Indoor carbon dioxide (CO2) can rapidly accumulate to form invisible pollution hotspots, posing significant health risks due to its odorless and colorless nature. Despite growing interest in wearable or stationary sensors for pollutant detection, effectively visualizing CO2 levels and engaging individuals remains an ongoing challenge. In this paper, we develop a portable wrist-sized pollution sensor that detects CO2 in real time at any indoor location and reveals CO2 bubbles by highlighting sudden spikes. In order to promote better ventilation habits and user awareness, we also develop a smartphone-based augmented reality (AR) game for users to locate and disperse these high-CO2 zones. A user study with 35 participants demonstrated increased engagement and heightened understanding of CO2’s health impacts. Our system’s usability evaluations yielded a median score of 1.88, indicating its strong practicality.

Continuous detection of human activities and presence is essential for developing a pervasive interactive smart space. Existing literature lacks robust wireless sensing mechanisms capable of continuously monitoring multiple users’ activities without prior knowledge of the environment. Developing such a mechanism requires simultaneous localization and tracking of multiple subjects. In addition, it requires identifying their activities at various scales, some being macro-scale activities like walking, squats, etc., while others are micro-scale activities like typing or sitting, etc. In this paper, we develop a holistic system called MARS using a single Commercial off-the-shelf (COTS) Millimeter Wave (mmWave) radar, which employs an intelligent model to sense both macro and micro activities. In addition, it uses a dynamic spatial time-sharing approach to sense different subjects simultaneously. A thorough evaluation of MARS shows that it can infer activities continuously with an accuracy of > 93% and an average response time of ≈ 2 sec, with 5 subjects and 19 different activities.

Developing robust wireless sensing mechanisms for continuously monitoring human activities and presence is crucial for creating pervasive interactive intelligent spaces. The existing literature lacks solutions that continuously monitor multiple users’ activities without prior knowledge of the environment. This requires simultaneous localization and tracking of multiple subjects and identifying their activities at various scales, including macro-scale activities like walking and squats and micro-scale activities like typing or sitting. In this demo, we present MARS , a holistic system using a single off-the-shelf mmWave radar. MARS employs an intelligent model to sense both macro and micro activities and uses a dynamic spatial time-sharing approach to sense different subjects simultaneously. Our thorough evaluation demonstrates that MARS can continuously infer activities with over 93% accuracy and an average response time of approximately 2 seconds, even with five subjects performing 19 different activities.

In recent years, indoor air pollution has posed a significant threat to our society, claiming over 3.2 million lives annually. Developing nations, such as India, are most affected since lack of knowledge, inadequate regulation, and outdoor air pollution lead to severe daily exposure to pollutants. However, only a limited number of studies have attempted to understand how indoor air pollution affects developing countries like India. To address this gap, we present spatiotemporal measurements of air quality from 30 indoor sites over six months during summer and winter seasons. The sites are geographically located across four regions of type: rural, suburban, and urban, covering the typical low to middle-income population in India. The dataset contains various types of indoor environments (e.g., studio apartments, classrooms, research laboratories, food canteens, and residential households), and can provide the basis for data-driven learning model research aimed at coping with unique pollution patterns in developing countries. This unique dataset demands advanced data cleaning and imputation techniques for handling missing data due to power failure or network outages during data collection. Furthermore, through a simple speech-to-text application, we provide real-time indoor activity labels annotated by occupants. Therefore, environmentalists and ML enthusiasts can utilize this dataset to understand the complex patterns of the pollutants under different indoor activities, identify recurring sources of pollution, forecast exposure, improve floor plans and room structures of modern indoor designs, develop pollution-aware recommender systems, etc.

Rotational movement is important in many applications, yet has been under-explored. In this paper, we explore the feasibility of using a single RFID reader antenna to simultaneously sense rotation and translation movement (i.e., rotation axis, rotation speed, and translation speed). We exploit the polarization in RFID to enable motion sensing. We develop an analytical model to capture the impact of polarization on the received signal and an optimization framework to incorporate the model to estimate the movement. We implement our system, Tag-based Inertial Measurement Unit (TIMU), and demonstrate its effectiveness through an extensive evaluation. To our knowledge, this is the first system that tracks general motion using a single RFID reader antenna.

