Wearable accelerometer based extendable activity recognition system

Recognizing the human activities of daily living (ADL) is an important research issue in the pervasive environment. Activity recognition is treated as a classification problem and the multi-class classifier is often used. Though the multi-class classifier can obtain high classification accuracy, it can not detect the noise activities and unknown activities, and the system has no extendable recognition capability. In this paper, we proposed a recognition system which can recognize known activities and detect unknown activities simultaneously. For each known activity, one one-class classification model is built up and the combined one-class classification models are used to judge whether a test sample belongs to known activities. For the known samples, the multi-class classifier is used to recognize their types. For the continuous unknown samples, based on segmentation algorithm, training samples of new activities are extracted and added into the recognition system to extend the system's recognition capability.

Authors

• Jie Yang 0002
• Shuangquan Wang
• Ningjiang Chen
• Xin Chen
• Pengfei Shi

Keywords

• Accelerometers
• Testing
• Humans
• Flowcharts
• Robotics and automation
• USA Councils
• Working environment noise
• Wearable computers
• Wearable sensors
• Assembly
• Recognition System
• Action Recognition
• Classification Model
• Test Samples
• Human Activities
• Classification Accuracy
• Multi-label
• Segmentation Algorithm
• Capability Of System
• Unknown Samples
• Recognition Capability
• One-class Classification
• Walking
• Training Dataset
• Support Vector Machine
• Test Dataset
• Detection Accuracy
• Dimensionality Reduction
• Training Phase
• Detection Performance
• Class Information
• Multi-class Model
• Continuous Segments
• Detection Results
• Dashed Arrows
• Recognition Results
• Power Spectral Density
• K-nearest Neighbor
• Target Class
• Cross-validation Test