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Matthies, Denys J.C.; Haescher, Marian; Bieber, Gerald; Urban, Bodo

iWOAR 2016: 3rd international Workshop on Sensor-based Activity Recognition and Interaction

2016

New York : ACM Press, 2016

International Workshop on Sensor-based Activity Recognition (iWOAR) <3, 2016, Rostock, Germany>

ACM International Conference Proceedings Series 1183

Wearable sensors potentially enable for a better and unobtrusive recognition of human activity and the state of rest, sleep, stress and drive the ongoing trend of the quantified self-movement. As an enabling technology, powerful, while yet inexpensive MEMS-Chips (micro-electro-mechanical system) push the penetration of a broad variety of mobile devices. Thereby, these devices gain high interest, not only in terms of general customer products, but also as integrated systems in an industrial context, either way to enable continuous monitoring of complex life processes and workplace situations. Another challenge that research is facing concerns the limited human abilities of interaction in context of mobility and in situations, in which high attention is being demanded. New and alternative ways are needed to be found in order to take advantage of all human capabilities to enable safe and unobtrusive interaction.

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Matthies, Denys J.C.; Haescher, Marian; Bieber, Gerald; Salomon, Ralf; Urban, Bodo

SeismoPen: Pulse Recognition via a Smart Pen

2016

Association for Computing Machinery (ACM): Proceedings of the 9th International Conference on PErvasive Technologies Related to Assistive Environments : PETRA 2016. New York: ACM, 2016. (ACM International Conference Proceedings Series (ICPS) 01198), Article No. 36, 4 p.

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <9, 2016, Corfu, Greece>

We propose SeismoPen, an enhanced ballpoint pen, which is capable of calculating the patient's heart rate. This is enabled when being pressed the pen towards the patient's throat so it can sense and analyze the seismographic micro-eruption caused by the pulsing blood. We developed a suitable algorithm and tested three sensor setups in which we attached (1) a force-sensing resistor (FSR), (2) an accelerometer, and (3) a piezoelectric transducer to the pen's head. We also conducted a user study, which resulted in suggesting SeismoPen to be potentially more accepted by users, since it is less obtrusive than alternative measurement methods. In contrast to medical devices, this simple pen looks less perilous and potentially reduces the risk of triggering symptoms of a white coat hypertension.

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Haescher, Marian; Trimpop, John; Matthies, Denys J.C.; Urban, Bodo

SeismoTracker: Upgrade Any Smart Wearable to Enable a Sensing of Heart Rate, Respiration Rate, and Microvibrations

2016

Kaye, Jofish (Conference Chair) et al.: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing : CHI EA 2016. New York: ACM Press, 2016, pp. 2209-2216

Conference on Human Factors in Computing Systems (CHI) <34, 2016, San Jose, CA, USA>

In this paper we present a method to enable any smart Wearable to sense vital data in resting states. These resting states (e.g. sleeping, sitting calmly, etc.) imply the presence of low-amplitude body-motions. Our approach relies on seismocardiography (SCG), which only requires a built-in accelerometer. Compared to commonly applied technologies, such as photoplethysmography (PPG), our approach is not only tracking heart rate (HR), but also respiration rate (RR), and microvibrations (MV) of the muscles, while being also computational inexpensive. In addition, we can calculate several other parameters, such as HR variability and RR variability. Our extracted vital parameters match with the vital data gathered from clinical state-of-the art technology. These data allow us to gain an impression on the user's activity, quality of sleep, arousal and stress level over the whole day, week, month, or year. Moreover, we can detect whether a device is actually worn or doffed, which is crucial when connecting such data with health services. We implemented our method on two current smartwatches: a Simvalley AW420 RX as well as on a LG G Watch R and recorded user data for several months. A web platform enables to keep track of one's data.

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Haescher, Marian; Trimpop, John; Bieber, Gerald; Urban, Bodo

SmartMove: A Smartwatch Algorithm to Distinguish Between High- and Low-Amplitude Motions as well as Doffed-States by Utilizing Noise and Sleep

2016

Matthies, Denys J.C. (Ed.) et al.: iWOAR 2016 : 3rd international Workshop on Sensor-based Activity Recognition and Interaction. New York: ACM Press, 2016. (ACM International Conference Proceedings Series 1183), Art. No. 1, 8 p.

