Continuous monitoring of human vital signs using non-contact mmWave radars is attractive due to their ability to penetrate garments and operate under different lighting conditions. Unfortunately, most prior research requires subjects to stay at a fixed distance from radar sensors and to remain still during monitoring. These restrictions limit the applications of radar vital sign monitoring in real life scenarios. In this article, we address these limitations and present Pi-ViMo, a non-contact P hysiology- i nspired Robust Vi tal Sign Mo nitoring system, using mmWave radars. We first derive a multi-scattering point model for the human body, and introduce a coherent combining of multiple scatterings to enhance the quality of estimated chest-wall movements. It enables vital sign estimations of subjects at any location in a radar’s field of view (FoV). We then propose a template matching method to extract human vital signs by adopting physical models of respiration and cardiac activities. The proposed method is capable to separate respiration and heartbeat in the presence of micro-level random body movements (RBM) when a subject is at any location within the field of view of a radar. Experiments in a radar testbed show average respiration rate errors of 6% and heart rate errors of 11.9% for the stationary subjects, and average errors of 13.5% for respiration rate and 13.6% for heart rate for subjects under different RBMs.

Recommender systems relieve users from cognitive overloading by predicting preferred items for users. Due to the complexity of interactions between users and items, graph neural networks (GNN) use graph structures to effectively model user–item interactions. However, existing GNN approaches have the following limitations: (1) User reviews are not adequately modeled in graphs. Therefore, user preferences and item properties that are described in user reviews are lost for modeling users and items; and (2) GNNs assume deterministic relations between users and items, which lack the stochastic modeling to estimate the uncertainties in neighbor relations. To mitigate the limitations, we build tripartite graphs to model user reviews as nodes that connect with users and items. We estimate neighbor relations with stochastic variables and propose a Bayesian graph attention network (i.e., ContGraph) to accurately predict user ratings. ContGraph incorporates the prior knowledge of user preferences to regularize the posterior inference of attention weights. Our experimental results show that ContGraph significantly outperforms 13 state-of-the-art models and improves the best performing baseline (i.e., ANR) by 5.23% on 25 datasets in the five-core version. Moreover, we show that correctly modeling the semantics of user reviews in graphs can help express the semantics of users and items.

Foldable handheld displays have the potential to offer a rich interaction space, particularly as they fold into a convex form factor, for collocated multi-user interactions. In this paper, we explore Tent mode, a convex configuration of a foldable device partitioned into a primary and a secondary display, as well as a tertiary, Edge display that sits at the intersection of the two. We specifically explore the design space for a wide range of scenarios, such as co-browsing a gallery or co-planning a trip. Through a first collection of interviews, end-users identified a suite of apps that could leverage Tent mode for multi-user interactions. Based on these results we propose an interaction design space that builds on unique Tent mode properties, such as folding, flattening or tilting the device, and the interplay between the three sub-displays. We examine how end-users exploit this rich interaction space when presented with a set of collaborative tasks through a user study, and elicit potential interaction techniques. We implemented these interaction techniques and report on the preliminary user feedback we collected. Finally, we discuss the design implications for collocated interaction in Tent mode configurations.

An important category of mobile crowdsensing applications involve collecting sensor measurements from mobile devices and querying mobile users for annotations to build machine learning models for inference and prediction. Trade-offs between inference performance and the costs of data acquisition (both unlabeled and labeled) are not yet well understood. In this paper, we develop, ALSense, a distributed active learning framework for mobile crowdsensing. The goal is to minimize prediction errors for classification-based mobile crowdsensing tasks subject to upload and query cost constraints. Novel stream-based active learning strategies are developed to orchestrate queries of annotation data and the upload of unlabeled data from mobile devices. We evaluate the effectiveness of ALSense through two applications that can benefit from mobile crowdsensing, namely, WiFi fingerprint-based indoor localization and IMU-based human activity recognition. Extensive experiments demonstrate that ALSense can indeed achieve higher classification accuracy given fixed data acquisition budgets for both applications.

Many infrastructure-free indoor positioning systems rely on fine-grained location-dependent fingerprints to train models for localization. The site survey process to collect fingerprints is laborious and is considered one of the major obstacles to deploying such systems. In this paper, we propose trajectory radio fingerprint (TuRF), a fast path-based fingerprint collection mechanism for site survey. We demonstrate the feasibility to collect fingerprints for indoor localization during walking along predefined paths. A step counter is utilized to accommodate the variations in walking speed. Approximate location labels inferred from the steps are then used to train a Gaussian Process regression model. Extensive experiments show that TuRF can significantly reduce the required time for site survey, without compromising the localization performance.

Location-based services have experienced substantial growth in the last decade. However, despite extensive research efforts, sub-meter location accuracy with low-cost infrastructure continues to be elusive. In this paper, we propose IDyLL -- an indoor localization system using inertial measurement units (IMU) and photodiode sensors on smartphones. Using a novel illumination peak detection algorithm, IDyLL augments IMU-based pedestrian dead reckoning with location fixes. We devise a robust particle filter framework to mitigate identity ambiguity due to the lack of communication capability of conventional luminaries and sensing errors. Experimental study using data collected from smartphones shows that IDyLL is able to achieve high localization accuracy at low costs. Mean location errors of 0.38 m, 0.42 m, and 0.74 m are reported from multiple walks in three buildings with different luminary arrangements, respectively.

Mobile participatory sensing (MPS), a paradigm that utilizes pervasive mobile devices to efficiently collect data, has gained much interest in a variety of applications in outdoor spaces. In this paper, we demonstrate the potential of MPS in indoor environments through one specific application - burned-out luminary detection. To mitigate the inherent inaccuracy of indoor positioning systems (IPS), we devise a Dynamic Time Warping-based approach and leverage majority votes across multiple runs of measurements. Experimental study using data collected from off-the-shelf smartphones and a basic IPS shows that the proposed approach can indeed detect the presence of burnout luminaries with high accuracy, and can significantly outperform a baseline method. Interestingly, we also demonstrate that the proposed mechanism can be leveraged to improve an existing luminary-assisted solution, and thus leads to zero-configuration and robust IPS.