Exploring the link between behaviour and health

Abstract

Mobile and wearable sensors are increasingly permeating our lives, and information gathered from them can provide unprecedented insights into diverse aspects of human behaviour. Analysis of human behaviour is of special interest in health care, as there exists dual relationship between behaviour and health. On one hand, our health is influenced by our behaviour, including physical activity levels, amount of social activity, and work–life balance amongst others, while on the other hand, symptoms of various disorders are manifested as behaviour changes. This is especially prominent for mental disorders [11]. Therefore, human behaviour understanding has significant value for health care, from the point of view of both maintaining good health and helping in the diagnosis of the diseases. While the link between various aspects of behaviour and health has been explored in clinical settings, use of technology to automatically measure behaviour is still in its infancy. Considering enormous potential of automatic behaviour understanding in health care, this Theme Issue explores the link between automatic understanding of human behaviour and how it can inform decisions of range of stakeholders in the health ecosystem. Sensing modalities, data processing methods, and behaviour capturing techniques that facilitate this exploration received a particular focus in the contents of this Theme Issue. As such, authors in [8] present an automated behaviour analysis system, consisting of a sensor network set-up in a home setting. Experiments performed showed how sensor readings can be used to automatically detect anomalous behaviour. This anomalous behaviour can be a sign of health changes in the user, and automatic detection could offer the possibility for intervention if required. In the same theme of detecting anomalous behaviour, authors in [5] propose an activity recognition system based on the Markov logic network. The performance and use of the method in dementia care is demonstrated by applying it to a dataset recorded in a smart home environment. Results indicate that the hierarchical approach presented has higher accuracy in recognition and a faster response time than existing approaches. As one of the first step in detecting activities, segmentation of data is typically required. In this regard, the paper in [9] presents an approach that enables segmentation of continuous sensor data in real time. The proposed dynamic segmentation is based on a two-layer strategy—sensor correlation and time correlation manipulation. The methodology was validated utilising two different datasets recorded in smart home settings. Performance measurement of machine learning methods in order to understand human behaviour was considered in [1]. The authors have evaluated the performance of two machine learning methods on five real-world datasets. They show that the commonly used metrics such as F. Gravenhorst A. Muaremi Wearable Computing Laboratory, ETH Zurich, Zurich, Switzerland e-mail: gravenhorst@ife.ee.ethz.ch