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MP16-899 Office Activity Awareness
Ian LiMachine PerceptionSpring 2005
MP16-899 Activity awareness can be good
• Awareness of how one uses time in the office can be useful
• Manage activities, coordinate interaction with others, and assess your own productivity
• But, too much to remember and recording can be tedious
MP16-899 Computers can help
• Delegate recording of activity to computers• Can monitor daily• Can store activity for months and years
• User can focus on analyzing the information at the end of the day or week
MP16-899 What did I do?
• System for office activity detection
• Applied system for “productivity” assessment
MP16-899 What is the result?
• System can reliably detect activity in the office environment (87%-93%)
• System can somewhat match the users’ measurements of their own “productivity” (up to 74%)
MP16-899 The rest of the talk…
• System: activity detection• Application: “productivity” assessment• Future work
MP16-899 Sensors for detecting activity
Walking
Sitting/standing
Sitting & talking
Not in space
Activities detected
Amount of motion
Extracted Features
Sensors
Using mouse
Pressing keys
Not using computer
Using mouse or keyboard?
Talking
Not talking
Sound level
MP16-899 Data collection tool
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
MP16-899 Ground truth for activity detection
• Took snapshot every half minute
not in space walking
sitting & talking
sitting & talking?
sitting
MP16-899 Activity can be detected accurately
• Using microphone and camera features Recall
Accuracy Classifier Outside Sitting Sit&Talk Walking
Prof 1 Day 1 88.0412 Bagging (REPTrees) 0.944 0.877 0.948 0
Prof 1 Day 2 92.8571 Bayes Net 0.657 0.97 0.986 0
Prof 2 Day 1 90.0322 Bagging (REPTrees) 0.829 0.931 0.923 0.381
Prof 2 Day 2 87.6202 Bagging (REPTrees) 0.886 0.898 0.227
Student 1 90.849 Bayes Net 0.723 0.972 0.867 0
Student 2 93.0769 Bagging (REPTrees) 0.88 0.976 0 0
MP16-899 Applying to productivity awareness
• Can we measure productivity by looking at activities?
• How aware are people of their own productivity?
MP16-899 Recording productivity
• Measurement of productivity• What percentage of the past 15 minutes did you
spend actively engaged in a work-related task?
• “Experience sampling” technique• Every 15 minutes the timer plays a bird sound
Bird sound
MP16-899
Using knowledge of activity is okay for detecting productivity
Recall
Classifier Accuracy Productive Not productive
All Logistic Regression 62.1053 0.792 0.447
Profs only Logistic Regression 64.6154 0.788 0.5
Students only Logistic Regression 56.6667 0.533 0.6
w/o student 1 Logistic Regression 65.7895 0.795 0.514
w/o student 2 Logistic Regression 63.0952 0.857 0.405
w/o prof 1 Logistic Regression 68.1818 0 0.978
w/o prof 2 Logistic Regression 76.2712 0.93 0.313
MP16-899
Using raw features is slightly better for detecting productivity!
Recall
Classifier Accuracy
Accuracy using activity labels Productive Not productive
All Logistic Regression 65.7658 62.1053 0.642 0.672
Profs only Logistic Regression 73.9726 64.6154 0.697 0.775
Students only Decision Tree 63.1579 56.6667 0.65 0.611
w/o student 1 Naïve Bayes Tree 65.2174 65.7895 0.955 0.375
w/o student 2 Logistic Regression 71.7391 63.0952 0.714 0.72
w/o prof 1 Logistic Regression 68.2927 68.1818 0.32 0.842
w/o prof 2 Decision Tree 73.1343 76.2712 0.833 0.474
MP16-899 Future work
• Longer deployment of the system• How many features are sufficient to predict
productivity?• Use temporal model (e.g., HMMs)• Activity-oriented vs. task-oriented
measurement of productivity• Other applications of activity awareness
• Setting goals and monitoring completion of goals
MP16-899 Office Activity Awareness
Ian LiMachine PerceptionSpring 2005
http://www.cs.cmu.edu/~ianl/16899/ will be up by March 13th for more details or contact me at [email protected]
MP16-899 Acknowledgements
• Software development help from Bilge Mutlu and James Fogarty
• System deployment participants: Anind Dey, Jason Hong, Bilge Mutlu, and Pedram Keyani