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Cited by 293Emotion and Mood RecognitionContext-Aware Activity Recognition SystemsSentiment Analysis and Opinion Mining

Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection

Open access
Kanjo, Eiman, Younis, Eman M.G., Ang, Chee Siang·2019·Information Fusion
DOI 10.1016/j.inffus.2018.09.001
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Abstract

The detection and monitoring of emotions are important in various applications, e.g., to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g., EEG and GSR), environmental data (e.g., audio and weather), videos (e.g., for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%)

AI snapshot

Written from the paper’s full text.

TL;DR

The paper evaluates deep learning on multimodal smartphone/wearable sensor data for emotion detection “in the wild” using the EnvBodySens dataset. A hybrid CNN-LSTM model on fused raw sensor channels achieved the best average accuracy and F-measure (94.7% and 0.949) compared with MLP and CNN baselines.

Key findings

  • CNN-LSTM on fused modalities achieved average accuracy 94.7% and F-measure 0.949 (Table 1; abstract rounds to 95% and 95%), outperforming MLP (72.9%, F-measure 0.729) and CNN (78.6%, F-measure 0.787).
  • Among single modalities, on-body data was strongest for CNN-LSTM (accuracy 87.3%, F-measure 0.874), while environment (59.7%) and location (64.0%) were weaker alone; fusing the other modalities with on-body data improved accuracy by approximately 7%.
  • CNN-LSTM outperformed the authors’ earlier ensemble feature-engineering approach by a 6% margin (ensemble average accuracy 83%, F-measure 82%) and exceeded MLP by more than 20%.
  • A sliding window size of 40 samples (160 ms) performed best, with F-measure 0.949, accuracy 94.7, and RMSE 0.291.

Methods

  • Dataset: EnvBodySens, collected from 40 female participants (average age 28) walking in Nottingham city centre for up to 45 minutes, yielding 550,432 sensor data frames and 5,345 self-report responses on a 5-step SAM valence scale; six users were excluded due to logging problems. Sensors included heart rate, GSR, body temperature, accelerometer, gyro, noise, UV, air pressure, and GPS, forming 20 channels across on-body, environmental, and location modalities.
  • Models: MLP with two hidden layers (64 and 32 neurons, input 20×40, 5 outputs), CNN with two convolutional layers (32 and 64 filters), two max-pooling layers, and a fully connected layer, and CNN-LSTM adding an LSTM layer. Models were trained per participant on each modality separately and on fused data, totaling 12 models per user dataset.
  • Analysis: Data were preprocessed by removing the first and last 30 seconds and segmenting with a sliding window; each subject’s data were split 70% training / 30% testing. Performance was compared across architectures and modality combinations using precision, recall, F-measure, accuracy, and RMSE, implemented in TensorFlow.

Limitations

  • Stated in the paper: many network parameters remain tunable and were not fully explored (e.g., overlapping windows and overlap width); the approach needs testing on diverse datasets with larger numbers of participants and additional modalities such as pollution levels and crowd density. Combining all participants into one dataset produced high across-subject variation and model accuracy below 50%, indicating that emotion recognition is subject-dependent and difficult to generalize across individuals.
  • Evident from the text: the sample is limited to 40 female participants, average age 28, walking fixed routes in Nottingham under similar weather conditions (average 20°C) around 11am, which restricts demographic, geographic, and contextual generalizability. Six users’ data were also excluded because of logging problems.

Model-written summary. Check it against the paper before relying on it.

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Registry

Crossref · 10.1016/j.inffus.2018.09.001

Cites

54 of 54 references verified0 citation errors · 0 not checkable

Statistics

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Claims the paper makes

0 of 26 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering, which achieved average accuracy 83% and F-measure 82%.

    “Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure = 82%)”
    Quote in the paperSubstantial claim
  • Findingown data

    Deep-learning approaches are effective in human emotion classification when a large number of sensor inputs is utilised, with average accuracy 95% and F-measure 95%.

