Affect and stress detection based on feature fusion of LSTM and 1DCNN

References

• Anusha AS, Sukumaran P, Sarveswaran V, Surees KS, Shyam A, Tony JA, Preejith SP, Mohanasankar S. 2019. Electrodermal activity based pre-surgery stress detection using a wrist wearable. IEEE J Biomed Health. 1:92–100.
   Google Scholar
• Awais M, Raza M, Singh N, Bashir K, Manzoor U, Islam S, Rodrigues JJPC. 2020. LSTM-based emotion detection using physiological signals: ioT framework for healthcare and distance learning in covid-19. IEEE Internet Things. 23:16863–16871.
   Google Scholar
• Castro-García JA, Molina-Cantero AJ, Gómez-González IM, Lafuente-Arroy S, Merino-Monge M. 2022. Towards human stress and activity recognition: a review and a first approach based on low-cost wearables. Electronics. 11(1):155.
   Web of Science ®Google Scholar
• Chakraborty S, Aich S, Joo M, Sain M, Kim HC. 2019. A multichannel convolutional neural network architecture for the detection of the state of mind using physiological signals from wearable devices. J Healthcare Eng. 2019:1–17.
   Web of Science ®Google Scholar
• Chakraborty SK, Chandel NS, Jat D, Tiwari MK, Rajwade YA, Subeesh A. 2022. Deep learning approaches and interventions for futuristic engineering in agriculture. Neural Comput Appl. 34(23):20539–20573.
   Web of Science ®Google Scholar
• Cosoli G, Poli A, Scalise L, Spinsante S. 2021. Measurement of multimodal physiological signals for stimulation detection by wearable devices. Measurement. 184:109966.
   Web of Science ®Google Scholar
• Dai RX, Lu CY, Yun LD, Lenze E, Avidan M, Kannampallil T. 2021. Comparing stress prediction models using smartwatch physiological signals and participant self-reports. Comput Meth Prog Biomed. 208:106207.
   PubMed Web of Science ®Google Scholar
• Dang CL, Wang F, Yang ZM, Zhang HX, Qian YF. 2022. Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model. Oper Manage Res. 15(3–4):662–675.
   Web of Science ®Google Scholar
• Dissanayake V, Seneviratne S, Rana R, Wen E, Kaluarachchi T, Nanayakkara S. 2022. SigRep: toward robust wearable emotion recognition with contrastive representation learning. IEEE Access. 10:18105–18120.
   Web of Science ®Google Scholar
• Garg P, Santhosh J, Dengel A, Ishimaru S. 2021. Stress detection by machine learning and wearable sensors. In: 26th International Conference on Intelligent User Interfaces - Companion: Association for Computing Machinery (IUI ’21 Companion); p. 43–45.
   Google Scholar
• Gautam R, Sharma M. 2020. Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. J Med Syst. 44(2):49.
   PubMed Web of Science ®Google Scholar
• Giannakakis G, Grigoriadis D, Giannakaki K, Simantiraki O, Roniotis A, Tsiknakis M. 2019. Review on psychological stress detection using biosignals. IEEE Trans Affect Comput. 1:440–460.
   Google Scholar
• Gupta D, Bhatia MPS, Kumar A. 2021. Resolving data overload and latency issues in multivariate time-series IoMT data for mental health monitoring. IEEE Sens J. 22:25421–25428.
   Google Scholar
• Hasnul MA, Aziz NAA, Alelyani S, Mohana M. 2021. Electrocardiogram-based emotion recognition systems and their applications in healthcare—a review. Sensors. 21(15):5015.
   PubMed Web of Science ®Google Scholar
• Heo S, Kwon S, Lee J. 2021. Stress detection with single PPG sensor by orchestrating multiple denoising and peak-detecting methods. IEEE Access. 9:47777–47785.
   Web of Science ®Google Scholar
• Hüsken M, Stagge P. 2003. Recurrent neural networks for time series classification. Neurocomputing. 50:223–235.
   Web of Science ®Google Scholar
• Iqbal T, Redon P, Simpkin AJ, Elahi A, Ganly S, Wijns W, Shahzad A. 2021. A sensitivity analysis of biophysiological responses of stress for wearable sensors in connected health. IEEE Access. 9:93567–93579.
   Web of Science ®Google Scholar
• Janiesch C, Zschech P, Heinrich K. 2021. Machine learning and deep learning. Electron Mark. 31(3):685–695.
   Web of Science ®Google Scholar
• Kang M, Shin S, Jung J, Kim YT. 2021. Classification of mental stress using cnn-lstm algorithms with electrocardiogram signals. J Healthcare Eng. 2021:1–11.
   Web of Science ®Google Scholar
• Kanjo E, Younis EMG, Ang CS. 2019. Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection. Inform Fusion. 49:46–56.
