Evidence map›Paper›PMID 36616958›Full record

ArticleSensors (Basel, Switzerland)2022

Upper Limb Position Tracking with a Single Inertial Sensor Using Dead Reckoning Method with Drift Correction Techniques.

Lu Bai, Matthew G Pepper, Zhibao Wang, Maurice D Mulvenna, Raymond R Bond, Dewar Finlay, Huiru Zheng

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Lu BaiSchool of Computing, Ulster University, Belfast BT15 1ED, UK.ORCID 0000-0003-1242-5412
Matthew G PepperSchool of Engineering, University of Kent, Canterbury CT2 7NZ, UK.
Zhibao WangSchool of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China.
Maurice D MulvennaSchool of Computing, Ulster University, Belfast BT15 1ED, UK.ORCID 0000-0002-1554-0785
Raymond R BondSchool of Computing, Ulster University, Belfast BT15 1ED, UK.
Dewar FinlaySchool of Engineering, Ulster University, Belfast BT15 1ED, UK.
Huiru ZhengSchool of Computing, Ulster University, Belfast BT15 1ED, UK.ORCID 0000-0001-7648-8709

Funding

University of Kent and East Kent Hospitals University NHS Foundation Trust (EKHUFT) N/A
6 · The paper itself

Abstract

Inertial sensors are widely used in human motion monitoring. Orientation and position are the two most widely used measurements for motion monitoring. Tracking with the use of multiple inertial sensors is based on kinematic modelling which achieves a good level of accuracy when biomechanical constraints are applied. More recently, there is growing interest in tracking motion with a single inertial sensor to simplify the measurement system. The dead reckoning method is commonly used for estimating position from inertial sensors. However, significant errors are generated after applying the dead reckoning method because of the presence of sensor offsets and drift. These errors limit the feasibility of monitoring upper limb motion via a single inertial sensing system. In this paper, error correction methods are evaluated to investigate the feasibility of using a single sensor to track the movement of one upper limb segment. These include zero velocity update, wavelet analysis and high-pass filtering. The experiments were carried out using the nine-hole peg test. The results show that zero velocity update is the most effective method to correct the drift from the dead reckoning-based position tracking. If this method is used, then the use of a single inertial sensor to track the movement of a single limb segment is feasible.

Indexed as

MovementUpper ExtremityBiomechanical PhenomenaHumansMotiondead reckoninghigh-pass filterinertial sensorupper limb motion monitoringwavelet analysiszero velocity update

Identifiers

PMID36616958
PMCPMC9823748

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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.