1 research outputs found
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Recently, deep learning (DL) methods have been introduced very successfully
into human activity recognition (HAR) scenarios in ubiquitous and wearable
computing. Especially the prospect of overcoming the need for manual feature
design combined with superior classification capabilities render deep neural
networks very attractive for real-life HAR application. Even though DL-based
approaches now outperform the state-of-the-art in a number of recognitions
tasks of the field, yet substantial challenges remain. Most prominently, issues
with real-life datasets, typically including imbalanced datasets and
problematic data quality, still limit the effectiveness of activity recognition
using wearables. In this paper we tackle such challenges through Ensembles of
deep Long Short Term Memory (LSTM) networks. We have developed modified
training procedures for LSTM networks and combine sets of diverse LSTM learners
into classifier collectives. We demonstrate, both formally and empirically,
that Ensembles of deep LSTM learners outperform the individual LSTM networks.
Through an extensive experimental evaluation on three standard benchmarks
(Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition
capabilities of our approach and its potential for real-life applications of
human activity recognition.Comment: accepted for publication in ACM IMWUT (Ubicomp) 201