Attention-Based Multi-Task Learning For Sensor Analytics

Sensors and internet of things (IoTs) are ubiquitous in our modern day-to-day living. Applications range from smart home devices that control cooking ranges to mobile phones, wearable devices that serve as fitness trackers and personalized coaches. There is a critical need for the analysis of heterogeneous multivariate temporal data obtained from individual sensors. In this work we show that multi-task learning (MTL) is naturally suited for sensor data learning, and propose a novel multi-task learning approach with attention mechanism, M-Att, that jointly trains classification/regression models from multiple related tasks where data on each task is generated from one or more sensors. The temporal and non-linear relationships underlying the captured data are modeled using a combination of both convolution neural network (CNN) and long-short term memory (LSTM) models. And the attention mechanism seeks to learn shared feature representations across multiple tasks for improving the overall generalizability of the machine learning model. We evaluate our proposed method in both classification and regression settings on an activity recognition dataset and environment monitoring dataset. Comparing the proposed approach to other competitive single-task learning and multi-task learning approaches we demonstrate the high performance of our proposed model with promising results.