We introduce IMU over Continuous Coordinates (IMUCoCo), a novel framework that maps signals from a variable number of IMUs placed on the body surface into a unified feature space based on their spatial coordinates.These features can be plugged into downstream models for pose estimation and activity recognition.Our evaluations demonstrate that IMUCoCo supports accurate pose estimation in a wide range of typical and atypical sensor placements.Overall, IMUCoCo supports significantly more flexible use of IMUs for motion sensing than the state-of-the-art, allowing users to place their sensors-laden devices according to their needs and preferences.The framework also supports the ability to change device locations depending on the context and suggests placement depending on the use case.

To address privacy concerns with the Internet of Things (IoT) devices, researchers have proposed enhancements in data collection transparency and user control. However, managing privacy preferences for shared devices with multiple stakeholders remains challenging. We introduced ThingPoll, a system that helps users negotiate privacy configurations for IoT devices in shared settings. We designed ThingPoll by observing twelve participants verbally negotiating privacy preferences, from which we identified potentially successful and inefficient negotiation patterns. ThingPoll bootstraps a preference model from a custom crowdsourced privacy preferences dataset. During negotiations, ThingPoll strategically scaffolds the process by eliciting users’ privacy preferences, providing helpful contexts, and suggesting feasible configuration options. We evaluated ThingPoll with 30 participants negotiating the privacy settings of 4 devices. Using ThingPoll, participants reached an agreement in 97.5% of scenarios within an average of 3.27 minutes. Participants reported high overall satisfaction of 83.3% with ThingPoll as compared to baseline approaches.

Deep supervised semantic change detection (SCD) methods require extensive annotated data, which is challenging and expensive, while the abundance of unlabeled multi-temporal remote sensing images offers a potential alternative. In this paper, we attempt to transfer semi-supervised methods for semantic segmentation to the task of remote sensing semantic change detection, leveraging information from unlabeled dual-temporal images to explore the feasibility of semi-supervised detection methods for SCD tasks. In this work, we conduct semi-supervised semantic change detection guided by the strong-weak consistency principle. Our experiments conducted on publicly available SCD datasets reveal that a more extensive perturbation space exhibits limited generalization capabilities in our scenario. More future work can be inspired by our experimental results and the transfer of UniMatch.

Acoustic sensing has become increasingly prevalent for mobile and ambient devices for applications such as human activity recognition, health monitoring, and environmental sensing. These approaches develop audio featurization techniques to enable machine learning-based inferences while offering privacy by preventing speech reconstruction. However, recent work [2] has shown that such methods are still vulnerable to speech content recovery when fine-tuned automatic speech recognition (ASR) models are applied. Here, we demonstrate the broad applicability of on-device speech filtering using a detect-and-remove method for acoustic sensing tasks, significantly reducing privacy risks in revealing speech content. Additionally, we introduce an interactive tool to experiment with audio featurization methods, aiding the development of privacy-preserving applications.

Smart devices have entered all spheres of modern living, from monitoring the steps we walk to managing refrigerator inventory, ushering in the dawn of a new urban experience. The kitchen is the heart of the home; a place to share, care for and nurture the family unit, but also a place seeing the greatest impact from the introduction of smart devices. The smart sensing and remote control capability of smart appliances have enabled great physical convenience for users but have had less impact on cognitive conveniences. While such devices can sensewhatthey are working with, they fail to understandwhothey are working for, leaving much of the burden of trivial planning and decision making to humans with less personalized services. Hence, we introduce TupperwareEarth, a knowledge-based ontological semantic network for the “Internet of Kitchen Things” with the aim of reducing physical as well as cognitive loads of humans in cooking tasks. Also, we present a testbed for exploring kitchen innovation and validating the effectiveness of TupperwareEarth that combines intelligent kitchen storage containers, Smart Tupperware, and existing smart kitchen appliances through an Internet of Things network and a user-friendly front-end interface, Tuppy. Using this testbed, the quantitative user studies show a 33% reduction in average food preparation time and qualitative user surveys show that 75% of the users observed a significant reduction in cognitive loads, thereby validating the cognitive conveniences granted by TupperwareEarth.