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Data fusion and mining in SPHERE
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SW Data Meetup talk - Tom Diethe - 28 March 2017 Dr Tom Diethe, Research Fellow for the £15 million SPHERE Interdisciplinary Research Collaboration (IRC) at the University of Bristol, will introduce the SPHERE project, which is designing a platform for eHealth in a smart-home context. This platform is currently being deployed into homes throughout Bristol. You may have seen the SPHERE House featured on the BBC's 'Joy of Data' documentary or in the wonderful Aardman animated overview of the project. This talk will focus on the Data Science and Machine Learning challenges and opportunities of SPHERE. Tom will discuss the implications for such an eHealth system in terms of the quantification and management of uncertainty for automated decision making in health care, gathering the necessary data to train Machine Learning models, and the importance of calibration in such systems, particularly in light of the differing operational contexts that will be encountered. Due to well-known demographic challenges, traditional regimes of health-care are in need of re-examination. Many countries are experiencing the effects of an ageing population, which coupled with a rise in chronic health conditions is expediting a shift towards the management of a wide variety of health related issues in the home. In this context, advances in Ambient Assisted Living are providing resources to improve the experience of patients, as well as informing necessary interventions from relatives, carers and health-care professionals. SPHERE has developed a number of different sensors that will combine to build a picture of how we live in our homes. This information can then be used to spot issues that might indicate a medical or well-being problem. The technology could help in the following ways: • Characterise the sedentary behaviour that is linked to so many conditions. • Detect correlations between factors such as diet and sleep. • Measure changes in movement, posture and patterns of movement over months. • Analyse eating behaviour - including whether people are taking prescribed medication. • Detect periods of depression or anxiety and intervene using a computer based therapy. • Predict falls and detect strokes so that help may be summoned. Further details of SPHERE can be found at www.irc-sphere.ac.uk Dr. Diethe is a Research Fellow on the SPHERE project at the University of Bristol, where he specialises in probabilistic methods for machine learning and data fusion, including time-series modelling of human behaviour, unsupervised, active, and transfer learning approaches. He has a Ph.D. in Machine Learning applied to multivariate signal processing from UCL, and was employed by Microsoft Research Cambridge where he co-authored a book titled “Model-Based Machine Learning”, an early access online version of which is available at http://www.mbmlbook.com. Contact him at tom.diethe@bristol.ac.uk
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Data fusion and mining in SPHERE
- Data fusion and mining in SPHERE Challenges & Opportunities for Machine Learning Tom Diethe Intelligent Systems Laboratory University of Bristol
- irc-sphere.ac.uk 21st Century Healthcare Challenges UK: 1.4 million aged > 85, by 2035 -> 3.6 million Japan: will have the oldest population in human history by 2050 (52 yrs) China: a retired population larger than Europe Ageing populations living with long term health conditions: obesity, diabetes, depression, heart disease, dementia … Technological solution to fill the gap between expectations and reality of healthcare
- Environmental Temperature, light level, humidity, air quality Water & electricity consumption Video emotion, gate, activity, interaction Wearables activity, sleep, etc. Contextual information medical history, demographics Feedback medical practitioner, users
- Use Cases • Clinician with information need • Hip injury example – how is their gait, are they walking better? • Causal relations – are there any common patterns of behaviour that lead to health issues • Information back to the user – better health-enhancing choices • Early warning system • Disease progress/treatment effectiveness • Many more …
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- • 100 home deployment underway
- What do we want to learn? 7
- Prediction and Modelling Who is in the house What are they doing When are activities happening Where are these happening Why does this matter? 8
- Is this Big Data? • Sensors • Heterogeneous • Noisy/intermittent • Different spatial/temporal resolutions • Velocity ✔ Variety ✔ volume ? 9
- What’s Important? • Quantification of uncertainty • Transparent models • Online Learning: models must adapt to changing habits • How to incorporate medical history • Daily/Weekly/Seasonal patterns • Personalisation 10
- Model-Based Machine Learning • Model uncertainty using probabilities • frequency • belief • Model contains variables and factors 1. Build a model 2. Incorporate observations 3. Perform inference over “latent” variables 11
- What is a Model? 12 A simulator programme bool A = random.NextDouble() > 0; bool B = random.NextDouble() > 0; bool C = A & B; C A B & • A set of assumptions 1. A coin has an equal chance of landing on heads or tails 2. Coin tosses are independent
- Rules of Probability 13 SUM RULE PRODUCT RULE
- Bayes’ Rule 14 POSTERIOR PRIOR LIKELIHOOD NORMALISER
- • Designed for the data-scientist • Case study based www.mbmlbook.com
- Experiments • SPHERE House: • Scripted Experiments • Medium Term Stays (1-7 days) • Long Stays (1-4 weeks) • Full Deployments 16
