Table 2 Main study: Mean ± standard deviation of classification performance metrics over the 5-fold model evaluation CV, trained on features extracted over the complete (f-)TSST, respectively.

From: Machine learning-based detection of acute psychosocial stress from body posture and movements

Scaler

Feature selection

Classifier

Accuracy [%]

F1-score [%]

Precision [%]

Standard

SFM

RF

71.6 (5.9)

71.2 (4.2)

75.1 (13.1)

Min-Max

RFE

kNN

67.0 (8.4)

64.9 (11.2)

68.1 (8.5)

Min-Max

SkB

MLP

66.8 (6.7)

62.4 (7.5)

75.4 (14.8)

Min-Max

RFE

DT

65.2 (9.2)

65.9 (7.7)

66.3 (11.3)

Min-Max

SkB

NB

64.1 (10.0)

57.3 (12.2)

70.3 (13.3)

Standard

SkB

Ada

63.0 (9.3)

61.9 (7.3)

66.3 (13.2)

Min-Max

SkB

SVM

62.9 (5.6)

53.8 (11.9)

69.7 (7.0)

  1. For each evaluated classifier, the classification pipeline combination with the highest mean accuracy is shown. The classification pipelines scoring the highest metrics are highlighted in bold.