Evidence map›Paper›PMID 35214504›Full record

ArticleSensors (Basel, Switzerland)2022

Re-Orienting Smartphone-Collected Car Motion Data Using Least-Squares Estimation and Machine Learning.

Enrico Bassetti, Alessio Luciani, Emanuele Panizzi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Enrico BassettiDepartment of Computer Science, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0003-4804-0311
Alessio LucianiDepartment of Computer Science, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0002-7477-8750
Emanuele PanizziDepartment of Computer Science, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0002-7442-8451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Smartphone sensors can collect data in many different contexts. They make it feasible to obtain large amounts of data at little or no cost because most people own mobile phones. In this work, we focus on collecting motion data in the car using a smartphone. Motion sensors, such as accelerometers and gyroscopes, can help obtain information about the vehicle's dynamics. However, the different positioning of the smartphone in the car leads to difficulty interpreting the sensed data due to an unknown orientation, making the collection useless. Thus, we propose an approach to automatically re-orient smartphone data collected in the car to a standardized orientation (i.e., with zero yaw, roll, and pitch angles with respect to the vehicle). We use a combination of a least-square plane approximation and a Machine Learning model to infer the relative orientation angles. Then we populate rotation matrices and perform the data rotation. We trained the model by collecting data using a vehicle physics simulator.

Indexed as

AutomobilesSmartphoneHumansMachine LearningRotationangle parkingcontext awarecurbimplicit interactionmachine learningparallelparkingsensingsmart citysmartphone

Identifiers

PMID35214504
PMCPMC8875019

What OpenQuestion holds

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Registered trials

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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.