Evidence map›Paper›PMID 32012920›Full record

ArticleSensors (Basel, Switzerland)2020

Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet.

Lin Shu, Yang Yu, Wenzhuo Chen, Haoqiang Hua, Qin Li, Jianxiu Jin, Xiangmin Xu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

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

  1. Pooled it
  2. Review
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  8. Multimodal Sensing and Modeling of Endocrine Therapy Adherence in Breast Cancer Survivors.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025
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  9. Review
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  15. Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025
    Review
  16. Article
  17. Preparing Wearable Data for AI-Powered Mood and Compliance Prediction in HCT Patients and Caregivers.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024
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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

7 authors.

Lin ShuSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
Yang YuSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
Wenzhuo ChenSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
Haoqiang HuaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
Qin LiSchool of Software Engineering, the Shenzhen Institute of Information Technology, Shenzhen 518172, China.
Jianxiu JinSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
Xiangmin XuSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.

Funding

National Natural Science Foundation of China U180120050
6 · The paper itself

Abstract

Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and human-computer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address this issue, our study proposes a method for emotional recognition using heart rate data from a wearable smart bracelet. A 'neutral + target' pair emotion stimulation experimental paradigm was presented, and a dataset of heart rate from 25 subjects was established, where neutral plus target emotion (neutral, happy, and sad) stimulation video pairs from China's standard Emotional Video Stimuli materials (CEVS) were applied to the recruited subjects. Normalized features from the data of target emotions normalized by the baseline data of neutral mood were adopted. Emotion recognition experiment results approved the effectiveness of 'neutral + target' video pair simulation experimental paradigm, the baseline setting using neutral mood data, and the normalized features, as well as the classifiers of Adaboost and GBDT on this dataset. This method will promote the development of wearable consumer electronic devices for monitoring human emotional moods.

Indexed as

Wearable Electronic DevicesAdultChinaEmotionsFacial ExpressionFemaleHeart RateHumansMaleRecognition, PsychologyYoung Adultemotion recognitionheart ratesmart braceletwearable

Identifiers

PMID32012920
PMCPMC7038485

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.