Common Sense Based Joint Training of Human Activity Recognizers
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Common Sense Based Joint Training of Human Activity Recognizers

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Common Sense Based Joint Training of Human Activity Recognizers Common Sense Based Joint Training of Human Activity Recognizers Presentation Transcript

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    • Introduction
    • Sensors
    • Techniques
    • Evaluation Methodology
    • Results
    • Conclusions
    • Object use is a promising ingredient for general, low-overhead indoor activity recognition
    • (e.g., whether a door is being opened or closed)
    • (e.g., microwaveable objects)
    • (e.g., clearing a table and eating dinner)
    • We present a joint probabilistic model of object-use, physical actions and activities that improves activity detection relative to models that reason separately about these quantities, and learns action models with much less labeling overhead than conventional approaches
    • two people performing 12 activities involving 10 actions on 30 objects
    • defined simple commonsense models for the activities using an average of 4 English words per activity
    • Combining action data with object-use in this manner increases precision/recall of activity recognition from 76/85% to 81/90%; the automated action-learning techniques yielded 40/85% precision/recall over 10 possible object/action classes
    • wear two bracelets on the dominant wrist
    • One is a Radio Frequency Identification (RFID) reader, called the iBracelet, for detecting object use,
    • The other is a personal sensing reader, called the Mobile Sensing Platform (MSP), for detecting arm movement and ambient conditions
    • eight sensors: a six-degree-of-freedom accelerometer, microphones sampling 8-bit audio at 16kHz, IR/visible light, high-frequency light, barometric pressure, humidity, temperature and compass
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    • Problem Statement
      • Infers the current action being performed and the object on which it is being performed.
      • Combines with object-use data O (when available) to produce better estimates of current activity A.
      • For efficiency reasons, uses F’ « F features of M.
    • A Joint Model
      • The Dynamic Bayesian Network (DBN)
    • A Layered Model
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    • 1. How good are the learned models?
      • how much better are the models of activity with action information combined?
      • how good are the action models themselves?
      • Why?
    • 2. How does classifier performance vary with our design choices?
      • With hyperparameters?
    • 3. How necessary were the more sophisticated aspects of our design?
      • Do simple techniques do as well?
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    • Feature selection and labeling are known bottlenecks in learning sensor-based models of human activity
    • it is possible to use data from dense object-use sensors and very simple commonsense models of object use, actions and activities to automatically interpret and learn models for other sensors, a technique we call common sense based joint training