Prolonged loneliness in older adults increases the risk of dementia, cardiovascular diseases, and premature death. However, timely detection and behavioral pattern analysis are challenging due to the slow progression of loneliness and diverse individual lifestyles. To address these barriers, we propose SMILE, an end-to-end framework for sensor-driven detection and counterfactual pattern analysis of loneliness. Our approach uses non-intrusive sensors to passively observe behavior and build a personalized profile over time. When SMILE detects meaningful changes in activity patterns, it employs generative models to produce counterfactual sensor profiles—synthetic representations of behavior predicted to result in lower loneliness scores. Suggested pattern changes are identified as the difference between observed and counterfactual profiles, highlighting the behaviors most associated with loneliness. We conducted a 6-month feasibility study with 18 participants from the United States and 15 from Japan to develop and evaluate SMILE. Our framework integrates a Time-series Transformer (TST) model for loneliness detection, which achieved 96.25% accuracy, and a diffusion model for generating behavioral explanations, which outperformed a baseline by up to 76.69%. These findings highlight the potential of SMILE to support sensor-driven, personalized loneliness detection and behavioral explanation in aging populations.

Accurate measurement of propeller rotation speed is essential for stable drone control. While various sensors have been employed for this purpose, their cost, size, and additional weight pose challenges, particularly for small indoor drones. This paper presents a method for estimating propeller rotational speed using Wi-Fi Channel State Information (CSI), which captures fine-grained wireless signal fluctuations and is obtainable on many commercial Wi-Fi devices. The proposed approach applies resampling and short-time Fourier transform (STFT) to the amplitude component of CSI to estimate propeller rotational speed. In this study, experiments were conducted using both fixed propellers and a hovering drone in indoor environments. The results revealed that antenna placement and polarization alignment significantly affect the accuracy of rotation speed estimation. With a tilted antenna configuration, the estimation error rate remained below 3.16% in all STFT windows (each 0.5 -second estimation interval). Furthermore, even with a horizontally placed antenna, selecting dominant subcarriers through signal processing reduced the estimation error rate to below 4% in over 95% of STFT windows. In addition, by evaluating three distances ($2 \mathrm{m}, 4 \mathrm{m}$, and 8 m) and varying the antenna elevation from 0° to 90°, we identified optimal orientations that reduced RMSE to as low as 23.60 RPM, achieving a mean error of only 1.24% at 8 m. These findings demonstrate the feasibility of noncontact propeller rotational speed estimation using Wi-Fi CSI and suggest its potential for real-time control applications.

Recently, there is growing interest in Wi-Fi CSI-based activity recognition due to its low setup costs. However, accurate CSI-based activity recognition depends on the number of Wi-Fi devices, which is suboptimal cost-wise. Our proposed solution is to use low-power backscatter tags within a Wi-Fi CSI sensing system, collecting multiple CSI data streams from various Wi-Fi channels. This enhances the number of observations without the need to install a large number of Wi-Fi devices. We evaluated classification of five daily activities using traditional Wi-Fi CSI and backscattered CSI, finding an accuracy improvement by combining them.

We present Sonoloc, a mobile app and system that allows a set of co-located commodity smart devices to determine their relative positions without local infrastructure. Sonoloc enables users to address each other based on their relative positions at events like meetings, talks, or conferences. This capability can, for instance, aid spontaneous communication among users based on their relative position (e.g., in a given section of a room, at the same table, or in a given seat), facilitate interaction between speaker and audience in a lecture hall, and enable the distribution of materials, crowdsensing, and feedback collection based on users' location. Sonoloc can position any number of devices within acoustic range with a constant number of chirps emitted by a self-organized subset of devices. Our experimental evaluation shows that the system can locate up to hundreds of devices with an accuracy of tens of centimeters using up to 15 audio chirps emitted by dynamically selected devices, in actual rooms and despite substantial background noise.

Mobile social apps provide sharing and networking opportunities based on a user's location, activity, and set of nearby users. A platform for these apps must meet a wide range of communication needs while ensuring users' control over their privacy. In this paper, we introduce EnCore, a mobile platform that builds on secure encounters between pairs of devices as a foundation for privacy-preserving communication. An encounter occurs whenever two devices are within Bluetooth radio range of each other, and generates a unique encounter ID and associated shared key. EnCore detects nearby users and resources, bootstraps named communication abstractions called events for groups of proximal users, and enables communication and sharing among event participants, while relying on existing network, storage and online social network services. At the same time, EnCore puts users in control of their privacy and the confidentiality of the information they share. Using an Android implementation of EnCore and an app for event-based communication and sharing, we evaluate EnCore's utility using a live testbed deployment with 35 users.