International Workshop on Sensor-based Activity Recognition (iWOAR) <3, 2016, Rostock, Germany>

In this paper, we describe a self adapting algorithm for smart watches to define individual transitions between motion intensities. The algorithm enables for a distinction between high-amplitude motions (e.g. walking, running, or simply moving extremities) low-amplitude motions (e.g. human microvibrations, and heart rate) as well as a general doffedstate. A prototypical implementation for detecting all three motion types was tested with a wrist-worn acceleration sensor. Since the aforementioned motion types are userspecific, SmartMove incorporates a training module based on a novel actigraphy-based sleep detection algorithm, in order to learn the specific motion types. In addition, our proposed sleep algorithm enables for reduced power consumption since it samples at a very low rate. Furthermore, the algorithm can identify suitable timeframes for an inertial sensor-based detection of vital-signs (e.g. seismocardiography or ballistocardiography).

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Haescher, Marian; Matthies, Denys J.C.; Trimpop, John; Urban, Bodo

A Study on Measuring Heart- and Respiration-Rate via Wrist-Worn Accelerometer-based Seismocardiography (SCG) in Comparison to Commonly Applied Technologies

2015

Matthies, Denys J.C. (Ed.) et al.: iWOAR 2015 : 2nd international Workshop on Sensor-based Activity Recognition and Interaction. New York: ACM Press, 2015, 6 p.

International Workshop on Sensor-based Activity Recognition (iWOAR) <2, 2015, Rostock, Germany>

Since the human body is a living organism, it emits various life signs which can be traced with an action potential sensitive electromyography, but also with motion sensitive sensors such as typical inertial sensors. In this paper, we present a possibility to recognize the heart rate (HR), respiration rate (RR), and the muscular microvibrations (MV) by an accelerometer worn on the wrist. We compare our seismocardiography (SCG) / ballistocardiography (BCG) approach to commonly used measuring methods. In conclusion, our study confirmed that SCG/BCD with a wrist-worn accelerometer also provides accurate vital parameters. While the recognized RR deviated slightly from the ground truth (SD=16.61%), the detection of HR is nonsignificantly different (SD=1.63%) to the gold standard.

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Haescher, Marian; Trimpop, John; Matthies, Denys J.C.; Bieber, Gerald; Urban, Bodo; Kirste, Thomas

aHead: Considering the Head Position in a Multi-sensory Setup of Wearables to Recognize Everyday Activities with Intelligent Sensor Fusions

2015

Kurosu, Masaaki (Ed.): Human-Computer Interaction. Proceedings Part II : Interaction Technologies. Springer International Publishing, 2015. (Lecture Notes in Computer Science (LNCS) 9170), pp. 741-752

International Conference on Human-Computer Interaction (HCII) <17, 2015, Los Angeles, CA, USA>

In this paper we examine the feasibility of Human Activity Recognition (HAR) based on head mounted sensors, both as stand-alone sensors and as part of a wearable multi-sensory network. To prove the feasibility of such setting, an interactive online HAR-system has been implemented to enable for multi-sensory activity recognition while making use of a hierarchical sensor fusion. Our system incorporates 3 sensor positions distributed over the body, which are head (smart glasses), wrist (smartwatch), and hip (smartphone). We are able to reliably distinguish 7 daily activities, which are: resting, being active, walking, running, jumping, cycling and office work. The results of our field study with 14 participants clearly indicate that the head position is applicable for HAR. Moreover, we demonstrate an intelligent multi-sensory fusion concept that increases the recognition performance up to 86.13 % (recall). Furthermore, we found the head to possess very distinctive movement patterns regarding activities of daily living.

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Haescher, Marian; Matthies, Denys J.C.; Urban, Bodo

Anomaly Detection with Smartwatches as an Opportunity for Implicit Interaction

2015

Association for Computing Machinery (ACM): MobileHCI 2015 : Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. New York: ACM, 2015, pp. 955-958

International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) <17, 2015, Copenhagen, Denmark>

In this paper we introduce application scenarios for implicit interaction with Smartwatches for the purpose of user assistance, to create awareness, and to enhance as well as simplify the interaction with Wearables. We envision three scenarios (1) the detection of sleep apnea, (2) the detection of epileptic seizures, and (3) a detection of accidents such as falling, car crashes etc., which are presented and discussed. Therefore, the recognition of all incidents described will be discussed under the meta-topic of anomaly detection.

