The estimation of internal joint loading is critical for assessing musculoskeletal health in daily life. This study proposes a simplified method for estimating knee joint contact forces (JCF) using only five inertial measurement units (IMUs) placed on the pelvis, upper legs, and lower legs. Data were collected from three healthy adults performing activities of daily living (ADL) in a home-like environment. Kinematics and ground reaction forces (GRF) were estimated using segment orientations and accelerations, and the virtual pivot point (VPP) concept. Joint reaction forces were then computed using a musculoskeletal model in OpenSim through static optimization and joint reaction analysis. Results showed strong correlations, larger than 0.79, with a mean Root Mean squared Error (RMSE) of 1.06 body weight (BW), that is about 15% relative to the maximum reference range, compared to a reference full-body system. Some error peaks occurred before toe-off, likely due to kinematic inaccuracies. These findings support the feasibility of accurate JCF estimation with minimal instrumentation, enabling realworld monitoring and potential clinical applications. Future work will address calibration improvements to reduce localized estimation errors.

Inertial measurement units are used in ambulatory human movement analysis, but their use is often subject to domain- or application-specific assumptions and methods to compensate for drift in kinematic estimations. Here, we propose and evaluate a generic drift reduction technique for orientation estimation. A second order Taylor approximation for 3D orientation estimation from sampled angular velocity is derived and evaluated on a publicly available dataset containing angular velocity data of 4 runners. The use of a second order Taylor approximation substantially reduces drift when the angular velocity has considerable contributions along all three axes. The second order Taylor approximation could therefore facilitate the use of minimal sensor setups for the study of dynamic 3D human movements.

In Human Behaviour Understanding, social interaction is often modeled on the basis of lower level action recognition. The accuracy of this recognition has an impact on the system's capability to detect the higher level social events, and thus on the usefulness of the resulting system. We model team interactions in volleyball and investigate, through simulation of typical error patterns, how one can consider the required quality (in accuracy and in allowable types of errors) of the underlying action recognition for automated volleyball monitoring. Our proposed approach simulates different patterns of errors, grounded in related work in volleyball action recognition, on top of a manually annotated ground truth to model their different impact on the interaction recognition. Our results show that this can provide a means to quantify the effect of different type of classification errors on the overall quality of the system. Our chosen volleyball use case, in the rising field of sports monitoring, also addresses specific team related challenges in such a system and how these can be visualized to grasp the interdependencies. In our use case the first layer of our system classifies actions of individual players and the second layer recognizes multiplayer exercises and complexes (i.e. sequences in rallies) to enhance training. The experiments performed for this study investigated how errors at the action recognition layer propagate and cause errors at the complexes layer. We discuss the strengths and weaknesses of the layered system to model volleyball rallies. We also give indications regarding what kind of errors are causing more problems and what choices can follow from them. In our given context we suggest that for recognition of non-Freeball actions (e.g. smash, block) it is more important to achieve a higher accuracy, which can be done at the cost of accuracy of classification of Freeball actions (which are mostly plays between team members and are more interchangable as to their role in the complexes).

Previous research has shown that estimating full-body poses from a minimal sensor set using a trained ANN without explicitly enforcing time coherence has resulted in output pose sequences that occasionally show undesired jitter. To mitigate such effect, we propose to improve the ANN output by combining it with a state prediction using a Kalman Filter. Preliminary results are promising, as the jitter effects are diminished. However, the overall error does not decrease substantially.

Quick and easy access to performance data during matches and training sessions is important for both players and coaches. While there are many video tagging systems available, these systems require manual effort. This paper proposes a system architecture that automatically supplements video recording by detecting events of interests in volleyball matches and training sessions to provide tailored and interactive multi-modal feedback.

