Steffen Ortmann

A self-configuring privacy management architecture for pervasive systems

In this paper we discuss how to configure a ubiquitous environment in such a way that diverging privacy requirements in a multi user environment can be satisfied. I.e. we are investigating how to make sure that concerned as well as open minded users can be satisfied, at the same time at the same place. In order to reduce overhead and increase granularity of objects to be managed we propose to cluster all available sensors to what we call virtual sensors. All further operations such as allowing or suppressing sensor data are done on the virtual sensors. This concept allows to adapt the environment to diverging needs of several users automatically. Users are enabled to configure the environment by a one stop approach. We will introduce the concept of virtual sensors and discuss our privacy management architecture that exploits this virtual sensor concept to adapt a pervasive system to contradicting privacy requirements.

Detecting Elementary Arm Movements by Tracking Upper Limb Joint Angles With MARG Sensors

This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a two-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and four stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a seminaturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and seminaturalistic experiment, respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the seminaturalistic were detected correctly. Finally, the detection ratio remains close (±6%) to the average value, for different task durations further attesting to the algorithms robustness.

Telemedicine system for game-based rehabilitation of stroke patients in the FP7-“StrokeBack” project

Stroke is a disease with very high socio-economic impact. In average, the healthcare expenditure cost for Strokes across different countries in Europe and USA exceeds 3% of their entire healthcare expenditure, including inpatient treatments, outpatient hospital visits and long-term rehabilitation and care1 Therefore, there is an urgent need for devising an effective long-term care and rehabilitation strategy for stroke patients, which would actively involve patients in the rehabilitation process while minimizing costly human support. This paper reports on the results of the FP7-StrokeBack project where game-based training system has been proposed allowing physicians to supervise the rehabilitation of patents at home. The proposed approach empowers the patients and their caretakers for effective application of rehabilitation protocols in their home settings, while leading physicians are enabled to supervise the progress of the rehabilitation (and intervene if needed) through the use of Personal Health Record (PHR) system. The increased rehabilitation speed and ability to perform training at home directly improves quality of life of patients.

Remote medical treatment at home using the Java Mobile Sensor API

Since wireless sensor networks are successfully deployed in real life scenarios, applications in medical health-care, structural control, homeland security etc. become feasible. In those envisioned applications, easy maintenance and usability become crucial to staff members, e.g., to doctors or nurses. Not only for widespread distribution of hundreds of sensors, but also in tele-medical applications, remotely-controlled sensing and maintenance without direct access to sensors is required. For this purpose we present a middleware abstraction based on the standard Java Mobile Sensor API (JSR-256). It allows transparent access to sensor measurements, sensor information and maintenance data, which appear as local sensor resources to the user even if the sensors are connected via network. Hence, the user neither requires technical skills nor location information to request sensor data. This paper gives an architectural and functional overview of our middleware within the context of telemedicine. We demonstrate how our middleware approach supports patient monitoring for pre- and post-operative treatment at home.

The Privacy Advocate: Assertion of Privacy by Personalised Contracts

Privacy has been a hot topic in research for several years. A lot of different approaches to protect privacy have been proposed recently. Among these there are several tools for negotiation of privacy contracts. In this paper we present our privacy negotiation framework called “The Privacy Advocate”. It consists of three main parts: the policy evaluation unit, the signature unit and the preferences. In addition, our framework supports an interface for negotiation strategies, so that they are independent of the framework. The preferences can be expressed with a combination of P3P and APPEL. The tests we executed using a state of the art PDA clearly indicate that our framework can be used on mobile devices. The completion of a successful negotiation usually takes about 2 sec. including message transfer via an 802.11b wireless link. This involves multiple evaluations of proposals. Each of them is done in less than 250 msec.

Adaptive pruning of event decision trees for energy efficient collaboration in event-driven WSN

Wireless Sensor Networks (WSN) are considered to be the key-enabler for low cost highly distributed applications in the area of homeland security, healthcare, environmental monitoring etc. A necessary prerequisite is reliable and efficient event detection. This paper introduces a novel approach for event configuration and in network processing, called Event Decision Trees (EDT). An EDT enables every node to self-divide event queries according to its resources. EDT autonomously adapt to the tasks assigned, even though it requires to organize collaboration between nodes to deliver expected results. The effort for maintain formal EDT is evaluated by analysis and simulations. Our results show that the proposed lease-based mechanism for maintaining producer/consumer pairs in an EDT outperforms even idealized Acknowledgment-based approaches.

On the sensor choice and data analysis for classification of elementary upper limb movements

In this paper we present a systematic exploration for determining the appropriate type of inertial sensor and the associated data processing techniques for classifying four fundamental movements of the upper limb. Our motivation was to explore classification techniques that are of low computational complexity enabling low power processing on body-worn sensor nodes for unhindered operation over a prolonged time. Kinematic data was collected from 18 healthy subjects, repeating 20 trials of each movement, using tri-axial accelerometers and tri-axial rate gyroscopes located near the wrist. Ten time-domain features extracted from data from individual sensor streams, their modulus and specific fused signals, were used to train classifiers based on three learning algorithms: LDA, QDA and SVM. Each classifier was evaluated using a leave-one-subject-out strategy. Our results show that we can correctly identify the four arm movements, with sensitivities in the range of 83-96%, using data from just a tri-axial gyroscope located near the wrist, and requiring only 12 features in combination with the lower complexity LDA learning algorithm.

WiSec 2011 demo: demonstrating self-contained on-node counter measures for various jamming attacks in WSN

This paper shortly introduces our real-time jamming detection approach which can be executed on standard wireless sensor nodes. The benefits are that no thresholds need to be defined since it detects jamming based on deviations of the Received Signal Strength Indication (RSSI) and the fact that for doing so it needs only 422 Bytes of memory including execution code and stored RSSI values. Our mock-up demonstrator visualises how various attacks of permanent, periodic and random jamming effect RSSI values and how the sensor nodes independently of the location of the jammer reliably indicate ongoing jamming.

Evaluations with Patients and Lessons Learned

A set of exercises for different stages of stroke recovery has been collected and combined with the analysis of relevant literature and our practical clinical knowledge. This allowed for more reliable clinical testing of relevant exercises in a telerehabilitation setting, supported by teleconferencing. Based on the results of these efforts, we defined basic principles for treatment of the upper limb using preferred distal movements and evidence-based therapeutic methods of treatment. We developed a set of concrete rehabilitation exercises, focusing on motor function training including bimanual activities, coordination, reach, grasp and dexterity tasks. According to the results of discussing selected exercises and technical capabilities, we modified the rehabilitation exercises and present the final selection in this chapter.

Adapting pervasive systems to multiuser privacy requirements

In this paper, we discuss how diverging privacy requirements in a multiuser ubiquitous environment can be satisfied. We are investigating how to ensure that the requirements of concerned as well as open-minded users can be satisfied, simultaneously. In order to reduce overhead and increase granularity of the objects to be managed, we propose to cluster all available sensors, creating objects we denote as virtual sensors. All further operations are done on the virtual sensors. We will discuss our management architecture and present our simulation environment showing how the pervasive environment automatically adapts to user requirements and user locations.