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Haescher, Marian; Matthies, Denys J.C.; Bieber, Gerald; Urban, Bodo

CapWalk: A Capacitive Recognition of Walking-Based Activities as a Wearable Assistive Technology

2015

Association for Computing Machinery (ACM): Proceedings of the 8th International Conference on PErvasive Technologies Related to Assistive Environments : PETRA 2015. New York: ACM, 2015. (ACM International Conference Proceedings Series (ICPS) 1032)

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <8, 2015, Corfu, Greece>

In this research project, we present an alternative approach to recognize various walking-based activities based on the technology of capacitive sensing. While accelerometry-based walking detections suffer from reduced accuracy at low speeds, the technology of capacitive sensing uses physical distance parameters, which makes it invariant to the duration of step performance. Determining accurate levels of walking activity is a crucial factor for people who perform walking with tiny step lengths such as elderlies or patients with pathologic conditions. In contrast to other gait analysis solutions, CapWalk is mobile and less affected by external influences such as bad lighting conditions, while it is also invariant to external acceleration artifacts. Our approach enables a reliable recognition of very slow walking speeds, in which accelerometer-based implementations can fail or provide high deviations. In CapWalk we present three different capacitive sensing prototypes (Leg Band, Chest Band, Insole) in the setup of loading mode to demonstrate recognition of sneaking, normal walking, fast walking, jogging, and walking while carrying weight. Our designs are wearable and could easily be integrated into wearable objects, such as shoes, pants or jackets. We envision such gathered information to be used to assist certain user groups such as diabetics, whose optimal insulin dose is depending on bread units and physical activity or elderlies whose personalized dosage of medication can be better determined based on their physical activity.

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Matthies, Denys J.C.; Haescher, Marian; Aehnelt, Mario; Bieber, Gerald; Urban, Bodo

iWOAR 2015: 2nd international Workshop on Sensor-based Activity Recognition and Interaction

2015

New York : ACM Press, 2015

International Workshop on Sensor-based Activity Recognition (iWOAR) <2, 2015, Rostock, Germany>

Wearable sensors potentially enable for a better and unobtrusive recognition of human activity and the state of rest, sleep, stress and drive the ongoing trend of the quantified self-movement. As an enabling technology, powerful, while yet inexpensive MEMS-Chips (micro-electro-mechanical system) push the penetration of a broad variety of mobile devices. Thereby, these devices gain high interest, not only in terms of general customer products, but also as integrated systems in an industrial context, either way to enable continuous monitoring of complex life processes and workplace situations. Another challenge that research is facing concerns the limited human abilities of interaction in context of mobility and in situations, in which high attention is being demanded. New and alternative ways are needed to be found in order to take advantage of all human capabilities to enable safe and unobtrusive interaction. This conference-like workshop is initiated and organized by the Fraunhofer IGD in Rostock. While we established the iWOAR workshop the 2nd time - in Rostock, North Germany - we aim to establish an annual tradition with the approach to bring together science and industry. It offers scientists, interested parties, and users in the area of sensor-based activity recognition and interaction the possibility to an exchange of experiences and a presentation of best-practice examples, as well as technical and scientific results. The workshop focuses on technologies for human activity recognition and interaction via inertial sensors (accelerometers, gyroscopes etc.) and their scientific applications.

