Effects of chest movements while sitting on Navon task performance and stress levels

Abstract

Background: This study explored physical activity during remote work, most of which takes place while sitting in front of a computer. The purpose of Experiment 1 was to develop a classification for body motion by creating a neural net that can distinguish among several kinds of chest movement. Experiment 2 examined the effects of chest movements on stress and performance on the Navon test to validate the model developed in Experiment 1.

Method and results: The procedures for this study were as follows.Experiment 1: Creation of the body movement classification model and preliminary experiment for Experiment 2.Data from five participants were used to construct a machine-learning categorization model. The other three participants participated in a pilot study for Experiment 2.Experiment 2: Model validation and confirmation of stress measurement validity.We recruited 34 new participants to test the validity of the model developed in Experiment 1. We asked 10 of the 34 participants to retake the stress measurement since the results of the stress assessment were unreliable.Using LSTM models, we classified six categories of chest movement in Experiment 1: walking, standing up and sitting down, sitting still, rotating, swinging, and rocking. The LSTM models yielded an accuracy rate of 83.8%. Experiment 2 tested the LSTM model and found that Navon task performance correlated with swinging chest movement. Due to the limited reliability of the stress measurement results, we were unable to draw a conclusion regarding the effects of body movements on stress. In terms of cognitive performance, swinging of the chest reduced RT and increased accuracy on the Navon task (β = .015 [-.003,.054], R² = .31).

Conclusions: LSTM classification successfully distinguished subtle movements of the chest; however, only swinging was related to cognitive performance. Chest movements reduced the reaction time, improving cognitive performance. However, the stress measurements were not stable; thus, we were unable to draw a clear conclusion about the relationship between body movement and stress. The results indicated that swinging of the chest improved reaction times in the Navon task, while sitting still was not related to cognitive performance or stress. The present article discusses how to collect sensor data and analyze it using machine-learning methods as well as the future applicability of measuring physical activity during remote work.

Keywords: Activity recognition; Machine learning; Mobile sensing; Remote work.

Fig. 1

Attachments and the position of smartphones

Fig. 2

The confusion matrix of the lowest accuracy rate among the five participants is shown in Table 1. The test data were from Participant B, and the learning data were from Participants A, C, D, and E. The vertical categories are the classification results of the model, and the horizontal categories are the actual categories

Fig. 3

The learning process of LSTM using 5 participants data

Fig. 4

Identification counts of each body movement. Figure 3a (left) shows standing by Participant F, Fig. 3b (middle) shows sitting by Participant G, and Fig. 3c (right) shows standing and sitting by Participant H over 45 min

Fig. 5

The vertical axis indicates the reaction time in milliseconds

Fig. 6

The numbers indicate the parameter’s standardized estimates