Sensor-based Semantic-level Human Activity Recognition...
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RESEARCH POSTER PRESENTATION DESIGN © 2015 www.PosterPresentations.com Figure 1:PCA reduction of multi-dimensional space to 3D. Individual examples are color coded according to high-level classification Human activity recognition has always been an active research topic driven by the potential ranging from health care, assistive technologies to manufacturing and smart surveillance system. Most of the effort has been put on recognizing simple action. However, semantic-level activity recognition, which for example could enhance the flexibility and the capability of humanoid robot in a complex context, is not well developed. Our purpose is to develop a hierarchical approach to recognize semantic-level activities based on sensor-based raw data and low-level actions. Motivation Data Methodology Results and Analysis Sensor-based Semantic-level Human Activity Recognition using Temporal Classification Rui Xu , Chuanwei Ruan , Weixuan Gao Sensor-based raw data Low-level activities High-level activities (1) Without Considering Temporal Pattern: ● Baseline is from raw data to high-level activities ● Low-level activities are used as middle layer ● KNN, Boosting, and Randomforest are applied (1) (2) (2) (2) Recurrent Neural Network: KNN Boosting Tree Random Forest Raw to Locomotion 86.59% 92.17% 94.1% Raw to Low-level Left Arm 82.66% 84.78% 82.5% Raw to Low-level Right Arm 72.15% 71.73% 74.47% Raw to High 76.40% 79.72% 86.01% Low to High 46.74% 44.7% 45% The dataset comprises the reading of motion sensors recorded while users executed typical daily activities. More than 27,000 atomic activities were recorded. There is a total of 242 attributes, including 145 attributes generated from body-worn sensors. Accuracy(with null class, subject 1): Number of Classification Example Locomotion 5 Walk, Sample, Sit Low level 73 unlock, stir, close Middle Level 18 Open Door1, Drink from cup High Level 6 Relaxing, Coffee Time ● Different combinations of low-level actions are considered as unique “words” ● Every high-level activity corresponds to a sequence of such “words” LSTM Boosting Tree Random Forest Low to High 70.97% 44.13% 45.95% Accuracy(without null class, all subjects): Future Works ● Improve the current models on low-level activities and loco- motions by more sophisticated features engineering and data preprocessing. ● Gather more data and try more appropriate neural network frames. The current poor performance of LSTM are partially due to lack of data and computational resources. ● Build a LSTM models which directly projects the sequences of raw sensor data to the high-level labels. ● Random Forest generally outperformed other methods ● Too many equidistant points on Low to High using KNN so we add small random noise ● Low level activities info is not put into good use because we failed to extract temporal sequential pattern ● Apply Long Short Term Memory(LSTM) with softmax layer to predict high-level activities. (3) Graphical Modeling+LSTM+Multitask Learning ● Building temporal sequential relatedness by using topological sort especially from low → high ● Incorporate raw → high and raw → low → high in LSTM Raw Data Kernel Smoothing Normalized Data ● Using more data fails to increase the accuracy of Random Forest and KNN due to the high variance between different subjects. ● LSTM significantly outperforms other methods by considering temporal relations.
Transcript of Sensor-based Semantic-level Human Activity Recognition...
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Figure 1:PCA reduction of multi-dimensional space to 3D. Individual examples are color coded according to high-level classification
Human activity recognition has always been an active research topic driven by the potential ranging from health care, assistive technologies to manufacturing and smart surveillance system. Most of the effort has been put on recognizing simple action. However, semantic-level activity recognition, which for example could enhance the flexibility and the capability of humanoid robot in a complex context, is not well developed. Our purpose is to develop a hierarchical approach to recognize semantic-level activities based on sensor-based raw data and low-level actions.
Motivation
Data
Methodology Results and Analysis
Sensor-based Semantic-level Human Activity Recognition using Temporal ClassificationRui Xu , Chuanwei Ruan , Weixuan Gao
Sensor-based raw data
Low-level activities
High-level activities
(1) Without Considering Temporal Pattern:
● Baseline is from raw data to high-level activities● Low-level activities are used as middle layer ● KNN, Boosting, and Randomforest are applied
(1)
(2) (2)
(2) Recurrent Neural Network:
KNN Boosting Tree Random ForestRaw to Locomotion 86.59% 92.17% 94.1%
Raw to Low-level Left Arm 82.66% 84.78% 82.5%
Raw to Low-level Right Arm 72.15% 71.73% 74.47%
Raw to High 76.40% 79.72% 86.01%Low to High 46.74% 44.7% 45%
The dataset comprises the reading of motion sensors recorded while users executed typical daily activities. More than 27,000 atomic activities were recorded. There is a total of 242 attributes, including 145 attributes generated from body-worn sensors.
Accuracy(with null class, subject 1):
Number of Classification
Example
Locomotion 5 Walk, Sample, Sit
Low level 73 unlock, stir, close
Middle Level 18Open Door1,
Drink from cup
High Level 6Relaxing, Coffee
Time
● Different combinations of low-level actions are considered as unique “words”
● Every high-level activity corresponds to a sequence of such “words”
LSTM Boosting Tree Random ForestLow to High 70.97% 44.13% 45.95%
Accuracy(without null class, all subjects):
Future Works● Improve the current models on low-level activities and loco-
motions by more sophisticated features engineering and data preprocessing.
● Gather more data and try more appropriate neural network frames. The current poor performance of LSTM are partially due to lack of data and computational resources.
● Build a LSTM models which directly projects the sequences of raw sensor data to the high-level labels.
● Random Forest generally outperformed other methods● Too many equidistant points on Low to High using KNN so
we add small random noise ● Low level activities info is not put into good use because
we failed to extract temporal sequential pattern
● Apply Long Short Term Memory(LSTM) with softmax layer to predict high-level activities.
(3) Graphical Modeling+LSTM+Multitask Learning
● Building temporal sequential relatedness by using topological sort especially from low → high
● Incorporate raw → high and raw → low → high in LSTM
Raw Data Kernel Smoothing Normalized Data
● Using more data fails to increase the accuracy of Random Forest and KNN due to the high variance between different subjects.
● LSTM significantly outperforms other methods by considering temporal relations.
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