We report in this paper on a novel modeling and simulation approach to predict orientation errors of garment-attached sensors and their effect on posture classification. Such errors occur frequently in smart garment implementations and can reduce sensor information quality for movement and posture recognition. A kinematic model of the human upper-body was developed to simulate upper limb postures and the output of virtual 3D acceleration sensors. The model was enhanced with a statistical approximation of garment-related orientation errors. We derived this model from acceleration sensor deviations between skin- and garment-attached units. The feasibility of our body model and the garment-attached sensor deviation was validated in experimental data. We compared the classification performance for ten posture types that are frequently used in shoulder rehabilitation. In a validation set of 7 participants we observed similar classifier confusions and a relative error of 2.6% (SD:±3.2%) between simulation and experiment. We utilized the model to estimate classification performance for further simulated textile error distributions. Our simulations showed that classification performance depends on low deviations of an acceleration sensor at the lower arm, while a sensor at the upper arm was less critical. Moreover, we included magnetic field sensors in our simulation. With the help of this additional modality our posture classification performance increased by 18%. We conclude that simulation of skin- and garment-attached sensors is a feasible approach to expedite design and development process of smart garments.

User needs and technology availability drive the introduction of wireless sensing applications in clinical environments. While these applications have the potential to improve efficiency and quality of care, very little is known about their performance during day-to-day use at the hospital. In this work, we use data from a deployment of a 802.15.4-based wireless sensor network at the Emergency Room of the Johns Hopkins hospital to answer these questions. Specifically, over a period of ten days we deployed a system of wireless vital signs monitors that measure the heart rate and blood oxygen levels of Emergency Room patients. During this time we collected statistics about the network's RF links, the performance of its tree routing protocol, and its end-to-end reliability. We find that the hospital environment we tested has considerably higher radio noise levels across multiple frequency channels and more bursty links compared to other indoor environments. Nonetheless, the routing protocol we use finds high quality links and the end-to-end packet reception ratio is above 99.9%. Taken as a whole, these preliminary results suggest that despite the challenges that clinical environments pose, wireless medical sensing applications can perform well in these conditions.

Emerging context aware applications call for new networking technologies to enable rapid development of integrated solutions that gather, process and store context from a diverse set of sensors. We examine Bluetooth in the context of enabling emerging classes of context aware applications, such as healthcare, fitness, gaming, etc., using off-the-shelf (OTS) products. While Bluetooth is widely used today, its applicability to this new class of applications is not widely understood and applications that use Bluetooth could suffer from inconsistent usages and poor performance as a result. We investigate and report the challenges of implementing solutions that use software and OTS products based on existing Bluetooth standards. We also present performance analysis through experimentations to highlight some of the issues discussed in the paper. Based on our experience from building Bluetooth based sensing solutions, we make informed recommendations for modifications in the Bluetooth standard and highlight areas where new standards are required.

Athletes in any sports can greatly benefit from feedback systems for improving the quality of their training. In this paper, we present a golf swing training system which incorporates wearable motion sensors to obtain inertial information and provide feedback on the quality of movements. The sensors are placed on a golf club and athlete's body at positions which capture the unique movements of a golf swing. We introduce a quantitative model which takes into consideration signal processing techniques on the collected data and quantifies the correctness of the performed actions. We evaluate the effectiveness of our framework on data obtained from four subjects and discuss ongoing research.