    “The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure = %95)”
    Quote in the paperSubstantial claim
  • Findingown data

    The confusion between state 0 and 1 is improved when LSTM is added to the architecture.

    “which is improved when LSTM is added to the architecture.”
    Quote in the paperNot a substantial claim
  • Findingcitation

    Applying deep learning on multimodal sensor data outperformed the earlier Ensemble algorithm by 6% margin.

    “Furthermore, applying deep learning on multimodel sensor data outperformed our earlier Ensemble algorithm by 6% margin [6]”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [6] Kanjo, 2018, Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach, Inf. Fusion source

      Not checked in the free run.

  • Findingown data

    CNN-LSTM did not do as well with Location data.

    “although the model did not do as well with Location data.”
    Quote in the paperSubstantial claim
  • May combine two claims. 16
  • The quote only partly states this claim. 1
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  • May combine two claims.

    Findingown data

    Hybrid models outperform traditional fully connected deep neural networks in human emotion classification, with the latter achieving average accuracy 73% and F-measure 73%.

    “and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure = 73%).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Both on-Body and Location models were improved with CNN.

    “Both on-Body and Location models were improved with CNN.”
    Quote in the paperNot a substantial claim

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    Findingown data

    CNN achieved an average accuracy of 0.79 (F-Measure 0.79±0.034).

    “with an average accuracy 0.79.(F-Measure:0.79± 0.034).”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Findingown data

    MLP on fused modalities achieved significantly higher accuracy than each single modality (p < 0.01).

    “which is significantly higher than each single modality ( p < 0.01).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Combining all participants data into one single dataset for emotion detection led to high across-subject variation and low model accuracy of less than 50%.

    “We have attempted to combine all participants data into one single dataset for emotion detection, however we found a high across-subject variation in the dataset which led to low model accuracy of less than 50%.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Location model achieved average accuracy 0.60 (F-Measure 0.58±0.032).

    “Location model achieved an average accuracy of 0.60 (F-Measure:0.580.032)”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Many accessible physiological signals may be non-differentiable in emotion detection.

    “Many accessible physiological signals may be non-differentiable in emotion detection [11] .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [11] Picard, 2003, Affective computing: challenges, Int. J. Hum. Comput. Stud. source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Controlled lab studies of emotion detection are valuable for certain applications but their use is limited to strictly controlled environments.

    “Although this type of controlled studies is valuable for certain applications (e.g., clinical diagnosis in healthcare), its use is rather limited to strictly controlled environments.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Deep learning has been increasingly used for non-image/speech data, including human activity recognition using time series data such as smartphone accelerometer data.

    “Due to its success in image and speech classification, deep learning has been increasingly used for non-image/speech data, including human activity recognition using time series data such as in the case of smart phone accelerometer data [1,[17][18][19] .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1,[17][18][19]

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    Premisecitation

    The computational cost of feature selection may increase combinatorially as the number of features increases.

    “The computational cost of feature selection may increase combinatorially as the number of features increases [28] .”
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    • cites [28] Bins, 2001, Feature selection from huge feature sets

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    Study design and conductreasoning

    The comparison between single, multiple modalities, and across fusion approaches is fair because all experiments follow the same procedure.

    “However, the comparison between single, multiple modalities, and across fusion approaches is fair because all experiments follow the same procedure.”
    Quote in the paperSubstantial claim

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    Premisecitation

    The CNN-LSTM hybrid model outperformed other deep models without using LSTM to model time dependencies.

    “Their results showed that CNN-LSTM hybrid model outperformed other deep models without using LSTM to model time dependencies.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [21] Ordóñez, 2016, Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition, Sensors source

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    Premisereasoning

    LSTMs help preserve the error that can be backpropagated through time and layers by using a gated cell that determines what information from the prior step should be forgotten and what information in the current time step should be remembered into the next state.

