ArticleSensors (Basel, Switzerland)2019
Recognizing Physical Activity of Older People from Wearable Sensors and Inconsistent Data.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Technological applications to enhance independence in daily activities for older adults: a systematic review.Frontiers in public health · 2024Pooled it
- Deep neural networks for wearable sensor-based activity recognition in Parkinson's disease: investigating generalizability and model complexity.Biomedical engineering online · 2024Article
- Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
- Human Activity Recognition Data Analysis: History, Evolutions, and New Trends.Sensors (Basel, Switzerland) · 2022Review
- Article
- Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN.Sensors (Basel, Switzerland) · 2021Article
- Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification.Sensors (Basel, Switzerland) · 2021Article
- Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear.Entropy (Basel, Switzerland) · 2021Article
- Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone.Sensors (Basel, Switzerland) · 2020Article
- Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning.Sensors (Basel, Switzerland) · 2020Article
- Deep CNN Sparse Coding for Real Time Inhaler Sounds Classification.Sensors (Basel, Switzerland) · 2020Article
- Two-Stage Latent Dynamics Modeling and Filtering for Characterizing Individual Walking and Running Patterns with Smartphone Sensors.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative.
Indexed as
Identifiers
What OpenQuestion holds
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.