ArticleScientific data2023
A multi-sensor dataset with annotated activities of daily living recorded in a residential setting.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Sensor-Driven Deep Learning for Smart Home Intelligence: Signal Analysis, Multimodal Perception, and System-Level Applications.Sensors (Basel, Switzerland) · 2026Review
- IoTMindCare: An Integrative Reference Architecture for Safe and Personalized IoT-Based Depression Management.Sensors (Basel, Switzerland) · 2025Article
- A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults.Scientific data · 2025Article
- The power of progressive active learning in floorplan images for energy assessment.Scientific reports · 2023Article
- A multi-sensor dataset with annotated activities of daily living recorded in a residential setting.Scientific data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
SPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the 'SPHERE House' in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence. Trained annotators provided ground-truth labels annotating posture, ambulation, activity and location. This dataset is a valuable resource both within and outside the machine learning community, particularly in developing and evaluating algorithms for identifying activities of daily living from multi-modal sensor data in real-world environments. A subset of this dataset was released as a machine learning competition in association with the European Conference on Machine Learning (ECML-PKDD 2016).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.