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Pose Invariant Activity Classification for Multi-floor Indoor Localization. Saehoon Yi 1 , Piotr Mirowski 2,3 , Tin Kam Ho 2,4 , Vladimir Pavlovic 1 1 Computer Science Department, Rutgers University

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mapping while walking

Pose Invariant Activity Classification

for Multi-floor Indoor Localization

Saehoon Yi1, Piotr Mirowski2,3, Tin Kam Ho2,4, Vladimir Pavlovic1

1Computer Science Department, Rutgers University

2Statistics and Learning Research Department, Bell Labs, Alcatel-Lucent3Now at Microsoft Bing

4Now at IBM Watson

“Mapping while walking”

  • Related work
    • Indoor localization
      • Pedestrian Dead Reckoning
      • GraphSLAM
  • Motivation
    • “Mapping while walking”
  • Methods
    • Pose-invariant sensor features
    • SVM classification of actions
    • HMM temporal smoothing
  • Results
indoor localization
Indoor Localization
  • Indoor localization
    • Various practical use
      • Radio Frequency (WiFi, 4G cell) maps“where am I in this building?”
      • Network deployment optimization:“where should we place that 4G cell in the building?”
    • No GPS
    • Smartphone sensors
      • Accelerometer
      • Gyroscope
      • Magnetometer
      • Barometer
      • WiFi, cell
pedestrian dead reckoning
Pedestrian Dead Reckoning
  • Step detection

on vertical acceleration

  • [Steinhoff et al., Pervasive Computing and Communications 2010]
  • 3-axis orientation

[Madgwick et al., Rehabilitation Robotics 2011]

  • Trajectory updates
  • step length d, offset angle β
  • Subject to drift

due to orientation error

    • Sensor measurement noise
    • Magnetic field perturbations

angle offsetbeta β


error atposition


Yaw of the phonew.r.t. horizontalX axis


directionof walk





longitude or human/horizontal X(“towards East”)


  • Modify trajectory to minimize estimation error
  • [Grisetti et al., Transportation Systems Magazine 2010]
  • Challenges
    • Requires landmark detection
      • How do we know that two different observations are actually taken at the same location?
      • Hand-placed landmarks, e.g.,: QR code or NFC tag
        • Manually installed and maintained





  • Detect and provide natural landmark for GraphSLAM
    • Stairs and elevators are accurately detected
    • They are non moving, distinct structures, which is ideal for landmarks
  • Classify human activities using a smartphone in the pocket
    • Pose invariant features extracted from smartphone sensors
  • Jointly infer activity and floor information
    • Focus on activities that incur floor change
  • “Mapping while walking”
    • Facilitate radio-frequency map building for network engineers
  • SVM: Classify activity at each time point
  • HMM:Smoothing SVM activity classification and jointly infering floor
  • Activities
    • walking
    • stair up
    • stair down
    • stand still
    • elevator up
    • elevator down
pose invariant features for imu sensors
Pose invariant features for IMU sensors
  • Pose invariant features from A
    • [Kobayashi et al., ICASSP, 2011]
  • Invariant to rotation R
statistical features for barometer
Statistical features for barometer
  • Rotation does not affect air pressure
  • Fluctuate over time
  • Depend on weatherand temperature
  • Detects ascending/descending air pressure
svm classification
SVM classification
  • Features are extracted from sliding window of sensor observations
  • Linear SVM for 6 activity classes
    • Fast and storage efficient
      • Linear classification
    • Able to implement real time classification in Android OS
  • Provide activity probability
    • Platt’s scaling algorithm
    • Required to obtain HMM observation probability
hmm smoothing
HMM smoothing
  • 6 activities for each floor
    • State transit for strong evidence
    • Smooth sporadic brief misclassification
  • Augment inference of activity with floor from Viterbi algorithm
  • Observation probability
  • from SVM confidence level(Platt’s scaling)
  • from mixture of Gaussian
hmm smoothing1
HMM smoothing
  • Transition probability
    • Manually design transition probabilities
      • Higher probability of transition to the same state
      • Floor changes only for stair and elevator
experiment set up
Experiment set up
  • Input data
    • Sensor data recorded at 50Hz
  • Feature extraction
    • Sliding window
      • IMU sensor features
        • Length: 64 frames
        • Step size: 35 frames
      • Barometer features
        • Length: 192 frames
  • Train data: 10271 seconds of each class repeatedly performed
  • Test data: 6160 seconds of 12 natural sequence
activity classification result
Activity classification result
  • For SVM,
    • Walking is confused to taking stairs
    • Standing still is confused to taking elevators
      • Leg dynamics are similar
      • Air pressure does not change over short period of time
      • Each sliding window is considered independently
activity classification result1
Activity classification result
  • HMM removes sporadic misclassification between walking and taking stairs
  • Rectification: rectifies stairs to walking when it does not incur floor change
landmarks match
Landmarks match
  • Types of landmark
    • Stair
    • Elevator
  • GraphSLAM requires matching of the same landmark along the trajectory
  • Training phase
    • Obtain information from reference landmarks
      • WiFi access point visibility
  • Testing phase
    • Landmarks matching
      • Get current WiFi AP visibility
      • Calculate distance to reference landmarks
      • Take the closest corresponding landmark
initial pdr trajectory
Initial PDR trajectory
  • Initial trajectory obtained from PDR
  • Rotation angle underestimated for every turn
  • Need to be modified using GraphSLAM
  • Rotation invariant features were able to capture different dynamics of motion activities
  • Our approach improves classification accuracy and jointly infers activity and corresponding floor information
  • GraphSLAM successfully modifies multi-floor trajectory using natural landmarks detected by our framework.
Q & A

Thank you