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Table 1 Recent and relevant studies on acute stress detection

From: Efficient methods for acute stress detection using heart rate variability data from Ambient Assisted Living sensors

Paper

Subjects (number, age)

Sensors

Stressing procedure

Classification methods

Window length (s)

Results

Ham 2017 [23]

6, 28.67 ± 3.3y

PPG (HRV)

Arithmetic stress, environmental stress, both while in Virtual Reality

LDA

240 s, non-overlapping

74% (baseline), 81% (mild stress), 82% (severe stress) Accuracy

Pereira 2017 [19]

14, 20-26y

ECG (HRV)

Trier Social Stress Test

N/A

50-220 s, non-overlapping

N/A

Zangróniz 2018 [24]

45, 20-28y

PPG (PRV)

IAPS

Decision Tree

70 s

82,35% Accuracy

Lawanont 2019 [10]

10, 20-26y

Activity tracker (FitBit)

Real-life work shifts

KNN, SVM, Decision Tree

1 h

84.1% (decision tree) Accuracy

Moridani 2020 [20]

20, 25 ± 5y

ECG (HRV)

Physical activity, arithmetic task, emotional stress (horror movie)

CNN

300 s (40 s for “mini emotional stress”)

97.9% (cognitive), 94.4% (emotional) F1 score

Pourmohammadi 2020 [34]

34, 25.4 ± 4.2y

EMG, ECG (HRV)

Arithmetic task, Stroop, environmental stressing

SVM

60 s, non-overlapping

100% (2 levels), 97.6% (3 levels), 96.2% (4 levels) Accuracy

Rodriguez-Arce 2020 [29]

21, 18-21y

GSR, PPG, body temp., breathing

Arithmetic task, electrocutaneous stimulation

KNN, SVM

 ~ 50 s (2500 samples per window)

90% (KNN), 95% (SVM) Accuracy

Sánchez-Reolid 2020 [27]

147, 31.4 ± 8.03y (w), 36.3 ± 4.99y (m)

EDA

IAPS

SVM, D-SVM

1-40 s, overlapping and non-overlapping

83% (SVM), 92% (D-SVM) F1 score

Zalabarria 2020 [30]

42, 22.88 ± 3.1y

ECG (HR), GSR, breathing

3D puzzle

Fuzzy algorithm

20 s sliding window

91.15% (stress), 96.61% (relax) F1 score

Zubair 2020 [26]

14, 25-30y

PPG (HRV)

Arithmetic tasks, Stroop

QDA, SVM

60 s, non-overlapping

94.33% (SVM), 89.73% (QDA) Accuracy

  1. LDA linear discriminant analysis, CNN Convolutional Neural Network, KNN K-nearest neighbours, (D-)SVM (Deep-)Support Vector Machine, QDA quadratic discriminant analysis