Passive radio-frequency identification (RFID) tags are attractive because they are low cost, battery-free, and easy to deploy. This technology is traditionally being used to identify tags attached to the objects. In this paper, we explore the feasibility of turning passive RFID tags into battery-free temperature sensors. The impedance of the RFID tag changes with the temperature and this change will be manifested in the reflected signal from the tag. This opens up an opportunity to realize battery-free temperature sensing using a passive RFID tag with already deployed Commercial Off-the-Shelf (COTS) RFID reader-antenna infrastructure in supply chain management or inventory tracking. However, it is challenging to achieve high accuracy and robustness against the changes in the environment. To address these challenges, we first develop a detailed analytical model to capture the impact of temperature change on the tag impedance and the resulting phase of the reflected signal. We then build a system that uses a pair of tags, which respond differently to the temperature change to cancel out other environmental impacts. Using extensive evaluation, we show our model is accurate and our system can estimate the temperature within a 2.9 degree centigrade median error and support a normal read range of 3.5 m in an environment-independent manner.

Smart speakers allow users to interact with home appliances using voice commands and are becoming increasingly popular. While voice-based interface is intuitive, it is insufficient in many scenarios, such as in noisy or quiet environments, for users with language barriers, or in applications that require continuous motion tracking. Motion-based control is attractive and complementary to existing voice-based control. However, accurate and reliable room-scale motion tracking poses a significant challenge due to low SNR, interference, and varying mobility. To this end, we develop a novel recurrent neural network (RNN) based system that uses speakers and microphones to realize accurate room-scale tracking. Our system jointly estimates the propagation distance and angle-of-arrival (AoA) of signals reflected by the hand, based on AoA-distance profiles generated by 2D MUSIC. We design a series of techniques to significantly enhance the profile quality under low SNR. We feed the profiles in a recent history to our RNN to estimate the distance and AoA. In this way, we can exploit the temporal structure among consecutive profiles to remove the impact of noise, interference and mobility. Using extensive evaluation, we show our system achieves 1.2--3.7~cm error within 4.5~m range, supports tracking multiple users, and is robust against ambient sound. To our knowledge, this is the first acoustic device-free room-scale tracking system.

In today's always-connected world, we receive a large number of notifications on our mobile devices. These notifications cause interruptions, stress, and even impact users' lifestyle. To understand how users respond to notifications, we develop an application that monitors various features (e.g., importance) of the notifications, users' actions, and the level of users' engagement with the notifications. We recruit 30 users to use the application and monitor over 30 days, and subsequently find that 20% to 50% of the notifications generally get ignored by the users. In addition, we also solicit explicit feedback about the importance of notifications from 12 users over 14 days and identify the relation between perceived importance and users' engagement level. Based on this study, we identify the key characteristics of notifications and users' engagement, which is further substantiated by an onfine survey of 400+ users. In addition, we develop a notification manager that includes a machine learning based prediction model and that shows only the important notifications and delays the unimportant notifications. Our experimental results show that our notification manager automatically assesses the importance of notifications with more than 87% accuracy. We believe this work is a promising step toward intelligent personal assistant that manages notifications.

In this paper, we design and develop RIO, a novel battery-free touch sensing user interface (UI) primitive for future IoT and smart spaces. RIO enables UIs to be constructed using off-the-shelf RFID readers and tags, and provides a unique approach to designing smart IoT spaces. With RIO, any surface can be turned into a touch-aware surface by simply attaching RFID tags to them. RIO also supports custom-designed RFID tags, and thus allows specially customized UIs to be easily deployed into a real-world environment. RIO is built using the technique of impedance tracking: when a human finger touches the surface of an RFID tag, the impedance of the antenna changes. This change manifests as a change in the phase of the RFID backscattered signal, and is used by RIO to track fine-grained touch movement over both off-the shelf and custom built tags. We study this impedance behavior in-depth and show how RIO is a reliable UI primitive that is robust even within a multi-tag environment. We leverage this primitive to build a prototype of RIO that can continuously locate a finger during a swipe movement to within 3 mm of its actual position. We also show how custom-design RFID tags can be built and used with RIO, and provide two example applications that demonstrate its real-world use.

This paper explores the feasibility of automatically extracting passwords from a user's daily activity logs, such as her Facebook activity, phone activity etc. As an example, a smartphone might ask the user: "Today morning from whom did you receive an SMS?" In this paper, we observe that infrequent activities (i.e., outliers) can be memorable and unpredictable. Building on this observation, we have developed an end to end system ActivPass and experimented with 70 users. With activity logs from Facebook, browsing history, call logs, and SMSs, the system achieves 95% success (authenticates legitimate users) and is compromised in 5.5% cases (authenticates impostors). While this level of security is obviously inadequate for serious authentication systems, certain practices such as password sharing can immediately be thwarted from the dynamic nature of passwords. With security improvements in the future, activity-based authentication could fill in for the inadequacies in today's password-based systems.