   Web of Science ®Google Scholar
• Ke L, Liu Y, Yang Y. 2022. Compound fault diagnosis method of modular multilevel converter based on improved capsule network. IEEE Access. 10:41201–41214.
   Web of Science ®Google Scholar
• Kumar A, Sharma K, Sharma A. 2021. Genetically optimized fuzzy c-means data clustering of IoMT-based biomarkers for fast affective state recognition in intelligent edge analytics. Appl Soft Comput. 109:107525.
   Web of Science ®Google Scholar
• Liang RH, Liu WF, Li WB, Wu ZZ. 2022. A traffic noise source identification method for buildings adjacent to multiple transport infrastructures based on deep learning. Build Environ. 211(108764):108764.
   Google Scholar
• Lv ZH, Yu ZC, Xie SX, Alamri A. 2022. Deep learning-based smart predictive evaluation for interactive multimedia-enabled smart healthcare. ACM Trans Multimedia Comput Commun Appl. 18:1–20.
   Web of Science ®Google Scholar
• Meng T, Jing XY, Yan Z, Pedrycz W. 2020. A survey on machine learning for data fusion. Inform Fusion. 57:115–129.
   Web of Science ®Google Scholar
• Mumtaz W, Qayyum A. 2019. A deep learning framework for automatic diagnosis of unipolar depression. Int J Med Inform. 132:103983.
   PubMed Web of Science ®Google Scholar
• Phutela N, Relan D, Gabrani G, Kumaraguru P, Samuel M. 2022. Stress classification using brain signals based on LSTM network. Comput Intel Neurosc. 2022:1–13.
   Web of Science ®Google Scholar
• Pouyanfar S, Sadiq S, Yan YL, Tian HM, Tao YD, Reyes MP, Shyu M, Chen S, Iyengar SS. 2018. A survey on deep learning. ACM Comput Surv. 51:1–36.
   Web of Science ®Google Scholar
• Saganowski S. 2022. Bringing emotion recognition out of the lab into real life: recent advances in sensors and machine learning. Electronics. 11(3):496.
   Web of Science ®Google Scholar
• Santamaria-Granados L, Munoz-Organero M, Ramirez-González G, Abdulhay E, Arunkumar N. 2019. Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS). IEEE Access. 7:57–67.
   Google Scholar
• Schmidt P, Reiss A, Duerichen R, Laerhoven KV. 2018. Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In: 20th ACM International Conference on Multimodal Interaction: association for Computing Machinery (ICMI); p. 400–408.
   Google Scholar
• Siirtola P. 2019. Continuous stress detection using the sensors of commercial smartwatch. 2019. In: ACM International Symposium on Wearable Computers: Association for Computing Machinery (ISWC); p. 1198–1201.
   Google Scholar
• Sun HB, Zhao SC, Qin Y. 2021. Fault diagnosis for bearing based on 1DCNN and LSTM. Shock Vib. 2021:1221462.
   Google Scholar
• Sundaresan A, Penchina B, Cheong S, Grace V, Valero‑Cabré A, Martel A. 2021. Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI. Brain Inform. 8:13.
   PubMedGoogle Scholar
• Tizzano GR, Spezialetti M, Rossi S. 2020. A deep learning approach for mood recognition from wearable data. In: 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA).
   Google Scholar
• Tran TD, Kim J, Ho NH, Yang HJ, Pant S, Kim SO, Lee GS. 2021. Stress analysis with dimensions of valence and arousal in the wild. Appl Sci. 11(11):5194.
   Google Scholar
• Tzevelekakis K, Stefanidi Z, Margetis G. 2021. Real-time stress level feedback from raw ECG signals for personalised, context-aware applications using lightweight convolutional neural network architectures. Sensors. 21(23):7802.
   PubMed Web of Science ®Google Scholar
• Yaday K, Yaday M, Saini S. 2022. Stock values predictions using deep learning based hybrid models. CAAI Trans Intell Technol. 7:107–116.
   Google Scholar
• Ye ZM, Deng F, Zhao JC, Lu MB. 2020. Dimension-raising processing framework for one-dimensional time series and its application in affect detection. In: 2020 IEEE 16th International Conference on Control & Automation (ICCA); p. 307–311.
   Google Scholar
• Zhu ZP, Song XZ, Zhang R, Li GS, Han L, Hu XL, Li DY, Yang DG, Qin FR. 2022. A hybrid neural network model for predicting bottomhole pressure in managed pressure drilling. Appl Sci. 12(13):6728.
   Google Scholar
• Zhuang L, Dai MH, Zhou Y, Sun LY. 2022. Intelligent automatic sleep staging model based on CNN and LSTM. Front Public Health. 10:946833.
   PubMed Web of Science ®Google Scholar