    “LSTMs help preserve the error that can be backpropagated through time and layers by using a gated cell which determines what information from the prior step should be forgotten and what information in current time step should be remembered into the next state, via gates that open and close (activate and deactivate).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    We believe our models can also work on almost any other sensor data (beyond emotion detection and city sensing).

    “we believe our models can also work on almost any other sensor data (beyond emotion detection and city sensing).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Batch size has implications for the robustness of the error that is propagated in the learning phase.

    “The batch size has implications for the robustness of the error that is propagated in the learning phase [29] .”
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    • cites [29] Bengio, 2007, Greedy layer-wise training of deep networks

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    Premisecitation

    Empirical studies showed that data representations obtained from stacking up non-linear feature extracting layers as in deep learning often yield better results, including improved classification model accuracy, better generative models, and invariant characteristics of data representations.

    “Empirical studies showed that data representations obtained from stacking up non-linear feature extracting layers as in deep learning often yield better results, e.g., improved classification model accuracy [18] , better generative models (to produce better quality samples) [18] , and the invariant characteristics of data representations [18] .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Ronao, 2016, Human activity recognition with smartphone sensors using deep learning neural networks, Expert Syst. Appl. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    The promising results hold potential for novel applications in emotion recognition and can open new opportunity in the study of mental health and well-being in real-life settings.

    “The promising results demonstrated in the study holds the potential for novel applications in emotion recognition and can open new opportunity in the study of mental health and well-being in real-life settings.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisenone given

    Deep learning techniques can work on raw data and automate feature extraction and selection.

    “One interesting property of deep learning techniques is that they can work on raw data and automate the feature extraction and selection.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Study design and conductnone given

    The dataset is reasonably balanced with small variation from one user to another.

    “We believe our dataset is reasonably balanced with small variation from one user to another.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    Emotion recognition is subject dependent, making it difficult to obtain a generalised model across individuals.

    “This observation is in agreement with previous studies [49] which verify that emotion recognition is subject dependent which makes it difficult to obtain a generalised model across individuals.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [49] Gao, 2015, Deep learninig of eeg signals for emotion recognition

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    These findings showcase the potential of deep learning for affective modeling based on multiple sensors and multiple modalities input, as both manual feature extraction and automatic feature selection could be ultimately bypassed.

    “These findings showcased the potential of deep learning for affective modeling based multiple sensors and multiple modalities input, as both manual feature extraction and automatic feature selection could be ultimately bypassed.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: In some affective states examined (e.g., relaxation models built on EDA; fun and excitement models built on BVP; relaxation models built on fused EDA and BVP), deep learning without prior feature selection manages to reach or even outperform the performances of models built on ad-hoc extracted features boosted by automatic feature selection.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Deep learning techniques have made significant impacts in computer vision, speech recognition and natural language processing, where it performs better than standard machine learning methods and comparably to human level.

    “Deep learning techniques have already made significant impacts in computer vision [13,29,30] , speech recognition [31,32] and natural language processing [20,33,34] , where it performs better than standard machine learning methods and the performance is comparable to human level.”
    Quote in the paperNot a substantial claim0 of 8 sources checked
    • cites [13] Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput. source

      Not checked in the free run.

    • cites [29] Bengio, 2007, Greedy layer-wise training of deep networks

      Not checked in the free run.

    • cites [30] Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks

      Not checked in the free run.

    • cites [31] Dahl, 2010, Phone recognition with the mean-covariance restricted Boltzmann machine

      Not checked in the free run.

    • cites [32] Dahl, 2012, Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, IEEE Trans. Audio Speech Lang. Process. source

      Not checked in the free run.

    • cites [20] X. Zhou, J. Guo, R. Bie, Deep learning based affective model for speech emotion recognition, in: Proceedings of the International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), IEEE, 2016, pp. 841–846. source

      Not checked in the free run.

