Compensating for Object Variability in DNN–HMM Object-Centered Human Activity Recognition
| Autor: | Peter Jancovic, Yikai Peng, Martin J. Russell |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Word error rate 020206 networking & telecommunications Pattern recognition 02 engineering and technology Object (computer science) Activity recognition 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business Hidden Markov model |
| Zdroj: | EUSIPCO |
| DOI: | 10.23919/eusipco.2019.8903124 |
| Popis: | This paper describes a deep neural network – hidden Markov model (DNN-HMM) human activity recognition system based on instrumented objects and studies compensation strategies to deal with object variability. The sensors, comprising an accelerometer, gyroscope, magnetometer and force-sensitive resistors (FSRs), are packaged in a coaster attached to the base of an object, here a mug. Results are presented for recognition of actions involved in manipulating a mug. Evaluations are performed using over 24 hours of data recordings containing sequences of actions, labelled without time-stamp information. We demonstrate the importance of data alignments. While the DNN-HMM system achieved error rate below 0.1% for matched train-test conditions, this increased up to 26.5% for highly mismatched conditions. The error rate averaged over all conditions was 1.4% when using multi-condition training and decreased to 0.8% by employing feature augmentation. The use of FSR feature compensation, specific to weight variability, resulted in 0.24% error rate. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|