Evidence map›Paper›PMID 37622919›Full record

ReviewBiosensors2023

Insole Systems for Disease Diagnosis and Rehabilitation: A Review.

Zhiyuan Zhang, Yanning Dai, Zhenyu Xu, Nicolas Grimaldi, Jiamu Wang, Mufan Zhao, Ruilin Pang, Yueming Sun, Shuo Gao, Hu Boyi

Abstract readReview
In one paragraph

Review in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. 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

10 authors.

Zhiyuan ZhangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.ORCID 0000-0002-3626-7968
Yanning DaiSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Zhenyu XuSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.ORCID 0000-0002-6782-0826
Nicolas GrimaldiJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
Jiamu WangSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
Mufan ZhaoSchool of Artificial Intelligence, Beihang University, Beijing 100191, China.
Ruilin PangSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Yueming SunSchool of Electronics and Information Engineering, Beihang University, Beijing 100191, China.
Shuo GaoSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Hu BoyiSchool of Industrial and Systems Engineering, University of Florida, Gaineville, FL 32611, USA.

Funding

Beihang University KG12090401 and ZG216S19C8Beijing Municipal Natural Science Foundation M22019National Natural Science Foundation of China 62171014 and 61803017
6 · The paper itself

Abstract

Some chronic diseases, including Parkinson's disease (PD), diabetic foot, flat foot, stroke, elderly falling, and knee osteoarthritis (KOA), are related to orthopedic organs, nerves, and muscles. The interaction of these three parts will generate a comprehensive result: gait. Furthermore, the lesions in these regions can produce abnormal gait features. Therefore, monitoring the gait features can assist medical professionals in the diagnosis and analysis of these diseases. Nowadays, various insole systems based on different sensing techniques have been developed to monitor gait and aid in medical research. Hence, a detailed review of insole systems and their applications in disease management can greatly benefit researchers working in the field of medical engineering. This essay is composed of the following sections: the essay firstly provides an overview of the sensing mechanisms and parameters of typical insole systems based on different sensing techniques. Then this essay respectively discusses the three stages of gait parameters pre-processing, respectively: pressure reconstruction, feature extraction, and data normalization. Then, the relationship between gait features and pathogenic mechanisms is discussed, along with the introduction of insole systems that aid in medical research; Finally, the current challenges and future trends in the development of insole systems are discussed.

Indexed as

Biomedical ResearchDiabetic FootMedicineAgedGaitHumansMuscleschronic diseasesgait analysisinsole systemsplantar pressure

Identifiers

PMID37622919
PMCPMC10452488

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.