Amir Muaremi

Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep

Work should be a source of health, pride, and happiness, in the sense of enhancing motivation and strengthening personal development. Healthy and motivated employees perform better and remain loyal to the company for a longer time. But, when the person constantly experiences high workload over a longer period of time and is not able to recover, then work may lead to prolonged negative effects and might cause serious illnesses like chronic stress disease. In this work, we present a solution for assessing the stress experience of people, using features derived from smartphones and wearable chest belts. In particular, we use information from audio, physical activity, and communication data collected during workday and heart rate variability data collected at night during sleep to build multinomial logistic regression models. We evaluate our system in a real work environment and in daily-routine scenarios of 35 employees over a period of 4 months and apply the leave-one-day-out cross-validation method for each user individually to estimate the prediction accuracy. Using only smartphone features, we get an accuracy of 55 %, and using only heart rate variability features, we get an accuracy of 59 %. The combination of all features leads to a rate of 61 % for a three-stress level (low, moderate, and high perceived stress) classification problem.

Assessing Bipolar Episodes Using Speech Cues Derived from Phone Calls

In this work we show how phone call conversations can be used to objectively predict manic and depressive episodes of people suffering from bipolar disorder. In particular, we use phone call statistics, speaking parameters derived from phone conversations and emotional acoustic features to build and test user-specific classification models. Using the random forest classification method, we were able to predict the bipolar states with an average F1 score of 82 %. The most important variables for prediction were speaking length and phone call length, the HNR value, the number of short turns and the variance of pitch F\(_0\).

Monitor and understand pilgrims: data collection using smartphones and wearable devices

Each year, millions of people visit the sacred sites in Makkah and Madinah. Even though the Hajj pilgrimage is one of the biggest annual events in the world, with many of the pilgrims reporting it as a life-changing experience, quite a little is done to objectively monitor the pilgrims and to understand from the crowd and from the individual point of view what makes this event so special. We present a data collection phase of 8 days of pilgrimage in April 2013 with 41 pilgrims carrying Android smartphones and 10 pilgrims wearing two physiological sensors, namely chest belts and wrist-worn devices. We describe the data recording itself, and emphasize the problems raised and the challenges faced during the study. We provide the best practices for performing solid and efficient user studies in such a difficult environment, and give first insights towards measuring important aspects of the Hajj pilgrimage such as recognition of activities and stages, analysis of group behavior, detection of stressful situations and health monitoring of pilgrims in general.

Discriminating Individually Considerate and Authoritarian Leaders by Speech Activity Cues

Effective leadership can increase team performance, however up to now the influence of specific micro-level behavioral patterns on team performance is unclear. At the same time, current behavior observation methods in social psychology mostly rely on manual video annotations that impede research. In our work, we follow a sensor-based approach to automatically extract speech activity cues to discriminate individualized considerate from authoritarian leadership. On a subset of 35 selected group discussions lead by leaders of different styles, we predict leadership style with75.5\% accuracy using logistic regression. We find that leadership style predictability is dependent on the relative discussion time and is highest for the middle parts of the discussions. Analysis of regression coefficients suggests that individually considerate leaders start speaking more often while others speak, use short utterances more often, change their speech loudness more and speak less than authoritarian leaders.

Strap and row: Rowing technique analysis based on inertial measurement units implemented in mobile phones

The length of a rowing stroke is an important performance metric for athletes and coaches. Accurate measurements are possible with optical or mechanical systems, which require significant setup effort. This work presents a new approach using a smart phone as a sensor device that is strapped to the oar. Two algorithms are introduced to calculate stroke lengths from the raw phone sensor data. The performance of each algorithm is evaluated by comparing the results to a mechanical reference system. Data was recorded during a single-user study performed on a rowing simulator. The best algorithm showed an average stroke length error of 7.64° ± 2.95°.

Mobile Health Systems for Bipolar Disorder: The Relevance of Non-Functional Requirements in MONARCA Project

This paper presents a series of challenges for developing mobile health solutions for mental health as a result of MONARCA project three-year activities. The lessons learnt on the design, development and evaluation of a mobile health system for supporting the treatment of bipolar disorder. The findings presented here are the result of over 3 years of activity within the MONARCA EU project. The challenges listed and detailed in this paper may be used in future research as a starting point for identifying important non-functional requirements involved in mobile health provisioning that are fundamental for the successful implementation of mobile health services in real life contexts.

Merging Inhomogeneous Proximity Sensor Systems for Social Network Analysis

Proximity information is a valuable source for social network analysis. Smartphone based sensors, like GPS, Bluetooth and ANT+, can be used to obtain proximity information between individuals within a group. However, in real-life scenarios, different people use different devices, featuring different sensor modalities. To draw the most complete picture of the spatial proximities between individuals, it is advantageous to merge data from an inhomogeneous system into one common representation. In this work we describe strategies how to merge data from Bluetooth sensors with data from ANT+ sensors. Interconnection between both systems is achieved using pre-knowledge about social rules and additional infrastructure. Proposed methods are applied to a data collection from 41 participants during an 8 day pilgrimage. Data from peer-to-peer sensors as well as GPS sensors is collected. The merging steps are evaluated by calculating state-of-the art features from social network analysis. Results indicate that the merging steps improve the completeness of the obtained network information while not altering the morphology of the network.

Exploring the link between behaviour and health

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