Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration ... more Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration but are still restricted by the availability of short range, accurate positioning methods necessary, e.g., when docking remote assets. Typical techniques used for high-accuracy positioning in indoor use case scenarios, such as systems using ultra-wide band radio signals (UWB), cannot be applied for underwater positioning because of the quick absorption of the positioning medium caused by the water. Acoustic and optic solutions for underwater positioning also face known problems, such as the multi-path effects, high propagation delay (acoustics), and environmental dependency. This paper presents an oscillating magnetic field-based indoor and underwater positioning system. Unlike those radio wave-based positioning modalities, the magnetic approach generates a bubble-formed magnetic field that will not be deformed by the environmental variation because of the very similar permeability of wa...
Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 2019
Crowdsensing applications are a popular and common research tool, because they allow volunteering... more Crowdsensing applications are a popular and common research tool, because they allow volunteering participants to provide valuable data via their mobile phones with minimal effort. In most scenarios, it is an important goal to gather data in a reliable and continuous way, while the app runs in the background to avoid disturbing the user. However, in recent versions, Android as well as iOS severely restrict the functionality of an app when it does not have the authorization of a foreground process. In this work, we present a structured overview of the technical state of background service restrictions under iOS (12) and Android (9). We demonstrate a practical approach for working with these restrictions by utilizing the respective operating system's location provider solution. CCS CONCEPTS • Computer systems organization → Embedded systems; • Applied computing → Health care information systems; • Human-centered computing → User studies.
Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, 2018
Education is the Achilles heel of successful resuscitation in cardiac arrest. Therefore, we aim t... more Education is the Achilles heel of successful resuscitation in cardiac arrest. Therefore, we aim to contribute to the educational efficiency by providing a novel augmentedreality (AR) guided interactive cardiopulmonary resuscitation (CPR) "trainer". For this trainer, a mixed reality smart glass, Microsoft HoloLens, and a CPR manikin covered with pressure sensors were used. To introduce the CPR procedure to a learner, an application with an intractable virtual teacher model was designed. The teaching scenario consists of the two main parts, theory and practice. In the theoretical part, the virtual teacher provides all information about the CPR procedure. Afterward, the user will be asked to perform the CPR cycles in three different stages. In the first two stages, it is aimed to gain the muscle memory with audio and optical feedback system. In the end, the performance of the participant is evaluated by the virtual teacher.
This paper explores the use of wearable eye-tracking to detect physical activities and location i... more This paper explores the use of wearable eye-tracking to detect physical activities and location information during assembly and construction tasks involving small groups of up to four people. Large physical activities, like carrying heavy items and walking, are analysed alongside more precise, hand-tool activities, like using a drill, or a screwdriver. In a first analysis, gazeinvariant features from the eye-tracker are classified (using Naive Bayes) alongside features obtained from wrist-worn accelerometers and microphones. An evaluation is presented using data from an 8-person dataset containing over 600 physical activity events, performed under real-world (noisy) conditions. Despite the challenges of working with complex, and sometimes unreliable, data we show that event-based precision and recall of 0.66 and 0.81 respectively can be achieved by combining all three sensing modalities (using experiment independent training, and temporal smoothing). In a further analysis, we apply ...
2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017
This paper presents a method of using wearable accelerometers and microphones to detect instances... more This paper presents a method of using wearable accelerometers and microphones to detect instances of ad-hoc physical collaborations between members of a group. 4 people are instructed to construct a large video wall and must cooperate to complete the task. The task is loosely structured with minimal outside assistance to better reflect the ad-hoc nature of many real world construction scenarios. Audio data, recorded from chestworn microphones, is used to reveal information on collocation, i.e. whether or not participants are near one another. Movement data, recorded using 3-axis accelerometers worn on each person's head and wrists, is used to provide information on correlated movements, such as when participants help one another to lift a heavy object. Collocation and correlated movement information is then combined to determine who is working together at any given time. The work shows how data from commonly available sensors can be combined across multiple people using a simple, low power algorithm to detect a range of physical collaborations.
We investigate the usefulness of information from a wearable eyetracker to detect physical activi... more We investigate the usefulness of information from a wearable eyetracker to detect physical activities during assembly and construction tasks. Large physical activities, like carrying heavy items and walking, are analysed alongside more precise, hand-tool activities like using a screwdriver. Statistical analysis of eye based features like fixation length and frequency of fixations show significant correlations for precise activities. Using this finding, we selected 10, calibration-free eye features to train a classifier for recognising up to 6 different activities. Frame-byframe and event based results are presented using data from an 8-person dataset containing over 600 activity events. We also evaluate the recognition performance when gaze features are combined with data from wearable accelerometers and microphones. Our initial results show a duration-weighted event precision and recall of up to 0.69 & 0.84 for independently trained recognition on precise activities using gaze. Thi...
