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Earables for Personal-scale Behaviour Analytics

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Wearables are embracing AI, transforming the way we live, act, learn and behave as social human beings. However, what’s at stake for this “PopSci rhetoric” to happen is nothing short of an enormous multifaceted challenge. In this talk, I will explore the system and algorithmic challenges in modelling behaviour in this augmented human era. In particular, I will discuss how an "Earable" can be used as a multi-sensory computational platform to learn and infer human behaviour and to design ultra-personal connected services.

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Earables for Personal-scale Behaviour Analytics

  1. 1. Fahim Kawsar @raswak http://www.fahim-kawsar.net Earables for Personal-Scale Behaviour Analytics
  2. 2. Everything is connected - The Rise of Sensory Systems @raswak
  3. 3. Cognitive Assistant - Seamless Extension of the Inner Human Cognition 24/7 Contextual Assistant Strengthening Willpower Safety & Adherence Assistive Guidance @raswak
  4. 4. Help us to communicate better Help us to sleep better Help us to focus better Help us to remember and recall better @raswak
  5. 5. @raswak - Cross Device Interactions - Spans Across Space and Time - Ultra Personalised Behavioural UX Accessing everything Controlling everything Understanding everything Sensing + Understanding you and the world around you
  6. 6. @raswak Behavioural UX
  7. 7. AI Assisted Quantified Enterprise Implication: People and Space Analytics Location is the key context. Social signals can be extracted from location traces Web Summit Largest Tech Conference in the Planet 2015 @ Dublin 40K+ Attendees, 134 Countries ±6000 Sq. Meter Startups, Entrepreneurs, Investors … Long Term Feedback Actionable Feedback Community Driven Feedback Privacy plays a critical role in users’ decision making process Form needs an primary established purpose for sustainable engagement Lessons Understand, quantify and radically transform how people interact, feel, collaborate and work together in the real enterprise for personal, group and larger organisation efficiency. @raswak ACM UbiComp 2015, 2016, ICMI 2016, MobileHCI 2016
  8. 8. Actionable and Longterm Feedback at the right moment is key to sustainable engagement Battery performance is absolutely important Privacy plays a critical role in users’ decision making process Form needs an primary established purpose for sustainable engagement Lessons @raswak
  9. 9. 1980 1990 2000 2010 2014 7 8 5 6 Which personal device will you carry in 2025? @raswak
  10. 10. Enterprise wearables market to reach $55Billion by 2022 ABI Research *424% increase from the $10.5 billion market value in 2017 @raswak
  11. 11. @raswak
  12. 12. - With immediate and subtle interaction - Unique placement for robust sensing - Intimate and privacy preserving - With an established purpose - Aesthetically beautiful - Ergonomically comfortable The most personal device yet Earables @raswak Sense Learn Act Sensor Sensor AI/ML Models
  13. 13. @raswak eSense Earable Signal-to-Noise Ratio (SNR) of eSense in comparison to a smartphone and a smartwatch concerning motion and audio sensing. CSR Processor Flash Memory 45 mAh Li-Po Battery Contact Charging Speaker 6-axis IMU Sensor MicrophonePush Button Multi Colour LED Bluetooth/BLE Size : 18x18x20 mm Weight: 20 g IEEE Pervasive 2018
  14. 14. @raswak DEMO
  15. 15. eSense Earable Over 90% accuracy with accelerometer only Can further expand the set of head gestures to tilting, turning, … PERFORMANCE MULTIMODAL MODEL SIGNAL BEHAVIOUR Cleaner signals from the earbuds due to unique placement HEAD GESTURE Detection of basic head gestures with IMU signals Nodding and Shaking Gyroscope Accelerometer Nodding Shaking Nearest Neighbour Statistical Features Gyroscope Combined Features • Nodding • Shaking • Other Statistical Features Accelerometer F1score 0 0.25 0.5 0.75 1 Fusion Accelerometer Gyroscope 0.850.890.93 @raswak ACM WearSys 2018
  16. 16. eSense Earable IMU signal when walking Accelerometer Gyroscope Nearest Neighbour Statistical Features Gyroscope Combined Features • Stationary • Walking • Stepping up • Stepping down • Other Statistical Features Accelerometer AverageF-1score 0.00 0.25 0.50 0.75 1.00 Fusion Accelerometer Gyroscope 0.62 0.950.96 Over 90% accuracy with accelerometer alone More robust to placements compared to watch and phone PERFORMANCE MULTIMODAL MODEL SIGNAL BEHAVIOUR Cleaner signals from the earbuds due to small head movements PHYSICAL ACTIVITY Detection of basic activities with IMU signals: stationary, walking, stepping up and stepping down @raswak ACM WearSys 2018
