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Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions ("I don't know") when the ML model's confidence is low. Evaluated on real-world data from eight smart homes, our method outperforms baseline methods in recall and other classification metrics while maintaining the lowest abstention proportion and interval width. A survey of 42 nurses confirms that our system's outputs are valuable for guiding clinical decision-making, underscoring their practical utility in improving informed decisions and effectively managing UTIs and other condition flare-ups in older adults.

Here's how I cut down my family's reliance on Alexa. Until recently, my smart home setup was in chaos. After years of testing, buying, and upgrading to the latest smart home gadgets in an attempt to make my life easier, it became a bloated mess that was actually making it more complicated. My Alexa, Google Home, and Apple Home apps were awash with dead devices, duplicates, and automations that simply didn't work. My Hue Bridge, trying desperately to tie it all together, was creaking at the seams.

Ambient sensor-based human activity recognition (HAR) in smart homes remains challenging due to the need for real-time inference, spatially grounded reasoning, and context-aware temporal modeling. Existing approaches often rely on pre-segmented, within-activity data and overlook the physical layout of the environment, limiting their robustness in continuous, real-world deployments. In this paper, we propose MARAuder's Map, a novel framework for real-time activity recognition from raw, unsegmented sensor streams. Our method projects sensor activations onto the physical floorplan to generate trajectory-aware, image-like sequences that capture the spatial flow of human movement. These representations are processed by a hybrid deep learning model that jointly captures spatial structure and temporal dependencies. To enhance temporal awareness, we introduce a learnable time embedding module that encodes contextual cues such as hour-of-day and day-of-week. Additionally, an attention-based encoder selectively focuses on informative segments within each observation window, enabling accurate recognition even under cross-activity transitions and temporal ambiguity. Extensive experiments on multiple real-world smart home datasets demonstrate that our method outperforms strong baselines, offering a practical solution for real-time HAR in ambient sensor environments.

Ikea's Smart Home Reset Goes Back to Basics Ikea's new 21-product series of bulbs, sensors, and remotes is dirt cheap, idiot-proof, Matter-ready, and designed to work with everything. But it's still years from the promised house of the future. That's what you might be thinking if you've been following smart-home tech for the past decade or, indeed, building out your own fortress of missed connections. The first Nest thermostat launched in 2011, Philips Hue in 2012, the Amazon Echo in 2014. But for anyone who has spent long nights scrolling through IoT troubleshooting forums since then, here's the latest: It's finally time for a do-over.

Abstract--The recognition of Activities of Daily Living (ADLs) from event-triggered ambient sensors is an essential task in Ambient Assisted Living, yet existing methods remain constrained by representation-level limitations. Sequence-based approaches preserve temporal order of sensor activations but are sensitive to noise and lack spatial awareness, while image-based approaches capture global patterns and implicit spatial correlations but compress fine-grained temporal dynamics and distort sensor layouts. Na ıve fusion (e.g., feature concatenation) fail to enforce alignment between sequence-and image-based representation views, under-utilizing their complementary strengths. We propose C ontrastive A lignment for ADL R ecognition from E vent-Triggered Sensor Streams (CARE), an end-to-end framework that jointly optimizes representation learning via Sequence-Image Contrastive Alignment (SICA) and classification via cross-entropy, ensuring both cross-representation alignment and task-specific discriminability. CARE integrates (i) time-aware, noise-resilient sequence encoding with (ii) spatially-informed and frequency-sensitive image representations, and employs (iii) a joint contrastive-classification objective for end-to-end learning of aligned and discriminative embeddings. Evaluated on three CASAS datasets, CARE achieves state-of-the-art performance (89.8% on Milan, 88.9% on Cairo, and 73.3% on Kyoto7) and demonstrates robustness to sensor malfunctions and layout variability, highlighting its potential for reliable ADL recognition in smart homes. Global increases in life expectancy are leading to aging societies, with a rising number of older adults who require continuous support from healthcare providers and their family members [30]. However, given the critical shortage of healthcare personnel, it is essential to support older adults in maintaining independence for as long as possible. These functional abilities often decline with aging, and can be further deteriorated by aging-related chronic conditions [32]. Ambient Assisted Living (AAL) technologies have emerged to support ADL performance, encompassing systems for activity recognition, anomaly detection, and personalized prompting.

If done right, the AI revolution will free us from their merciless tyranny. Instead, you activate various wearables embedded in your body and have a series of conversations with inanimate objects. You make -style gestures in the air. Things power on, tasks get done, the day begins. It turns out you have no need for a smartphone at all.

When you purchase through links in our articles, we may earn a small commission. Google's Gemini lets you chat with your smart home With help from the revamped Google Home app, Gemini for Home promises to be the eyes and ears of your household. "Hey Google, set bedroom lamp to 50 percent." Such stilted voice commands have been the stuff of smart home for years, but with Gemini for Home, Google is promising a smart home you can have an actual conversation with. That idea--of a smart home that understands the big picture and can act with context in mind--underpins Google's ambitious Gemini for Home plans, which it's rolling out today following months of slow buildup.

In a new apartment in Tel Aviv, the internet-connected lights go out. The smart shutters covering its four living room and kitchen windows start to roll up simultaneously. And a connected boiler is remotely turned on, ready to start warming up the stylish flat. The apartment's residents didn't trigger any of these actions. They didn't put their smart devices on a schedule.

A smart speaker makes for an easy first step into smart home technology. Before you kit out your house with thousands of dollars of lighting and security upgrades, you can familiarize yourself with voice-assistant technology while enjoying music, podcasts, and news in a hands-free home environment. Here are our top picks in several categories. If you want information about smart speakers in addition to our top recommendations, scroll down the page to read our in-depth buyers' guide. Alexa is the most popular voice assistant, and the 2024 edition of the Echo Pop is the best value in Amazon's smart speaker lineup. While it's not a true smart display, it is equipped with a touchscreen that can display the time, date, weather conditions, and other information. It can also show album art while streaming music (not that we recommend this speaker for that task).