An Online Unsupervised Dynamic Window Method to Track Repeating Patterns From Sensor Data.

Short bursts of repeating patterns [intervals of recurrence (IoR)] manifest themselves in many applications, such as in the time-series data captured from an athlete's movements using a wearable sensor while performing exercises. We present an efficient, online, one-pass, and real-time algorithm for finding and tracking IoR in a time-series data stream. We provide a detailed theoretical analysis of the behavior of any IoR and derive fundamental properties that can be used on real-world data streams. We show that why our method, unlike current state-of-the-art techniques, is robust to variations in repeats of the same pattern adjacent to each other. To evaluate our algorithm, we build a wearable device that runs our algorithm to conduct a user study. Our results show that our algorithm can detect intervals of repeating activities on edge devices with high accuracy (over 70% F1 -Score) and in a real-time environment with only a 1.5-s lag. Our experimental results from real-world datasets demonstrate that our approach outperforms state-of-the-art algorithms in both accuracy and robustness to variations of the signal of recurrence.

Measuring Mobility and Room Occupancy in Clinical Settings: System Development and Implementation.

BACKGROUND: The use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings. Such a streamlined data-driven approach can help in increasing the uptime of operating rooms and more broadly provide an improved understanding of facility utilization. OBJECTIVE: The aim of this study is to measure the accuracy of the system and algorithmically calculate measures of mobility and occupancy. METHODS: We developed a Bluetooth low energy, proximity-based localization system and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. In addition, we collected ground-truth data and used them to validate system performance and accuracy. A number of analyses were conducted to estimate how people move in the hospital and where they spend their time. RESULTS: Using ground-truth data, we estimated the accuracy of our system to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms in the hospital occupied by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering, we showed that the system could differentiate between staff and patients without training. CONCLUSIONS: Analysis of longitudinal, location-based data can offer rich operational insights into hospital efficiency. In particular, they allow quick and consistent assessment of new strategies and protocols and provide a quantitative way to measure their effectiveness.

Eyes-free Target Acquisition During Walking in Immersive Mixed Reality.

Reaching towards out-of-sight objects during walking is a common task in daily life, however the same task can be challenging when wearing immersive Head-Mounted Displays (HMD). In this paper, we investigate the effects of spatial reference frame, walking path curvature, and target placement relative to the body on user performance of manually acquiring out-of-sight targets located around their bodies, as they walk in a spatial-mapping Mixed Reality (MR) environment wearing an immersive HMD. We found that walking and increased path curvature negatively affected the overall spatial accuracy of the performance, and that the performance benefited more from using the torso as the reference frame than the head. We also found that targets placed at maximum reaching distance yielded less error in angular rotation and depth of the reaching arm. We discuss our findings with regard to human walking kinesthetics and the sensory integration in the peripersonal space during locomotion in immersive MR. We provide design guidelines for future immersive MR experience featuring spatial mapping and full-body motion tracking to provide better embodied experience.

Fully-Occluded Target Selection in Virtual Reality.

The presence of fully-occluded targets is common within virtual environments, ranging from a virtual object located behind a wall to a datapoint of interest hidden in a complex visualization. However, efficient input techniques for locating and selecting these targets are mostly underexplored in virtual reality (VR) systems. In this paper, we developed an initial set of seven techniques techniques for fully-occluded target selection in VR. We then evaluated their performance in a user study and derived a set of design implications for simple and more complex tasks from our results. Based on these insights, we refined the most promising techniques and conducted a second, more comprehensive user study. Our results show how factors, such as occlusion layers, target depths, object densities, and the estimation of target locations, can affect technique performance. Our findings from both studies and distilled recommendations can inform the design of future VR systems that offer selections for fully-occluded targets.