# Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey

arXiv: https://arxiv.org/abs/2107.05989 · HTML: https://mlsanity.com/paper/2107.05989/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 51 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Survey on Emotional Body Gesture Recognition](https://mlsanity.com/paper/1801.07481/) (2018)
- [Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges](https://mlsanity.com/paper/2004.07311/) (2020)
- [SSHealth: Toward Secure, Blockchain-Enabled Healthcare Systems](https://mlsanity.com/paper/2006.10843/) (2020)
- [DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network](https://mlsanity.com/paper/1702.04280/) (2017)

## Built on by (most cited first, 6 of 6)

- [BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Digital Behavioural Change](https://mlsanity.com/paper/2505.19328/) (2025)
- [Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss](https://mlsanity.com/paper/2207.11482/) (2022)
- [ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos](https://mlsanity.com/paper/2405.13903/) (2024)
- [Improving Facial Emotion Recognition through Dataset Merging and Balanced Training Strategies](https://mlsanity.com/paper/2604.20307/) (2026)
- [Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition](https://mlsanity.com/paper/2207.12248/) (2022)
- [CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion Recognition](https://mlsanity.com/paper/2402.06923/) (2024)

## Abstract (arXiv)

Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements. Automatically identifying the emotions can help build smart healthcare centers that can detect depression and stress among the patients in order to start the medication early. Using advanced technology to identify emotions is one of the most exciting topics as it defines the relationships between humans and machines. Machines learned how to predict emotions by adopting various methods. In this survey, we present recent research in the field of using neural networks to recognize emotions. We focus on studying emotions' recognition from speech, facial expressions, and audio-visual input and show the different techniques of deploying these algorithms in the real world. These three emotion recognition techniques can be used as a surveillance system in healthcare centers to monitor patients. We conclude the survey with a presentation of the challenges and the related future work to provide an insight into the applications of using emotion recognition.