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Matthies, Denys J.C.; Haescher, Marian; Alm, Rebekka; Urban, Bodo

Properties of a Peripheral Head-Mounted Display (PHMD)

2015

Stephanidis, Constantine (Ed.): HCI International 2015 - Posters' Extended Abstracts. Proceedings Part I : HCI International 2015. Springer International Publishing, 2015. (Communications in Computer and Information Science (CCIS) 528), pp. 208-213

International Conference on Human-Computer Interaction (HCII) <17, 2015, Los Angeles, CA, USA>

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Haescher, Marian; Bieber, Gerald; Trimpop, John; Urban, Bodo; Kirste, Thomas; Salomon, Ralf

Recognition of Low Amplitude Body Vibrations via Inertial Sensors for Wearable Computing

2015

Giaffreda, Raffaele (Ed.) et al.: Internet of Things : User-Centric IoT. Springer International Publishing, 2015. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST) 150), pp. 236-241

International Summit on Internet of Things (IoT360) <1, 2014, Rome Italy>

Pathological shaking of the body or extremities is widely known and might occur at chronic diseases e.g. Parkinson. The rhythmical shaking, also known as tremor, can be such intense that extremities are flapping. Under certain circumstances, healthy people also show a shivering and shaking of their body. For example, humans start to shiver whenever it is too cold or if feelings such as stress or fear become dominant. Some wearable devices that are in direct contact to the body, such as smartwatches or smartglasses, provide a sensing functionality of acceleration force that is sufficient to detect the tremor of the wearer. The tremor varies in frequency and intensity and can be identified, by applying detection algorithms and signal filtering. Former works figured that all endotherms show muscle vibrations. These vibrations occur in the condition of sleeping as well as when being awake, or in unconsciousness. Furthermore, the vibrations are also present when subjects are physically active, emotionally stressed, or absolutely relaxed. The vibration itself varies in structure, amplitude, and frequency. This paper shows that these muscle vibrations are measureable by acceleration sensors attached to the user, and provides an outlook to new applications in the future. It also proves that custom mobile devices are able to detect body and muscle vibration and should motivate designers to develop new applications and treatment opportunities.

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Trimpop, John; Haescher, Marian; Bieber, Gerald; Matthies, Denys J.C.; Lämmel, Friedrich; Burggraf, Paul

The Digital Health Companion: Personalized Health Support on Smartwatches via Recognition of Activity- and Vital-Data

2015

Schulz, Hans-Jörg (Ed.) et al.: Proceedings of the International Summer School on Visual Computing 2015. Stuttgart: Fraunhofer Verlag, 2015, pp. 125-133

International Summer School on Visual Computing <1, 2015, Rostock, Germany>

It has been shown that in various fields of social life, people tend to seek opportunities to measure their daily activities, bodily behaviors, and health related parameters. These kinds of activity tracking should be accomplished comfortably, unobtrusively and implicitly. Tracking behavior can be important for certain user groups, such as the growing population of elderlies. These people have a substantially higher risk of falling down, as they often live alone and thus have a greater need for other supporting services, as emergencies quickly occur. We would like to support these people, while providing a comfortable emergency detection and a monitoring of physical activities. Moreover, we believe such tracking applications to be beneficial for any user group, since we can perceive the trend of quantified self: knowing about one's own body characteristics, which is expressed in body movement. Simultaneously, we also perceive that a strong desire for a comprehensive monitoring of vital and health data is emerging. In this paper we describe the concept and implementation of the Digital Health Companion, a smart health support system that combines research developments of activity, vital data, and anomaly recognition with the functionality of contemporary smartwatches. The system's health monitoring includes an emergency detection and allows for the prevention of health risks in the short and long term through the recognition of body movement patterns.

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Peter, Christian; Prophet, Heinrich; Haescher, Marian

AktiDia - Aktivitätsmessung in der Dialyse

2014

Bieber, Gerald (Ed.) et al.: WOAR 2014 : Proceedings of the Workshop on Sensor-based Activity Recognition. Stuttgart: Fraunhofer Verlag, 2014, pp. 80-96

Workshop on Sensor-Based Activity Recognition (WOAR) <1, 2014, Rostock, Germany>

Im Rahmen der klinischen Studie AktiDia wurden Dialyse-Patienten mit verschiedenen Sensoren zur Erfassung der körperlichen Aktivität ausgestattet. Die Studie verfolgte drei Fragestellungen: zum einen wurde untersucht, wie verschiedene Aktivitätssensoren von Patienten angenommen werden und welche Eigenschaften ein Sensor für die Überwachung körperlicher Aktivität von Dialysepatienten haben sollte, um eine möglichst hohe Akzeptanz zu erlangen. Zweitens wurde das Aktivitätsverhalten von Dialysepatienten über einen Zeitraum von einer Woche untersucht, wobei insbesondere charakteristische Merkmale der Aktivitäten an Dialysetagen und den Tagen zwischen den Behandlungen betrachtet wurden sowie entsprechende Korrelationen zum Nachtschlaf der Patienten. Drittens wurde untersucht, ob und wie sich das Tragen eines Aktivitätssensors auf das Aktivitätsverhalten von Dialysepatienten auswirkt.

