Franz Gravenhorst

Towards long term monitoring of electrodermal activity in daily life

Manic depression, also known as bipolar disorder, is a common and severe form of mental disorder. The European research project MONARCA aims at developing and validating mobile technologies for multi-parametric, long term monitoring of physiological and behavioral information relevant to bipolar disorder. One aspect of MONARCA is to investigate the long term monitoring of Electrodermal activity (EDA) to support the diagnosis and treatment of bipolar disorder patients. EDA is known as an indicator of the emotional state and the stress level of a person. To realize a long-term monitoring of the EDA, the integration of the sensor system in the shoe or sock is a promising approach. This paper presents a first step towards such a sensor system. In a feasibility study including 8 subjects, we investigate the correlation between EDA measurements at the fingers, which is the most established sensing site, with measurements of the EDA at the feet. The results indicate that 88% of the evoked skin conductance responses (SCRs) occur at both sensing sites. When using an action movie as psychophysiologically activating stimulus, we have found weaker reactivity in the foot than in the hand EDA. The results also suggest that the influence of moderate physical activity on EDA measurements is low and has a similar effect for both recording sites. This suggests that the foot recording location is suitable for recordings in daily life even in the presence of moderate movement.

An IMU-based sensor network to continuously monitor rowing technique on the water

In the sport of rowing athletes and coaches are concerned with optimizing a rowers technique in order to improve rowing performance. In this paper, we present the design and real-world evaluation of a sensor network approach to support improving the rowers performance. In cooperation with professional rowing teams, we found that a network of inertial measurement units (IMUs) is well suited to continuously and unobtrusively monitor important indicators relating to rowing technique. In a feasibility study with 5 participants we first investigated the optimal sensor setup, and in the final setup we attached 3 IMUs to the oars and the boat. From 18 participants (including both ambitious amateurs and world-class rowers) we recorded both training and racing sessions which each consisted of 1000m rowing. We present 4 rowing technique indicators for all 18 participants. Using the example of two world-class rowers we demonstrate in detail how sensor networks support the iterative process of optimizing the individual rowing technique.

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\(_0\).

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°.

Validation of a Rowing Oar Angle Measurement System Based on an Inertial Measurement Unit

Measuring the horizontal rowing oar angle in an unobtrusive way is an unsolved problem for the rowing community and an interesting field for ubiquitous computing. We present the design and implementation of a new rowing oar angle measurement system that is based on an inertial measurement unit mounted inside the rowing oar and a user interface running on a waterproof smartphone. As well as proving the feasibility, we evaluate the accuracy of our system by comparing its performance with a more obtrusive system which is currently state-of-the-art. The mean deviation of the stroke length measurements between both systems is 1.81%.

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.

Ear-worn reference data collection and annotation for multimodal context-aware hearing instruments

In this work we present a newly developed ear-worn sensing and annotation device to unobtrusively capture head movements in real life situations. It has been designed in the context of developing multimodal hearing instruments (HIs), but is not limited to this application domain. The ear-worn device captures triaxial acceleration, rate of turn and magnetic field and features a one-button-approach for real-time data annotation through the user. The system runtime is over 5 hours at a sampling rate of 128 Hz. In a user study with 21 participants the device was perceived as comfortable and showed a robust hold at the ear. On the example of head acceleration data we perform unsupervised clustering to demonstrate the benefit of head movements for multimodal HIs. We believe the novel technology will help to push the boundaries of HI technology.

Design of a multimodal hearing system

Hearing instruments (HIs) have become context-aware devices that analyze the acoustic environment in order to automatically adapt sound processing to the user’s current hearing wish. However, in the same acoustic environment an HI user can have different hearing wishes requiring different behaviors from the hearing instrument. In these cases, the audio signal alone contains too little contextual information to determine the user’s hearing wish. Additional modalities to sound can provide the missing information to improve the adaption. In this work, we review additional modalities to sound in HIs and present a prototype of a newly developed wireless multimodal hearing system. The platform takes into account additional sensor modalities such as the user’s body movement and location. We characterize the system regarding runtime, latency and reliability of the wireless connection, and point out possibilities arising from the novel approach.

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