    • cites [33] Socher, 2011, Dynamic pooling and unfolding recursive autoencoders for paraphrase detection

      Not checked in the free run.

    • cites [34] Bordes, 2012, Joint learning of words and meaning representations for open-text semantic parsing

      Not checked in the free run.

  • May combine two claims.

    Study design and conductcitation

    The correlation matrix in [6] shows a low level of correlation between the independent variables, suggesting that our model will not be affected by the multi-collinearity problem.

    “The correlation matrix in [6] shows a low level of correlation between the independent variables, suggesting that our model will not be affected by the multi-collinearity problem.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [6] Kanjo, 2018, Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach, Inf. Fusion source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Data from six users were excluded due to logging problem.

    “Data from six users were excluded due to logging problem.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S1 (asserted), S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Study design and conductnone given

    Our approach needs to be tested on diverse datasets with larger number of participants and with more modalities and account to other factors such as pollution levels and crowd density, which may have significant impact on human emotions.

    “our approach needs to be tested on diverse datasets with larger number of participants and with more modalities and account to other factors such as pollution levels and crowd density, which may have significant impact on human emotions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The CNN-LSTM hybrid model can potentially be used in multimodal sensor data.

    “Importantly, it was shown that the model can potentially be used in multimodal sensor data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [21] Ordóñez, 2016, Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition, Sensors source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Search algorithms may not be able to converge to optimal feature sets for a given model.

    “In general, search algorithms may not be able to converge to optimal feature sets for a given model [16] .”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [16] Supratak, 2016, Survey on feature extraction and applications of biosignals

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Our results confirm that emotion recognition is subject dependent as the accuracy varies from subject to subject and exhibits high variance of accuracy.

    “Our results, confirm this, and verify that the emotion recognition is subject dependent as the accuracy varies from subject to subject and exhibits high variance of accuracy.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning

    This procedure may hinder partially the generality of the created models, as the average cross-validation accuracy is used to guide the feature selection search.

    “This procedure may hinder partially the generality of the created models, as the average cross-validation accuracy is used to guide the feature selection search.”
    Quote in the paperNot a substantial claim
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  • The paper relies on this without support.

    No other work has applied deep learning on the combination of physiological, environmental and location data for emotion recognition.

    “Specifically, to the best of our knowledge, no other work has applied deep learning on the combination of physiological, environmental and location data for emotion recognition.”

  • The paper relies on this without support.

    The majority of studies employing deep learning on activity recognition are restricted to a handful of data channels.

    “The majority of the studies employing deep learning on activity recognition are restricted to a handful of data channels as opposed to this study, where we utilise 20 sensor channels from three different modalities to classify emotion against self-reported emotion labels.”

  • The paper relies on this without support.

    Hand-crafted features are domain-specific and often poorly generalise to other similar problem domains.

    “Although hand-crafted features have yielded promising results, they are domainspecific, and often poorly generalise to other similar problem domains.”

  • The paper relies on this without support.

    Handcrafted-based approaches involve laborious human intervention for selecting the most discriminating features and decision thresholds from sensor data.

    “Handcrafted-based approaches involve laborious human intervention for selecting the most discriminating features and decision thresholds from sensor data.”

  • The paper relies on this without support.

    Traditional machine learning and feature engineering algorithms may not be efficient enough to extract the complex and non-linear patterns generally observed in multimodal time series datasets.

    “Moreover, traditional machine learning and feature engineering algorithms may not be efficient enough to extract the complex and non-linear patterns generally observed in multimodal time series datasets.”

  • The paper relies on this without support.

    Feature extraction and feature selection are computationally expensive.

    “Furthermore, feature extraction and feature selection are computationally expensive.”

  • The paper relies on this without support.

    Traditional shallow methods, which contain only a small number of non-linear operations, do not have the capacity to accurately model variation of time series data.

    “Traditional shallow methods, which contain only a small number of non-linear operations, do not have the capacity to accurately model such variation of time series data.”

