ArticleBiometrics2016
Movement prediction using accelerometers in a human population.
Article in Biometrics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed, 22 citations in OpenAlex.
- Assessment of gait and posture characteristics using a smartphone wearable system for persons with osteoporosis with and without falls.Scientific reports · 2023Article
- Adaptive empirical pattern transformation (ADEPT) with application to walking stride segmentation.Biostatistics (Oxford, England) · 2021Article
- Classification of human physical activity based on raw accelerometry data via spherical coordinate transformation.Statistics in medicine · 2020Article
- Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery.Journal of healthcare informatics research · 2020Article
- Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data.Sensors (Basel, Switzerland) · 2020Article
- Accelerometry data in health research: challenges and opportunities.Statistics in biosciences · 2019Article
- On Placement, Location and Orientation of Wrist-Worn Tri-Axial Accelerometers during Free-Living Measurements.Sensors (Basel, Switzerland) · 2019Article
- Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data.Statistics in biosciences · 2019Article
- Validation of Gait Characteristics Extracted From Raw Accelerometry During Walking Against Measures of Physical Function, Mobility, Fatigability, and Fitness.The journals of gerontology. Series A, Biological sciences and medical sciences · 2018Observational
- Stride variability measures derived from wrist- and hip-worn accelerometers.Gait & posture · 2017Article
- Automatic car driving detection using raw accelerometry data.Physiological measurement · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
We introduce statistical methods for predicting the types of human activity at sub-second resolution using triaxial accelerometry data. The major innovation is that we use labeled activity data from some subjects to predict the activity labels of other subjects. To achieve this, we normalize the data across subjects by matching the standing up and lying down portions of triaxial accelerometry data. This is necessary to account for differences between the variability in the position of the device relative to gravity, which are induced by body shape and size as well as by the ambiguous definition of device placement. We also normalize the data at the device level to ensure that the magnitude of the signal at rest is similar across devices. After normalization we use overlapping movelets (segments of triaxial accelerometry time series) extracted from some of the subjects to predict the movement type of the other subjects. The problem was motivated by and is applied to a laboratory study of 20 older participants who performed different activities while wearing accelerometers at the hip. Prediction results based on other people's labeled dictionaries of activity performed almost as well as those obtained using their own labeled dictionaries. These findings indicate that prediction of activity types for data collected during natural activities of daily living may actually be possible.
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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.