Microservice-based systems enable the independent development, deployment, and scalability for separate system components of enterprise applications. A significant aspect during development is the microservice integration in frontends of web, mobile, and desktop applications. One challenge here is the selection of an adequate frontend architecture as well as suitable patterns that satisfy the application requirements. This paper analyses available strategies for organizing and implementing microservices frontends. These approaches are then evaluated based on a quality model and various prototypes of the same application implemented using the distinct approaches. The results of this analysis are generalized to a guideline that supports the selection of a suitable architecture.

In this paper, we investigate monitoring of kinematic changes evoked by fatigue in running using wearable technology. Movement data were recorded with ETHOS devices. ETHOS is the ETH Orientation Sensor, a customized inertial measurement unit for unconstrained monitoring of human movement. We perform two real-world experiments, in which 21 runners of different skill levels participated. The real-world experiments capture two exhausting 45-min runs: one on a treadmill and one on a conventional outdoor track. We describe and evaluate algorithms to extract kinematic parameters from the sensor data. We identified parameters that change with fatigue for all runners, ones that change for runners of distinct skill levels, and ones that are dependent on an individual's running technique. Overall, we found that observations from treadmill running are not always generalizable to outdoor running. We, thus, argue for using wearable technology to provide athletes and trainers with continuous, quantitative objective measurements of running technique. These could be used to further gain insight into the complex relationship of running kinematics, injury risk, fatigue, and running economy.

Injuries in running are often provoked by fatigue or improper technique, which are both reflected in the runner's kinematics. State of the art research on kinematics in sports is using optical motion capture systems that are inaccessible to most athletes. This paper demonstrates the potential of wearable sensors for runners' kinematics analysis. We present a user study including 21 subjects of different running experience that performed an exhausting run on a conventional outside track wearing ETHOS units. For performance level assessment, training assistance, and fatigue monitoring we extracted the foot contact duration, the foot strike type, and the heel lift as kinematic parameters. A questionnaire revealed that subjects perceived the sensors as comfortable to wear and would use them on a regular basis. We concluded that wearable sensors provide a valuable tool for runners, from beginners to experts, for running technique assessment.

A fundamental challenge limiting information quality obtained from smart sensing garments is the influence of textile movement relative to limbs. We present and validate a comprehensive modeling and simulation framework to predict recognition performance in casual loose-fitting garments. A statistical posture and wrinkle-modeling approach is introduced to simulate sensor orientation errors pertained to local garment wrinkles. A metric was derived to assess fitting, the body-garment mobility. We validated our approach by analyzing simulations of shoulder and elbow rehabilitation postures with respect to experimental data using actual casual garments. Results confirmed congruent performance trends with estimation errors below 4% for all study participants. Our approach allows to estimate the impact of fitting before implementing a garment and performing evaluation studies with it. These simulations revealed critical design parameters for garment prototyping, related to performed body posture, utilized sensing modalities, and garment fitting. We concluded that our modeling approach can substantially expedite design and development of smart garments through early-stage performance analysis.

This paper introduces a sensing garment to support posture coaching in children. The system measures back bending postures using acceleration sensors embedded in the garment. We present a sensing garment architecture and the evaluation of garments of different sizes in a study with 21 children. A vision-based reference system was used to evaluate sensor positions and measurement accuracy for 54 back bending postures and related head positions. Then, we asked eight physiotherapists to rate the children's back postures in this study. Ratings of experts correlated significantly with the back bending measurements obtained from the garment. The garment enables an objective assessment of back postures and could form the basis of a system that provides coaching feedback to improve postural control in children.