The academic success of individual students differs widely and it depends on various factors, ran... more The academic success of individual students differs widely and it depends on various factors, ranging from financial to social and to health aspects. In this article, we propose a concept for a novel productivity tracking system that provides the basis for a self-assessment of academic behaviour and that can be used by students to support their academic success. The development of such a system requires interdisciplinary efforts, most of them located in the field of collaborative interactive learning (CIL) that is grounded on a socio-technical system perspective. The system is interactive since it is based on bidirectional communication, collaborative in the sense that it uses students, other students, and external sources such as the Internet for generation of knowledge, and learning in the sense that it continuously and autonomously acquires knowledge. It is further self-organised as it decides about interaction partners and self-adaptive in terms of modifying its behaviour accord...
Proceedings of the 2018 ACM International Symposium on Wearable Computers, 2018
We present a study comparing the effect of real-time wearable feedback with traditional training ... more We present a study comparing the effect of real-time wearable feedback with traditional training methods for cardiopulmonary resuscitation (CPR). The aim is to ensure that the students can deliver CPR with the right compression speed and depth. On the wearable side, we test two systems: one based on a combination of visual feedback and tactile information on a smart-watch and one based on visual feedback and audio information on a Google Glass. In a trial with 50 subjects (23 trainee nurses and 27 novices,) we compare those modalities to standard human teaching that is used in nurse training. While a single traditional teaching session tends to improve only the percentage of correct depth, it has less effect on the percentage of effective CPR (depth and speed correct at the same time). By contrast, in a training session with the wearable feedback device, the average percentage of time when CPR is effective improves by up to almost 25%.
Communications in Computer and Information Science, 2016
To investigate incremental collaborative classifier fusion techniques, we have developed a compre... more To investigate incremental collaborative classifier fusion techniques, we have developed a comprehensive simulation framework. It is highly flexible and customizable, and can be adapted to various settings and scenarios. The toolbox is realized as an extension to the NetLogo multi-agent based simulation environment using its comprehensive Java-API. The toolbox has been integrated in two di↵erent environments, one for demonstration purposes and another, modeled on persons using realistic motion data from Zurich, who are communicating in an ad hoc fashion using mobile devices.
We study situations where (such as in a city festival) in the case of a phone signal outage cell ... more We study situations where (such as in a city festival) in the case of a phone signal outage cell phones can communicate opportunistically (for instance, using WiFi or Bluetooth) and we want to understand and control information spreading. A particular question is, how to prevent false information from spreading, and how to facilitate the spreading of useful (true) information? We introduce collaborative knowledge fusion as the operation by which individual knowledge claims are "merged". Such fusion events are necessarily local, e.g. happen upon the physical meetings of knowledge providers. We study and evaluate different methods for collaborative knowledge fusion and study the conditions for and tradeoffs of the convergence to a global true knowledge state under various conditions.
Utilizing Smartphones as an Effective Way to Support Patients with Bipolar Disorder: Results of the Monarca Study
European Psychiatry, 2015
Background Bipolar disorder is characterized by depressive and manic episodes, each with its own ... more Background Bipolar disorder is characterized by depressive and manic episodes, each with its own specific outcomes. To guarantee the best therapy it is important and necessary to assess the episodes of the disease and its exact degree of severity at an early stage. Methods During a time period of 12 weeks, 9 patients suffering from bipolar disorder were provided with a commercially available smartphone in order to collect behavioral patterns by the phone's internal sensors. These sensors included acceleration, GPS-traces, phone-call behavior and sound. During the trial the patients were also asked to fill out a daily self-assessment questionnaire that included a self-rating. Additionally, to gain ground truth psychological state examinations were performed every three weeks. Results The sensor traces are very similar to the diagnosed scores and thus clearly provide an accurate representation of the patient's state. Further, our data suggest a strong empirical evidence that the sensor based data are, on average, a more reliable and objective way of monitoring the mental state and mood than the patient's self-assessment. Conclusion The MONARCA system introduces new opportunities for the treatment of patients with bipolar disorder. The acquired data allow for identifying changes in the patient's condition at an early stage and therefore support the timely intervention by psychiatrists.