  • The paper relies on this without support.

    CNN can capture local dependencies and invariant features in accelerometer data.

    “It was shown that CNN can capture local dependencies and invariant features in the data.”

  • The paper relies on this without support.

    In some affective states examined (e.g., relaxation models built on EDA; fun and excitement models built on BVP; relaxation models built on fused EDA and BVP), deep learning without prior feature selection manages to reach or even outperform the performances of models built on ad-hoc extracted features boosted by automatic feature selection.

    “Furthermore, in some affective states examined (e.g., relaxation models built on Electrodermal Activities (EDA); fun and excitement models built on Blood Volume Pulse (BVP); relaxation models built on fused EDA and BVP), deep learning without prior feature selection manages to reach or even outperform the performances of models built on ad-hoc extracted features which are boosted by automatic feat”

  • The paper relies on this without support.

    The EnvBodySens dataset includes key components for emotion modelling and is representative of a typical affective modelling scenario.

    “While the Env-BodySens dataset includes key components for emotion modelling and is representative of a typical affective modelling scenario”

  • The paper relies on this without support.

    This is one of the few studies looking into emotional recognition of participants in their natural environment using multiple sources of time series data.

    “This is one of the few studies looking into emotional recognition of participants in their natural environment using multiple sources of time series data.”

  • The paper relies on this without support.

    Traditional emotion recognition techniques require feature engineering process to be applied to data prior to modelling, which might be challenging especially if the dataset is multimodal and large.

    “Although this type of time series data can help us understand peoples emotion, traditional emotion recognition techniques requires features engineering process to be applied to data prior to modelling, which might be challenging especially if the dataset is multimodal and large.”

  • The paper relies on this without support.

    Deep learning offers an automated way for features extraction embedded in the process.

    “Deep learning offers an automated way for features extraction embedded in the process.”

  • The paper relies on this without support.

    Others have successfully created a universal deep learning model for gesture data as gestures performed by different individuals are typically quite similar.

    “Others have successfully created a universal deep learning model for gesture data as gestures performed by different individuals are typically quite similar.”
14 of 14

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Convolution padding descriptions conflict

    Major

    The methods description says the proposed convolution uses no zero padding, but the CNN implementation specifies padding that prevents the matrix from shrinking.

  • Fused MLP F-measure disagrees with Table 1

    Minor

    The text reports an F-measure of 0.58 for the MLP trained on fused modalities, whereas Table 1 reports 0.729 for the MLP "All" model.

2 of 2

Cites

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Research works printed but not linked: 1

  1. M. Gadaleta, M. Rossi, IDnet: smartphone-based gait recognition with convolutional neural networks, arXiv:1606.03238 (2016). · arXiv

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Related

Authors
Kanjo, E., Younis, E. M. G., & Ang, C. S.
Affiliations
  • Nottingham Trent University (GB)
  • Minia University (EG)
  • University of Kent (GB)
Corresponding author
Kanjo, Eiman
ORCID
Venue
Information Fusion
Publisher
Elsevier BV
Published
2019-09
Volume
49
Pages
46-56
ISSN
1566-2535 (ISSN-L)
DOI
10.1016/j.inffus.2018.09.001
OpenAlex
W2898242330
Cited by
293
References
51
Citations by year
2026: 18 · 2025: 23 · 2024: 43 · 2023: 43 · 2022: 52 · 2021: 42 · 2020: 54 · 2019: 16 · 2018: 2
FWCI
37.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Information Fusion (publishedVersion, cc-by-nc-nd)
  • Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) (acceptedVersion)
  • Kent Academic Repository (University of Kent) (submittedVersion)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
emotion recognition, multimodal sensor data, mobile sensing, wearable devices, on-body sensors, location data, environmental sensors, CNN-LSTM, emotion classification, feature extraction, human-robot interaction, smartphone sensing

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