Utilizing Machine Learning for Context-Aware Digital Biomarker of Stress in Older Adults

被引:1
|
作者
Onim, Md Saif Hassan [1 ]
Thapliyal, Himanshu [1 ]
Rhodus, Elizabeth K. [2 ]
机构
[1] Univ Tennessee, Dept Elect Engn & Comp Sci, VLSI Emerging Design & Nano Things Secur Lab, VEDANTS Lab, Knoxville, TN 37996 USA
[2] Univ Kentucky, Sanders Brown Ctr Aging, Dept Behav Sci, Lexington, KY 40536 USA
关键词
CNN; machine learning; stress detection; context; cortisol; digital biomarkers;
D O I
10.3390/info15050274
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Identifying stress in older adults is a crucial field of research in health and well-being. This allows us to take timely preventive measures that can help save lives. That is why a nonobtrusive way of accurate and precise stress detection is necessary. Researchers have proposed many statistical measurements to associate stress with sensor readings from digital biomarkers. With the recent progress of Artificial Intelligence in the healthcare domain, the application of machine learning is showing promising results in stress detection. Still, the viability of machine learning for digital biomarkers of stress is under-explored. In this work, we first investigate the performance of a supervised machine learning algorithm (Random Forest) with manual feature engineering for stress detection with contextual information. The concentration of salivary cortisol was used as the golden standard here. Our framework categorizes stress into No Stress, Low Stress, and High Stress by analyzing digital biomarkers gathered from wearable sensors. We also provide a thorough knowledge of stress in older adults by combining physiological data obtained from wearable sensors with contextual clues from a stress protocol. Our context-aware machine learning model, using sensor fusion, achieved a macroaverage F-1 score of 0.937 and an accuracy of 92.48% in identifying three stress levels. We further extend our work to get rid of the burden of manual feature engineering. We explore Convolutional Neural Network (CNN)-based feature encoder and cortisol biomarkers to detect stress using contextual information. We provide an in-depth look at the CNN-based feature encoder, which effectively separates useful features from physiological inputs. Both of our proposed frameworks, i.e., Random Forest with engineered features and a Fully Connected Network with CNN-based features validate that the integration of digital biomarkers of stress can provide more insight into the stress response even without any self-reporting or caregiver labels. Our method with sensor fusion shows an accuracy and F-1 score of 83.7797% and 0.7552, respectively, without context and 96.7525% accuracy and 0.9745 F-1 score with context, which also constitutes a 4% increase in accuracy and a 0.04 increase in F-1 score from RF.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Learning Context-Aware Measurement Models
    Virani, Nurali
    Lee, Ji-Woong
    Phoha, Shashi
    Ray, Asok
    [J]. 2015 AMERICAN CONTROL CONFERENCE (ACC), 2015, : 4491 - 4496
  • [22] Context-Aware Personalized Mobile Learning
    Madhubala, Radhakrishnan
    Akila
    [J]. INTELLIGENT COMPUTING AND COMMUNICATION, ICICC 2019, 2020, 1034 : 469 - 481
  • [23] Context-aware Personal Learning Environment
    Alharbi, Mafawez T.
    Platt, Amelia
    Al-Bayatti, Ali H.
    [J]. 2012 INTERNATIONAL CONFERENCE FOR INTERNET TECHNOLOGY AND SECURED TRANSACTIONS, 2012, : 692 - 697
  • [24] Learning with a Context-Aware Multiagent System
    Vladoiu, Monica
    Constantinescu, Zoran
    [J]. 9TH ROEDUNET IEEE INTERNATIONAL CONFERENCE, 2010, : 368 - +
  • [25] Context-Aware Learning for Generative Models
    Perdikis, Serafeim
    Leeb, Robert
    Chavarriaga, Ricardo
    Millan, Jose del R.
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (08) : 3471 - 3483
  • [26] Workshop on context-aware mobile learning
    Benlamri, Rachid
    Berri, Jawad
    [J]. 5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings, 2008,
  • [27] Context-Aware Mobile Language Learning
    Morales, Roberto
    Igler, Bodo
    Boehm, Stephan
    Chitchaipoka, Pichaya
    [J]. 10TH INTERNATIONAL CONFERENCE ON FUTURE NETWORKS AND COMMUNICATIONS (FNC 2015) / THE 12TH INTERNATIONAL CONFERENCE ON MOBILE SYSTEMS AND PERVASIVE COMPUTING (MOBISPC 2015) AFFILIATED WORKSHOPS, 2015, 56 : 82 - 87
  • [28] Management Challenges of Context-Aware Learning
    Pankowska, Malgorzata
    [J]. INTERNATIONAL CONFERENCE ON INTEGRATED INFORMATION (IC-ININFO 2014), 2015, 1644 : 207 - 214
  • [29] Enhancing QoS Context-Aware Ubiquitous Learning by Utilizing Logical and Physical Characteristic of Device
    Selviandro, Nungki
    Sabariah, Mira Kania
    Purna, Novandy
    [J]. 2016 4TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT), 2016,
  • [30] The digital government imperative: a context-aware perspective
    Castelnovo, Walter
    Sorrentino, Maddalena
    [J]. PUBLIC MANAGEMENT REVIEW, 2018, 20 (05) : 709 - 725