ArticleSensors (Basel, Switzerland)2020
Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 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
9 citing papers in PubMed, 21 citations in OpenAlex.
- Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.Behavior therapy · 2026Observational
- Patient engagement in digital health: a preliminary observation on user-centred and stakeholder feedback.BMJ open · 2026Article
- Measuring Psychological Well-Being and Behaviors Using Smartphone-Based Digital Phenotyping: An Intensive Longitudinal Observational mHealth Pilot Study Embedded in a Prospective Cohort of Women.JMIR mHealth and uHealth · 2025Observational
- Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor.IEEE journal of translational engineering in health and medicine · 2024Article
- Accuracy Improvement of Vehicle Recognition by Using Smart Device Sensors.Sensors (Basel, Switzerland) · 2022Article
- Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data.Sensors (Basel, Switzerland) · 2022Article
- Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors.Sensors (Basel, Switzerland) · 2021Article
- A systematic review of smartphone-based human activity recognition methods for health research.NPJ digital medicine · 2021Review
- Correction: Huang, E.J., and Onnela, J.P. Augmented Movelet Method for Activity Classification Using Smartphone Gyroscope and Accelerometer Data.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Physical activity, such as walking and ascending stairs, is commonly used in biomedical settings as an outcome or covariate. Researchers have traditionally relied on surveys to quantify activity levels of subjects in both research and clinical settings, but surveys are subjective in nature and have known limitations, such as recall bias. Smartphones provide an opportunity for unobtrusive objective measurement of physical activity in naturalistic settings, but their data tends to be noisy and needs to be analyzed with care. We explored the potential of smartphone accelerometer and gyroscope data to distinguish between walking, sitting, standing, ascending stairs, and descending stairs. We conducted a study in which four participants followed a study protocol and performed a sequence of activities with one phone in their front pocket and another phone in their back pocket. The subjects were filmed throughout, and the obtained footage was annotated to establish moment-by-moment ground truth activity. We introduce a modified version of the so-called movelet method to classify activity type and to quantify the uncertainty present in that classification. Our results demonstrate the promise of smartphones for activity recognition in naturalistic settings, but they also highlight challenges in this field of research.
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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.