This study aims to get, starting from behavioural change theories, a better understanding of the perceptions of patients regarding their own responsibility for promoting their health and to translate this into requirements for development and implementation of eHealth services. Nineteen patients with Diabetes Type 2 and nine older adults (>65 years) with lower limb or hip fracture were interviewed. The topic list was based on the Self-Determination Theory, the Theory of Change and the Social Cognitive Theory and included Autonomy, Relatedness, Competence, Motivation, Skills and Knowledge. Results show that both patient groups have insufficient knowledge about proper health management. In addition, patients with Diabetes Type 2 appear to be insufficiently motivated for life style changes with outcomes that become apparent mainly in long-term. Patients with lower limb or hip fractures are in contrast highly motivated but lack autonomy, competence and skills. Both patients groups feel positively influenced by relatives, rely to a large extent on or have a wish for more in depth involvement of the professionals. These issues will probably hamper patients from becoming proactive in improving their health and using technology but are expected to be solvable when addressed in the development of the technologies as well as with implementing in daily health care.

This paper address the challenge of design a feasible social-based mechanism to manage wireless mobile connectivity. In a previous work, we proposed a methodology to share connectivity experiences among mobile users inside on-line social networks [11]. The aim was explore peoples social circles to enhance their wireless connectivity experiences e.g., QoS metrics such as: throughput, latency and signal quality. In this paper, details of the mashups, between wireless connectivity context data and location-based social media, are provided. We report how this data is handled using complex networks metrics e.g., vertex's strength and centrality degree, to identify high density handover areas, define the mobile users' reputation and to reveal the networks' coverage. Real experiments showed that collaboration can improve QoS metrics from ∼18 to ∼30% if compared to just use a mobility predictor or a modern operational system, respectively. The discussion unfolds with focus on the collaboration's efficiency as function of time, number of users, discovered area size and mobility patterns.

Wireless connectivity context data is composed by date, time, geographical localization, and QoS metrics, to cite the most common. These data are employed, in a particular way, by fundamental techniques for context-aware connectivity management, e.g. mobility predictors, handoff mechanisms and mobility management. For instance, mobility and QoS predictors use, as input, previous georeferenced network context data. Normally, context data are available in hardly updated databases with considerable size. In this paper, we propose a social-based methodology to allow mobile users collaborate to discover wireless connectivity islands. The methodology is composed by methods to gather, combine, summarize and share context data inside the users' social circles. We, also, designed a schema to mashup context data with location-based social media. It is result of a prototyping effort and we focus the discussion on its feasibility and limitations in terms of storage size, power consumption and QoS metrics.

The advances in the area of location based computing will soon make it feasible to predict the availability of wireless networks and their application level Quality of Service (QoS) characteristics along the location and time dimensions. Such predictions are referred to as QoS predictions which are provided by the QoS Context Source (QoSCS) hosted in the fixed network. Herewith we present a network selection mechanism on the mobile device which requests QoS predictions as needed and uses these predictions combined with the current location and time to select the wireless network which satisfies QoS requirements of the services hosted on a mobile device. We evaluate the performance of the proposed scheme using simulations and compare the results with the other mechanism that only uses information locally available on the mobile device for the network selection. The results obtained from the simulations encourage further system development and prototyping.

The pervasive computing world in which the context-aware applications are aimed at is constituted of multiple network environments, e.g. ad hoc, mobile and fixed. There exist specialized Context Management Systems (CMSs) addressing context management needs of every network environment and the existence of a single CMS catering all the network environments does not seem to be plausible. Placing CMS bridges between different types of CMS is a practical approach for exchanging context information in the multiple network environments. Herewith, we focus on a bridge named as Context Discovery Adapter (CDA) which bridges two CMSs namely Mobile Service Platform (targeted towards the mobile network environment) and Ahoy (designed for the ad-hoc network environment), with greatly different properties. CDA enables context discovery and exchange across MSP and Ahoy by means of a mobile device which is capable of participating in both of the CMSs and specialized CDA broker service. The successful working prototype of CDA offers possibility for richer pervasive context-aware applications and a platform for further research.

This paper presents the early phase requirements elicitation for a work-related neck-shoulder pain teletreatment trial and the assessment of those requirements in respect of their importance to the trial and the feasibility of the needed software adaptations of the telemedicine system within the constraints of the project organizing the trial. The elicitation adopts specific solutions and constraints that are typical for working practices in medicine. In particular, the contribution of this paper is its holistic approach in addressing the intertwining of the requirements elicitation and the trial design. Trial design defines the trial settings and the treatment protocols in a standardized way such that treatment efficacy can be evaluated based on evidence. A cyclic approach which applies a set of elicitation techniques and informal specification styles (e.g. tables and mock-ups) is adopted in order to converge on a set of requirements understood and agreed upon also by the trial designers, who are medical professionals, often unaware of formal methods, and who co-shape the telemedicine system.