  17. 17. eSense Earable PERFORMANCE MULTIMODAL MODEL SIGNAL BEHAVIOUR Cleaner signals from the earbuds due to small head movements DIET Detection of basic activities with IMU signals: drinking and chewing Audio spectrogram when chewing Gyroscope data when drinking Random Forest MFCC Statistical Features Accelerometer Gyroscope Microphone • Drinking • Chewing • OtherCombined Features 78% accuracy for fusion classifier even with simple features Outperforms single-sensor classifiers F-1score 0 0.2 0.4 0.6 0.8 Fusion Audio IMU 0.69 0.3 0.78 0.21 0.70.73 Chewing Drinking @raswak ACM MobiSys 2018
  18. 18. eSense Earable PERFORMANCE SIGNAL PROCESSING SIGNAL BEHAVIOUR Amplified sound of heartbeats can be easily captured due to placement HEART RATE Simple filtering and peak detection is enough for reliable detection Average error of 2.4 BPM Capable of detecting heart rate from in-ear microphone Following ECG Pattern Raw Signal Microphone Low-Pass Filter Amplifier Z Peak Detector Z Heart rate Beatsperminute 0 30 60 90 Ours Ground truth 81.684.0 @raswak
  19. 19. eSense Earable PERFORMANCE MULTIMODAL MODEL SIGNAL BEHAVIOUR Cleaner phase response from IMU to detect speech segment CONVERSATION Detection of speech segments using IMU and simple, lightweight classifier 85% accuracy in speaking detection only with inertial sensors Much more robust to ambient noise, e.g., nearby person’s speaking Energy efficient trigger of more expensive microphone SVM Statistical Features Gyroscope Combined Features • Speaking • Non-speaking Statistical Features Accelerometer F1score 0 0.2 0.4 0.6 0.8 1 Audio IMU All 0.880.86 0.65 + 20% @raswak ACM WellComp 2018
  20. 20. eSense Earable PERFORMANCE MULTIMODAL MODEL SIGNAL BEHAVIOUR Cleaner phase response from IMU to detect facial expression FACIAL EXPRESSION Fusion of IMU and Audio signals with SVM followed by HMM Smoothing 70-80% F1 score with statistical features High user variability for ‘smiling’ expression Gyroscope data Camera Stationary Pull Up Movement Pull Down Stationary SVM • State 1 • State 2 • State 3 • … MFCC Statistical Features Accelerometer Gyroscope Microphone Feature Selection HMM • Laugh • Smile • Frown • Other F1score 0 0.2 0.4 0.6 0.8 1 Other Smile Laugh Frown 0.740.68 0.61 0.81 @raswak ACM AH 2019
  21. 21. Situation-Aware Conversational Agent Bringing cognition to conversational agents to radically transform their ability to assist and augment human KEY OBJECTIVE Customer Experience, Conversational Commerce, Digital Health, Entertainment, Education Home Automation and Life Style. KEY APPLICATIONS KEY INNOVATION • AI-assisted software platform to understand emotion and situation at personal-scale. • AI-as-a-Service to enable conversational agents to become situation-aware and dynamically adjusts its conversation style, tone, volume in response to users emotional, social activity and environmental context Emotion Awareness Sociality Awareness Activity Awareness Realtime Adaptation KEY NUMBERS 97.8% RECOGNITION ACCURACY 1.2 RECOGNITION LATENCY 2.48 E2E LATENCY SEC SEC @raswak ACM ACII 2019
  22. 22. 360 Wellbeing Management and Cognitive Augmentation People and Space Analytics Stress and Happiness Analytics Physical Social Network Understand, quantify and radically transform how people interact, feel, collaborate and work together in the real enterprise for personal, group and larger organisation efficiency. Implication Key Objective • Audio and Motion Sensor Processing • Speech and OneTouch Interactions • HD Quality Music • Speech Recognition • Speech Synthesis • Notification Management • Context Processing BLE Localisation • External Service Interaction • Conversational Agent • Selective Rule Engines APP Inference Engine for Realtime Context Awareness End-to-End Architecture Audio Conversational Activity Audio Environment Dynamics Audio Emotion Motion Head Gesture Motion Physical Activity Location Face to Face Interaction AI Model AI Model MFCC Statistical Features BLE RSS Accelerometer Gyroscope BLE Microphone • Heart Rate • Emotion and Stress • Eating and Drinking • Conversation • Ambient Environment • Stationary • Walking • On-Transport • Head Gesture • Placement • Social Interaction • Proxemic Interaction AI Model • Sampling Rate • Duty Cycle CONTEXT PRIMITIVES CONFIGURATION • Sampling Rate • Duty Cycle • Packet Interval + + @raswak
  23. 23. Interaction with People, Places, and Things On-the-Go. Feedback on Physical and Mental Well Being Feedback on Collaboration, and Social Behaviour Personalised Recommendation on Wellbeing @raswak
  24. 24. http://www.esense.io
  25. 25. @raswak Behavioural UX FutureBehavioural UX @raswak
  26. 26. Future is Multi-Device Multi-Modal Personal @raswak
  27. 27. SINGLE DEVICE MULTIPLE DEVICES SINGLEMODALITYMULTIPLEMODALITIES @raswak
  28. 28. Multiple devices offers more, better, and longer learning opportunities at the expense of significant complexity. 1 Design for Multiplicity - Cognitive Orchestration How to select, combine and compose devices to construct a dynamic sensing pipeline contextually for highest QoS?CHALLENGE @raswak
