Evidence map›Paper›PMID 38645596›Full record

ArticleBiomedical engineering letters2024

A multimodal framework based on deep belief network for human locomotion intent prediction.

Jiayi Li, Jianhua Zhang, Kexiang Li, Jian Cao, Hui Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical engineering letters, 2024. 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

5 authors.

Jiayi LiSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401 China.
Jianhua ZhangSchool of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083 China.
Kexiang LiSchool of Mechanical and Materials Engineering, North China University of Technology, Beijing, 100144 China.
Jian CaoSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401 China.
Hui LiSchool of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prediction of human locomotion intent benefits the seamless switching of lower limb exoskeleton controllers in different terrains to assist humans in walking safely. In this paper, a deep belief network (DBN) was developed to construct a multimodal framework for recognizing various locomotion modes and predicting transition tasks. Three fusion strategies (data level, feature level, and decision level) were explored, and optimal network performance was obtained. This method could be tested on public datasets. For the continuous performance of steady state, the best prediction accuracy achieved was 97.64% in user-dependent testing and 96.80% in user-independent testing. During the transition state, the system accurately predicted all transitions (user-dependent: 96.37%, user-independent: 95.01%). The multimodal framework based on DBN can accurately predict the human locomotion intent. The experimental results demonstrate the potential of the proposed model in the volition control of the lower limb exoskeleton.

Indexed as

Deep belief networkDeep learningExoskeletonsLocomotion intent predictionMultimodal fusion

Identifiers

PMID38645596
PMCPMC11026357

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.