Mobile Patient Monitoring System (MPMS) is positioned to provide high quality healthcare services in the near future. The gap between its application demands and resource supplies, however, still remains and may hinder this process. Dynamic context-aware adaptation mechanisms are required in order to meet the stringent requirements on such mission critical applications. The fundamental model underlying an MPMS includes a set of biosignal data processing tasks distributed across a set of networked devices. In our earlier work, we designed and validated a task distribution framework to support dynamic system reconfiguration of MPMS by means of task redistribution. This paper focuses on its decision-making component that can calculate the optimal task assignment by taking into account the reconfiguration costs. This paper has three major contributions. Firstly, we study a context-aware scenario and derive the design requirements for a task assignment algorithm in MPMS. Secondly, using a graph-based system model, we proposed an A*-based task assignment algorithm that minimizes the system end-to-end delay while guaranteeing required system battery lifetime and availability. We introduce a set of node expansion rules and a pre-processing procedure to calculate the heuristic function (h(n)). Thirdly, we evaluate the algorithm performance with experiments and compare this A*-based algorithm with other heuristic approaches, e.g. greedy and bounded A*.

Emerging healthcare applications rely on personal mobile devices to monitor and transmit patient vital signs to hospital-backend servers for further analysis. However, these devices have limited resources that must be used optimally in order to meet the application user requirements (e.g. safety, usability, reliability, performance). This paper reports on a case study of a Chronic Obstructive Pulmonary Disease telemonitoring application delivered by the mobihealth system. This system relies on a commercial mobile device with multiple (wireless) Network Interfaces (NI). Our study focuses on how NI activation strategies affect the application end-to-end data delay (important in case of an emergency situation) and the energy consumption of the device (important for device sustainability while a patient is mobile). Our results show the trade-off between end-to-end delay and battery life-time achieved by various NI activation strategies, in combination with application-data flow adaptation for realtime and near real-time data transmission. For a given mobile device, our study shows an increase in battery life-time of 40- 90 %, traded against higher end-to-end data delay. The insights of our studies can be used for application-data flow adaptation aiming to increase battery life-time and device sustainability for mobile patients; which effectively increases the healthcare application usability.

Socio-technical developments in computing have resulted in the emergence of innovative mobile systems which exploit the information available on the Internet to optimize the performance of hosted applications and services. One of the challenges in the real-time critical mobile applications such as remote patient monitoring is to ensure optimal power usage. We consider herewith a case, where context-aware middleware hosted on the mobile device uses wireless networks availability data obtained from the remote QoS Information Service to selectively turn on the network interface and handover to the wireless access network which results in the optimal power consumption on the mobile device. We evaluate the power savings potential of the proposed concept using close-to-real life simulations and compare the results with the network selection mechanism which uses only information locally available on the mobile device. The results obtained from the simulations encourage applying the proposed concept in real operational remote patient monitoring systems.

The focus of existing virtual communities is centered on a particular product or social interaction and the role of mobile devices is restricted to exchange a limited amount of contents. Herewith we envisage that the upcoming virtual communities will exploit the potential of social interaction and context information to offer personalized services to its members and mobile devices will play a significant role in this process. As a step towards this direction, in this paper we propose a business model for the mobile virtual communities in which the mobile device takes on the role of a content producer and content consumer. Though there are a number of research issues which need to be addressed to realize such virtual communities, in this paper we focus on the service requirements, architecture and open source software implementation of a technical platform for the content producer and consumer mobile devices.

We are investigating the use of body area networks (BANs), wearable sensors and wireless communications for measuring, processing, transmission, interpretation and display of biosignals. The goal is to provide telemonitoring and teletreatment services for patients. The remote health professional can view a multimedia display which includes graphical and numerical representation of patientspsila biosignals. Addition of feedback-control enables teletreatment services; teletreatment can be delivered to the patient via multiple modalities including tactile, text, auditory and visual. We describe the health BAN and a generic mobile health service platform and two context aware applications. The epilepsy application illustrates processing and interpretation of multi-source, multimedia BAN data. The chronic pain application illustrates multi-modal feedback and treatment, with patients able to view their own biosignals on their handheld device.

