ArticleIEEE sensors journal2023
Tactile Gloves Predict Load Weight During Lifting With Deep Neural Networks.
Article in IEEE sensors journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Flexible Resistive Sensors for Wearable and Ergonomics Applications: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Computer vision and tactile glove: A multimodal model in lifting task risk assessment.Applied ergonomics · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Overexertion in lifting tasks is one of the leading causes of occupational injuries. The load weight is the key information required to evaluate the risk of a lifting task. However, weight varies across different objects and is unknown in many circumstances. Existing methods of estimating the load weight without manual weighing focused on analyzing body kinematics or muscle activations, which either utilize indirect indicators or require intrusive sensors. This study proposed using tactile gloves as a new modality to predict the load weight. Hand pressure data measured by tactile gloves during each lift were formulated as a 2-D matrix containing spatial and temporal information. Different types of deep neural networks were adopted, and a ResNet 18 regression model achieved the best performance. Specifically, it achieved a predicted
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Registered trials
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.