  29. 29. COGNITIVE ORCHESTRATION 2x accuracy gain at the expense of 13 mW energy 4x energy gain - inversely proportional to number of devices Learning the Runtime sensing quality of multiple devices using Siamese Neural Net Predicting the best inference path addressing device and usage variability Eliminate redundant computation. Multi-Device Sensory AI Systems Select and orchestrate the best devices for the task at hand maximising accuracy and mining energy SenSys 2019 Motion based Physical Activity Detection Audio Prosody based Emotion Detection@raswak
  30. 30. Design for Robustness. - Cognitive Translation 2 Environment - Environment Translation Device - Device Translation OS - OS Translation Sensor - Sensor Translation Guarantee a model to withstand its functional behaviour across heterogenous conditions Every single execution environment (sensor, device, OS, user) is different. How to build robust sensory systems for 100 billion AI devices (some of which are not invented yet)? CHALLENGE @raswak
  31. 31. COGNITIVE TRANSLATION 0% 25% 50% 75% 100% iPhone S8 Mic2Mic Loss Recovery 0% 25% 50% 75% 100% Thigh Chest Accel2Accel Loss Recovery Audio Signal - Device Variability Motion Signal - User Variability Accuracy Accuracy Recover up to 90% of the accuracy lost due to device variability using 15 minutes of unlabelled data. Generative Models for Domain Adaptation and Domain Generalisation Brand-new Model Architecture with CycleGAN principles for learning domain translation functions Robust and Future-Proof Sensory AI Systems Sensory models that work irrespective of how and where the sensor data is collected. IPSN 2018, IPSN 2019 @raswak CASE 1 CASE 2
  32. 32. Qualitative insights need to shape the systems’ runtime behaviour3 How to extend shape System’s behaviour at different phases in a personalised way? Turn user interaction into learning parameters CHALLENGE @raswak
  33. 33. COGNITIVE EXTENSION Privacy Preserving and Personalised Extension of Sensory AI Systems M M Running M M Cycling Swimming M M Running M Cycling M Swimming Time On-Device Continual Learning APPROX POSTERIOR M M M Upper Bounded KL Loss Cross Entropy Loss JSD Loss PRIOR APPROX POSTERIOR M Labelled Data x y x’ x’’ MM + Unlabelled Data Data Augmentation s s’ s’’ 0 0.5 1 Period 1 Period 2 Period 3 Period 4 Period 5 Other Walking Sitting Walking Upstairs Walking Downstairs Standing Laying 0 500 1000 1500 Period 2 Period 3 Period 4 Period 5 986 1286 1374 1211 362 461 590 728 Retained Samples New Samples Continual Learning Accuracy for Motion Tasks Continual Learning Data Requirement Accuracy 90% accuracy across multiple learning periods for extension Only 10% data is retained Labelled data reduction by 80% Semi-supervised Bayesian continual learning Small and imperfectly labelled supervised datasets Rich approximate posteriors with uncertainty estimates Extend sensory systems ability in a personalised and user- defined way using on-device continual learning @raswak
  34. 34. Design for Efficiency (and Privacy) - Cognitive Efficiency Inference Performance Privacy Protection Energy Awareness Scale down cloud-scale algorithms to run locally on devices Where will we find the next 10xgain?CHALLENGE 4 @raswak
  35. 35. COGNITIVE EFFICIENCY Online Model Compression Compress deep neural networks with negligible degradation in accuracy Dynamic Model Fusion Simultaneous execution of multiple models through parallelisation of parameter heavy and computation heavy layers Optimal Resource Allocation Reduce energy footprints of neural networks and allocate an optimal set of resources at runtime Inference Performance Privacy Protection Energy Awareness Factorisation reduces memory and computational requirements 1.5x gains in overall execution time With runtime model fusion Privacy Preserving Software Accelerator for Sensory AI Systems IPSN 2016, SenSys 2016, MobiSys 2017, IEEE Pervasive 2017 @raswak
  36. 36. Design needs to shape the understanding ability of the IoT Systems5 COMFORT MEMORABLE CONVERSATION From recognition to understanding — {Design} enabled understanding How to define the learning targets based on UX, and not the literals towards a universal understanding model?CHALLENGE @raswak
  37. 37. Intelligibility 6 Engage users and keep them informed about system’s behaviour How to embed intelligibility in sensory system’s behaviour? Answer the WHY ? CHALLENGE @raswak
  38. 38. Design needs to guide AI-assisted wearables failover strategy7 Design for AI Failure How to guide the intelligibility of Sensory Systems in dealing with failure, and in deciding when to engage human for right UX?CHALLENGE @raswak
  39. 39. Pervasive Systems Research Cambridge

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