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Bieber, Gerald; Wacker, Fred; Haescher, Marian; Alm, Rebekka

Konzept zur Arbeitsgeräteerkennung mittels Vibrationsanalyse durch Methoden des maschinellen Lernens

2014

Cleve, Jürgen (Ed.) et al.: WIWITA 2014. Proceedings : 9. Wismarer Wirtschaftsinformatiktage. Wismar, 2014, pp. 247-252

Wismarer Wirtschaftsinformatiktage (WIWITA) <9, 2014, Wismar, Germany>

Die Verwendung verschiedenster Arbeitsgeräte unterstützt den Arbeiter bei seiner Tätigkeit und ist im industriellen Umfeld nicht mehr wegzudenken. Viele elektrisch oder pneumatisch betriebene Arbeitsgeräte erzeugen im Betrieb Vibrationen, die auf den Anwender übertragen werden. Überschreiten diese über längere Zeit ein gewisses Maß, kann dieses langfristig zu Beeinträchtigungen und Schädigungen bis hin zur Berufsunfähigkeit führen. Entsprechend der Lärm- und Vibrations-Arbeitsschutzverordnung ist an Arbeitsplätzen mit potentieller Exposition gegenüber Vibrationen eine laufende Gefährdungsanalyse vorgeschrieben. In der Praxis geschieht dies derzeit stichprobenartig durch externe Experten oder behelfsmäßig anhand von Zeitschätzungen und Tabellenwerten. In der einschlägigen Literatur wird darauf verwiesen, dass die tatsächliche Exposition dabei häufig überschätzt wird, wodurch den betroffenen Unternehmen ein finanzieller Schaden entsteht. In der vorliegenden Arbeit wird ein Konzept vorgestellt, um Hand-Arm-Vibrationen durch eine handelsübliche Smartwatch abzuschätzen. Dabei werden durch verschiedene Sensoren der Smartwatch die Beschleunigungskräfte (Akzelerometer), die Winkeländerungen (Gyroskop) sowie die Geräusche (Mikrofon) gemessen. Mit Methoden des maschinellen Lernens können auf dieser Grundlage die verwendeten Arbeitsgeräte sowie die genaue Nutzungsdauer bestimmt werden. Auf diese Weise wird mit preisgünstiger COTS-Hardware eine wesentlich genauere Bewertung vibrationsbedingter Gefährdungen am Arbeitsplatz möglich. Das System kann den Träger bei zu hoher Vibrationsbelastung selbstständig warnen. Darüber hinaus können die durchgeführten Arbeiten bei der Ausführung automatisch oder manuell annotiert werden. Um die grundlegende Machbarkeit des Konzeptes zu überprüfen, wurden unter Verwendung von Arbeitsgeräten aus sechs Geräteklassen mit 13 Probanden Messdaten erhoben und mit dem beschriebenen Verfahren analysiert. Dabei zeigte sich zunächst, dass der Gebrauch stark vibrierender Arbeitsgeräte mit hoher Präzision von Inaktivität abgegrenzt werden kann, wodurch eine Zeiterfassung der Gerätenutzung ermöglicht wird. Darüber hinaus konnten im Laborumfeld einzelne Werkzeuge auch zwischen verschiedenen Probanden wiedererkannt werden. Die Einsatzfähigkeit unter praxisnahen Bedingungen muss jedoch in weiteren Arbeiten untersucht werden.

