Evidence map›Paper›PMID 29020959›Full record

ArticleJournal of neuroengineering and rehabilitation2017

Configurable, wearable sensing and vibrotactile feedback system for real-time postural balance and gait training: proof-of-concept.

Junkai Xu, Tian Bao, Ung Hee Lee, Catherine Kinnaird, Wendy Carender, Yangjian Huang, Kathleen H Sienko, Peter B Shull

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 6 pooled it
2.9field-weighted citation impact, top 9% of its field
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

27 citing papers in PubMed, 6 syntheses or guidelines pooled it, 69 citations in OpenAlex.

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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

8 authors at 2 institutions in 2 countries.

Junkai XuState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
Tian BaoDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
Ung Hee LeeDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
Catherine KinnairdDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
Wendy CarenderVestibular Testing Center, Department of Otolaryngology, University of Michigan, Ann Arbor, MI, USA.
Yangjian HuangState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
Kathleen H SienkoDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
Peter B ShullState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China. pshull@sjtu.edu.cn.
University of Michigan–Ann Arbor · USShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPostural balance and gait training is important for treating persons with functional impairments, however current systems are generally not portable and are unable to train different types of movements.

methodsThis paper describes a proof-of-concept design of a configurable, wearable sensing and feedback system for real-time postural balance and gait training targeted for home-based treatments and other portable usage. Sensing and vibrotactile feedback are performed via eight distributed, wireless nodes or "Dots" (size: 22.5 × 20.5 × 15.0 mm, weight: 12.0 g) that can each be configured for sensing and/or feedback according to movement training requirements. In the first experiment, four healthy older adults were trained to reduce medial-lateral (M/L) trunk tilt while performing balance exercises. When trunk tilt deviated too far from vertical (estimated via a sensing Dot on the lower spine), vibrotactile feedback (via feedback Dots placed on the left and right sides of the lower torso) cued participants to move away from the vibration and back toward the vertical no feedback zone to correct their posture. A second experiment was conducted with the same wearable system to train six healthy older adults to alter their foot progression angle in real-time by internally or externally rotating their feet while walking. Foot progression angle was estimated via a sensing Dot adhered to the dorsal side of the foot, and vibrotactile feedback was provided via feedback Dots placed on the medial and lateral sides of the mid-shank cued participants to internally or externally rotate their foot away from vibration.

resultsIn the first experiment, the wearable system enabled participants to significantly reduce trunk tilt and increase the amount of time inside the no feedback zone. In the second experiment, all participants were able to adopt new gait patterns of internal and external foot rotation within two minutes of real-time training with the wearable system.

conclusionThese results suggest that the configurable, wearable sensing and feedback system is portable and effective for different types of real-time human movement training and thus may be suitable for home-based or clinic-based rehabilitation applications.

Indexed as

Biofeedback, PsychologyExoskeleton DeviceAgedEquipment DesignFemaleGaitGait Disorders, NeurologicHealthy VolunteersHumansMalePostural BalancePsychomotor PerformanceSoftwareTouchVibrationBalance trainingGait retrainingWearable systems

Identifiers

PMID29020959
PMCPMC5637356
OpenAlexW2761474107

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.