With the advent of ubiquitous computing, a user is surrounded by a variety of devices including tiny sensor nodes, handheld mobile devices and powerful computers as well as diverse communication networks. In this networked society, the role of a human being is evolving from the data consumer to the data producer. In these changing circumstances, pipelined processing finds applications where the data obtained from the human producer needs to be processed and interpreted in real-time. For example, in an M- Health system, the vital signs acquired from the patient are processed in the pipelined fashion. This paper proposes a genetic algorithm (GA) based approach for the optimal assignment of pipelined processing tasks onto a chain of networked devices that minimizes total end-to-end processing delay considering knowledge about the communication and computation resources as the context information. Although some existing graph-based algorithms can solve this problem in polynomial time, we expect that GA can take less computational time and requires less memory while providing a reasonably good assignment. We compare the performance of GA approach with the graph-based approaches. It is observed that when the number of devices and the number of processing tasks are large, the GA approach performs better in terms of the satisfactory quality of the obtained sub-optimal solution considering the advantage of reduced computational time.

Nowadays, a variety of handheld mobile devices are capable of connecting to the Internet using multiple network interfaces. This is referred to as multi-homing. In addition to this, enriched computation resources allow them to host nomadic mobile services and provide these services to the clients located anywhere in the Internet. Potential advantages of multi-homing for nomadic mobile services typically includes: an increased service availability and improved service performance. However, applications running on the handheld mobile devices either do not, or cannot, exploit multi-homing. In this paper we address the problem of providing infrastructural support to the nomadic mobile services that can fully exploit multi-homing. To this end we propose to incorporate multi-homing functionality and support in a middleware layer to reduce the complexity of the design and maintenance of these services. The proposed solution uses a context-aware computing approach to realize this functionality. We report the initial experimental results in the remote tele-monitoring of a patient equipped with a Body Area Network.

This paper addresses how quality of context (QoC) can be used to optimize end-to-end mobile healthcare (m-health) data delivery services in the presence of alternative delivery paths, which is quite common in a pervasive computing and communication environment. We propose min-max-plus based algebraic QoC models for computing the quality of delivered data impeded by the QoS of the resources along the alternative delivery paths. The constructed algebraic structures in those models directly relate to the resource configurations represented as directed graphs. The properties of the applied algebras correspond to the properties of the operations of the addressed QoS dimensions. To rank all the possible resource configurations and therewith select from those the most optimal one(s) we introduce a workflow management metric based on the quality dimensions like freshness and availability. We focus on the pre-establishment phase of m-health data delivery services; dynamic QoC issues existing during service execution are not considered

Today, telematics services are often compositions of different chargeable service components offered by different service providers. To enhance component-based accounting and charging, the service composition information is used to match with the corresponding charging structure of a service session. This enables the sharing of revenues among the service providers, and calculation of the total cost for the end-user. When multiple independent service providers are involved, it is a great challenge to apply interim accounting and charging during a service session in order to minimize financial risks between business partners. Another interesting development is the trend towards outsourcing accounting and charging processes to specialized business partners. This requires a decoupling between provisioning and accounting and charging processes. In this paper, we propose a comprehensive component-based accounting and charging architecture to support service session provisioning across multiple domains. The architecture, modeled in UML, incorporates an interim accounting and charging mechanism to enable the processing and exchange of accounting information needed to update intermediate charges for separate service components and the user's credit, even during the service provisioning phase.

In this paper, we report on the results of performance measurements for an integrated B-ISDN/intelligent network signaling network. The specification, implementation and realization as well as the performance measurements are all part of a European project named INSIGNIA. The results reported in this paper pertain to the signaling system built in the second trial of the project and pertain to one of the demonstrated services: the interactive multimedia retrieval service. In the performance analysis, the results are presented with different 'perspectives', each serving a different purpose. For instance, the 'message processing' perspective which helps implementers to optimize the systems and provides parameter values for performance modeling. The 'service procedure' perspective takes a more service oriented view which helps in evaluating the service and gives a clear view of the contribution of each physical entity involved in the provisioning of the service.