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- SPHERE House Script <<<<Downstairs>>>> Living Room Enter the room and close the door behind you Stand facing the mirror and jump twice. Turn light on Go to window and open and close the curtains Take off shoes Artificial activities … repeat 5 times with 3 seconds between each stand to bend bend to stand stand to kneel kneel to stand stand to sit sit to lie (back) lie (back) to lie (side) (on sofa) lie (side) to lie (back) lie (back) to sit sit to stand cough Turn light off 18
- SPHERE Challenge • Task: predict posture and ambulation labels given the sensor data • Accelerometer, RGB-D and environmental data 19
- SPHERE Challenge • Data was collected from a script in the SPHERE house • 10 participant, ~20-30 minutes per script • Even split between training and testing data • Test data split into short 10-30s sequences https://www.drivendata.org/competitions/42/senior-data-science- safe-aging-with-sphere bit.ly/sphere-challenge 20
- Targets • Each sequence was annotated at least twice • Not all annotators will agree all of the time • Start/end time of annotations may not be aligned • Actual label assigned to a time interval may not agree • Task: predict mean annotation on a per-second basis — the targets are probabilistic • Also provided localisation annotations (in training only) 21
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- Challenge Participation • ~ 80 teams • > 100 participants • ~ 400 total registrants • > 770 individual submissions 25
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- Challenges 1 & 2 Shifting Sands Humans are costly 27
- Goal: Activity Recognition in Smart Homes Deployment context differs from learning context (home/resident) TRANSFER LEARNING Labels costly and time-consuming to acquire ACTIVE LEARNING 28
- 29 ONLINE ACTIVE + TRANSFER + TRANSFER
- Method • Extension of the “Bayes Point Machine” • Additional layer of hierarchy: • model “shared” and “individual” weights • can smoothly evolve from generic to personalised predictions • Implemented using Infer.NET • http://research.microsoft.com/en- us/um/cambridge/projects/infernet/ 30
- irc-sphere.ac.uk Accelerometer Data • Source: 30 subjects, Smartphone, 50Hz, Video annotations • Target: 14 subjects, MotionNode, 100Hz, Observer annotations • Classes: Walking upstairs vs. Walking downstairs • Features: 48 features based on the ‘body’ acceleration signal 31 https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+ Using+Smartphones http://sipi.usc.edu/HAD/
- 32 Online Learning Active Transfer Learning VOI method fails to out-perform US
- irc-sphere.ac.uk • Bayesian framework very appealing • Transfer boosts initial accuracy to 70% • Active Learning -> ~5 instances to personalise Diethe, T., Twomey, N. and Flach, P., 2016. Active transfer learning for activity recognition. ESANN Summary
- Challenge 3: Where did I put my sensor? 34
- Unsupervised learning of sensor topologies • signal processing and information-theoretic techniques • learn an adjacency matrix • enables us to determine combinations of sensors useful for classification • Experiments using CASAS data: http://ailab.wsu.edu/casas/datasets/ 35
- irc-sphere.ac.uk Modelling of CASAS datasets • Experiments based on dataset 11 (Kyoto Daily Life 2010) • Existing methods: • Naïve Bayes, HMM, CRF (segmented data) Krishnan & Cook 2012 • SVM, Decision trees (streaming data) Cook 2012 • SOTA: ~80-90% accuracy in controlled environments, some transfer learning • Two approaches: • Undirected models: Further work using Linear Chain CRFs • Directed models: Online Bayesian classifiers
- Dataset: CASAS twor2009 37
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- Results • 5-10% boost in classification performance • Can help • when transferring to new sensor configuration • disambiguating multiple residents Twomey, N., Diethe, T., Craddock, I. and Flach, P., 2016. Unsupervised learning of sensor topologies for improving activity recognition in smart environments. Neurocomputing. 39
- Challenge 4 What to compute, and when? 40
- HyperStream • Software for streaming data • High-level interfaces • Complex interlinked workflows • Online and offline execution modes https://github.com/IRC-SPHERE/HyperStream • Diethe, T., Twomey, N., Kull, M., Sokol, K., Song, H., Tonkin, E., & Flach, P.. (2017). IRC-SPHERE/HyperStream: First public pre-release version. Zenodo. http://doi.org/10.5281/zenodo.242227 • General purpose tool • Domain-independent • “Compute-on-request”
- Challenge 5 Tick-tock-tick-tock 42
- Circular Statistics 43
- Further Challenges Opportunities! 44
- Opportunities • True house-to-house transfer learning • How well does this all work with multiple residents • What happens when houses or people change/move? • Complex activities • Sleep • Real medical applications 45
- Summing up … 46
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- Resources • SPHERE Code: • https://github.com/IRC-SPHERE • SPHERE Challenge Dataset • http://irc-sphere.ac.uk/sphere-challenge/home • http://bit.ly/sphere-challenge • Infer.NET • http://research.microsoft.com/en-us/um/cambridge/projects/infernet/ • MBML Book • http://mbmlbook.com/ 48
Editor's Notes
- Build a model, which if we’re taking the Bayesian approach is a joint distribution over the relevant variables. This can be represented as a graph. Incorporate observations. Run inference, which again in the Bayesian approach means computing the distributions over the desired variables. In real-time applications we iterate over the 2nd & 3rd steps, and extend the model as required.
- ----- Meeting Notes (09/09/2014 14:37) ----- - Coming to the end of year 1, working on setting things up. Names of the team members. - We're hiring. Visits. Collaboration. Who wants this? Nobody wants it for themselves, but everyone wants it for someone else Cut out 1 slide Cliniciian with information need Hip injury example – how it their gait, are they walking better? Causal relations – what causes illness Information back to the user – better health-enhancing choices Early warning system Disease progress/treatment effectiveness Embed video New “House” graphic to replace existing & WP slide Differing temporal/spatial resolutions