Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data [Book Chapter]

NESL Technical Report #: 2020-8-0


Abstract: Complex activity recognition using multiple on-body sensors is challenging due to missing samples, misaligned data times tamps across sensors, and variations in sampling rates. In this paper, we introduce a robust training pipeline that handles sampling rate variability, missing data, and misaligned data time stamps using intelligent data augmentation techniques. Specifically, we use controlled jitter in window length and add artificial misalignments in data timestamps between sensors, along with masking representations of missing data. We evaluate our pipeline on the Cooking Activity Dataset with Macro and Micro Activities, benchmarking the performance of deep convolutional bidirectional long short-term memory (DCBL) classifier. In our evaluations, DCBL achieves test accuracies of 88% and 72%, respectively, for macro- and micro-activity classifications, exceeding performance over state-of-the-art vanilla activity classifiers.

External paper URL


Publication Forum: Human Activity Recognition Challenge. Smart Innovation, Systems and Technologies

Volume: 199

Page (Start): 39

Page (End): 53

Date: 2020-08-26

Publisher: Springer, Singapore

Public Document?: Yes

NESL Document?: Yes

Document category: Book Chapter

Primary Research Area: Mobile Phone based Sensing