2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops, 2014
Activity Recognition (AR) Systems more and more find their way into our daily lives, from monitor... more Activity Recognition (AR) Systems more and more find their way into our daily lives, from monitoring daily activities to support in medical care. However, such systems tend to be used with narrowly defined specifications, demanding for applicationdependent setup and configuration by their users. A long term goal are autonomous systems, being able to work with no (or minimal) user interaction. Closely related to that vision is the ability of autonomously adding further input sources (e.g., sensors) at run-time, leading to an increased dimensionality of the inputspace. Our approach aims at systematically investigating methods necessary for the creation of self-adapting classification systems. This includes an architecture, based on Organic Computing (OC) principles, as well as the development of measures for comparing probabilistic models and procedures for evaluating classifiers of different dimensionality. With such evaluation techniques, systems should be able to adapt their system model at run-time in a self-organized manner. Besides self-improvement (adding a new sensor) we also address the problem of self-healing (replacing a sensor that dropped out).
Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques, 2013
This paper presents the lessons learnt on the design, development and evaluation of a pervasive c... more This paper presents the lessons learnt on the design, development and evaluation of a pervasive computing-based 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 set of relevant checklist items in the development of innovative solutions for mental health treatment and in a broader way for future research on personal health systems.
& THE NEW CORONAVIRUS pandemic has promoted the new development of mobile and wearable computing ... more & THE NEW CORONAVIRUS pandemic has promoted the new development of mobile and wearable computing in unprecedented ways. We discuss how on-body devices can help to fight the pandemic and may stay as a toolset to effectively deal with infectious diseases in the future. WHY WEARABLES? Researchers and health policy managers turned to smartphones and on-body devices mostly for their ubiquity, i.e., to offer health and safety-related information to a large share of the population or gather patient responses. Yet, smartphones and wearables enable fast data and information flow, which is particularly relevant for the rapid infectious character of SARS-CoV-2. We observe that continuous sensor and behavior data of smartphones and on-body devices are important as virus testing is associated with effort, cost, and provides only one-time information, and global immunization is still far away. We focus here on already existing and newly created wearable devices and smartphone apps for everyday use, but we exclude clinical and laboratory measurement systems, e.g., for heart and respiratory assessment. For researchers and public health authorities, symptom screening and tracking based on continuous sensor data from wearables and smartphones can
We aim at activity and context recognition in opportunistic sensor setups. The system ought to ma... more We aim at activity and context recognition in opportunistic sensor setups. The system ought to make use of sensor modalities that just happen to be available, rather than to rely on specific sensor deployment. In order to assess opportunistic activity recognition methods, we collected a large-scale dataset of complex activities in a highly sensor rich environment, with 72 sensors of 10 modalities in the environment, in objects and on-body. The dataset contains composite and atomic activities in large numbers (>28000 hand interactions). We present the activity scenario and the sensor setup. We show the user's activities and the corresponding sensor signals side by side. We argue that such a visualization may be an efficient form of dataset documentation, especially when such a dataset is shared, as it gives an insight into the complexity of the activities and richness of the sensor setup.
Opportunistic activity and context recognition systems draw from the characteristic to use sensin... more Opportunistic activity and context recognition systems draw from the characteristic to use sensing devices that just happen to be available rather than pre-defining a fixed sensor infrastructure at design time. Opportunistic sensing offers the possibility to obtain data from sensors that just happen to be available in the area surrounding the user. This enables users or applications to state recognition goals, saying what has to be sensed for, at runtime to the system. The available sensing devices that can contribute to the recognition goal are configured to an ensemble, which is the best set of sensors to recognize the goal. This paper describes the OPPORTUNITY Framework and shows its functionality with respect to four application cases (goal querying and sensor configuration, sensor appears/disappears, sensor learns from other sensor and sensor self trust) to show the dynamic nature of an opportunistic system as the available sensing infrastructure is not fixed and changes during runtime.
We present an analysis, using ROC curves, of a method for partitioning continuous activity data u... more We present an analysis, using ROC curves, of a method for partitioning continuous activity data using two microphones. The algorithm is based on utilising the difference in sound intensity recorded by microphones placed on the upper arm and on the wrist. We show that the method is feasible for detecting activities involving the interaction of the hand with tools or machinery where noise is produced close to the hand. We also show that the method is relatively robust across multiple subjects.
From motion to emotion: a wearable system for the multimedia enrichment of a Butoh dace performance
Journal of Mobile Multimedia, Jun 1, 2005
... Michael Barry, Juerg Gutknecht, Irena Kulka, Paul Lukowicz, Thomas Stricker. ... multimedia s... more ... Michael Barry, Juerg Gutknecht, Irena Kulka, Paul Lukowicz, Thomas Stricker. ... multimedia system based on a network of body worn motion sensors, a wearable computer and a visualization engine that is used to produce a visual enhancement of Butoh dance performance. ...
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Papers by Paul Lukowicz