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Haescher, Marian

Multi-Sensory Environment Analysis and Human Activity Recognition via Wearable Technologies

2014

Bieber, Gerald (Ed.) et al.: WOAR 2014 : Proceedings of the Workshop on Sensor-based Activity Recognition. Stuttgart: Fraunhofer Verlag, 2014, pp. 2-11

Workshop on Sensor-Based Activity Recognition (WOAR) <1, 2014, Rostock, Germany>

The sensing of human activities and user surrounding environments is an essential topic in computer science. Application domains include the Ambient Assisted Living (AAL), healthcare, sports gear and military use cases. Especially mobile or wearable technologies made significant progress in the past years. The current development of Smartwatches and Smartphones which include a variety of sensors is a good example for this progress. With the increasing sensor density in unobtrusive wearable designs, new ways for complex Human Activity Recognition (HAR) and environmental sensing (ES) are opened. This paper focuses on the current state of the art in wearable sensor technologies and gives a short overview of present techniques for HAR and ES. Therefore, a classification of sensors by use case and body position is made. Furthermore, the principal challenges and issues are discussed and known solutions will be referenced. Finally, existing problems that should be addressed are pointed out.

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Bieber, Gerald; Haescher, Marian; Vahl, Matthias

Sensor Requirements for Activity Recognition on Smart Watches

2013

The University of Texas at Arlington (UTA): Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments : PETRA 2013. New York: ACM, 2013, 6 p.

ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <6, 2013, Rhodes Island, Greece>

The new generation of watches is smart. Smart watches are connected to the internet and provide sensor functionality that allows an enhanced human-computer-interaction. Smart watches provide a gesture interaction and a permanent monitoring of physical activities. In comparison to other electronic home consumer devices with integrated sensors, Smart watches provide monitoring data for 24h per day, many watches are water resistant and can be worn constantly. The integrated sensors are varying in performance and are not intended to distinguish between different states of activity and inactivity. This paper reports on identified requirements on sensors of smart watches for detection of activity, inactivity as well as sleep detection. Hereby a new measurement quantity is introduced and applications of heart beat detection or wearing situation are presented.

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Bieber, Gerald; Haescher, Marian; Peter, Christian; Aehnelt, Mario; Richter, Claas; Gohlke, Holger

Hands-free Interaction mittels Handgelenksensoren für mobile Assistenzsysteme

2011

Institut für Multimediatechnik (IFM): 6. Multimediakongress Wismar 2011 : Netzwerk - Forschung - Innovation, 7 pp.

Kongress Multimediatechnik <6, 2011, Wismar, Germany>

Zunehmend verbreitet sich der Einsatz von digitalen Wartungshandbüchern und begleitender Multimedia- Unterstützung bei Montage und Wartungsarbeiten. Interaktive PDF Dokumente, Montagevideos oder Audioanleitungen können auf immer preiswerter werdenden Tablet PCs oder Smartphones zielgerecht angeboten werden. Im realen Arbeitsumfeld können jedoch Probleme mit der Bedienung dieser Geräte auftreten, da Arbeitshandschuhe getragen werden oder die Hände nicht frei oder stark verschmutzt sind. Neue Interaktionsformen können hier helfen, indem sie eine robuste Bedienung der Geräte ohne Einsatz einzelner Finger ermöglicht. Bereits heute sind erste Geräte am Markt, die entsprechende Interaktionen sowie neue Visualisierungsmöglichkeiten eröffnen. Handgelenksdisplays und bald auch Uhrenhandys (wrist phones) mit entsprechenden Minidisplays und integrierten Sensoren ermöglichen schon heute faszinierende Interaktionen. Dieser Beitrag beschreibt neue Konzepte zur Interaktion mit Handgelenkssensoren und -displays und stellt die damit möglich gewordene Hands-free Interaction (HFI) für mobile Assistenzsysteme vor. Die Technologie der HFI trennt die Phasen der normalen Bewegung während der Arbeitsausführung von den Bewegungen innerhalb der Interaktionsphase. Dieses ermöglicht eine nahtlose Bedienung von Geräten im Arbeitsablauf und es ergeben sich besonders im Bereich des Maschinen- und Anlagenbaus neue Möglichkeiten, den Montagearbeiter oder Wartungstechniker bei der Ausführung seiner Arbeit zu unterstützen. Mittels der HFI, die Gesten durch Beschleunigungssensoren erfasst, werden neue Bedienkonzepte und Interaktionselemente dargestellt, die in rauen Umgebungen eingesetzt werden können.