomares, H., Rojas, I.: Activity recognition based on a multi-sensor meta-classifier. In: Proceedings of the 2013 International Work Conference on Neural Networks (IWANN 2013), Tenerife, June 12-14, (2013) – Banos, O., Toth, M. A., Damas, M., Pomares, H., Rojas, I., Amft, O.: A benchmark dataset to evaluate sensor displacement in activity recognition. In: Proceedings of the 14th International Conference on Ubiquitous Computing (Ubicomp 2012), Pittsburgh, USA, September 5-8, (2012) 118 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  119. 119. Selected Publications • Conference papers (cont.) – Banos, O., Calatroni, A., Damas, M., Pomares, H., Rojas, I., Troester, G., Sagha, H., Millan, J. del R., Chavarriaga, R., Roggen, D.: Kinect=IMU? Learning MIMO Signal Mappings to Automatically Translate Activity Recognition Systems Across Sensor Modalities. In: Proceedings of the 16th annual International Symposium on Wearable Computers (ISWC 2012), Newcastle, United Kingdom, June 18-22 (2012) – Banos, O., Damas, M., Pomares, H., Rojas, I.: Human multisource activity recognition for AAL problems. In: Proceedings of the 5th International Symposium on Ubiquitous Computing and Ambient Intelligence (UCAmI 2011), Riviera Maya, Mexico, December 5-9, (2011) – Banos, O., Damas, M., Pomares, H., Rojas, I.: Recognition of Human Physical Activity based on a novel Hierarchical Weighted Classification scheme. In: Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN 2011), IEEE, San Jose, California, July 31-August 5, (2011) – Banos, O., Pomares, H., Rojas, I.: Ambient Living Activity Recognition based on Feature-set Ranking Using Intelligent Systems. In: Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN 2010), IEEE, Barcelona, July 18-23, (2010) 119 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  120. 120. Future Work • Collection of new large standard datasets • Dynamic reconfiguration of the HWC • Self-adaptive HWC • Tolerance to other sensor technological and topological anomalies • Multiple trainers and complex modalities in transfer learning • Integration in commercial systems and end-user applications 120 INTRODUCTION TECHNOLOGICAL ANOMALIES DEPLOYMENT VARIATIONS NETWORK CHANGES CONCLUSIONS
  121. 121. ¡Gracias a todos! 121

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