Relation between synchronization of head movements and degree of understanding on interpersonal communication

Ono, Eisuke; Motohasi, Masanari; Inoue, Y.; Ikari, Daisuke; Miyake, Yoichi

doi:10.1109/sii.2012.6427358

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…For this study, we used low-level sensor data. Although our study suggests a coevolution of nods in laboratory settings [40], it is yet unclear which upper-body movements, such as nods, hand gestures, and trunk movements, coevolve in a real world setting. A detailed measurement of body movements might reveal the daily coevolution more clearly.…”

Section: Discussionmentioning

confidence: 64%

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Interpersonal Coevolution of Body Movements in Daily Face-to-Face Communication

Ogata

Higo

Nozawa

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYPeople's body movements in daily face-to-face communication influence each other. For instance, during a heated debate, the participants use more gestures and other body movements, while in a calm discussion they use fewer gestures. This "coevolution" of interpersonal body movements occurs on multiple time scales, like minutes or hours. However, the multi-time-scale coevolution in daily communication is not clear yet.In this paper, we explore the minute-to-minute coevolution of interpersonal body movements in daily communication and investigate the characteristics of this coevolution. We present quantitative data on upper-body movements from thousand test subjects from seven organizations gathered over several months via wearable sensors. The device we employed measured upperbody movements with an accelerometer and the duration of face-to-face communication with an infrared ray sensor on a minute-by-minute basis. We defined a coevolution measure between two people as the number of per-minute changes of their body movement and compared the indices for face-to-face and non-face-to-face situations. We found that on average, the amount of people's body movements changed correspondingly for face-toface communication and that the average rate of coevolution in the case of face-to-face communication was 3-4% higher than in the case of nonface-to-face situation. These results reveal minute-to-minute coevolution of upper-body movements between people in daily communication. The finding suggests that the coevolution of body movement arises in multiple time scales.

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Section: Discussionmentioning

confidence: 64%

“…Ono et al investigated the relation between synchronization of body movements and communication context [40]. They revealed that people's nods synchronize when they reach an agreement.…”

Section: Introductionmentioning

confidence: 99%

Interpersonal Coevolution of Body Movements in Daily Face-to-Face Communication

Ogata

Higo

Nozawa

et al. 2017

IEICE Trans. Inf. & Syst.

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“…In the case of lower acquisition frequency, around 10-20 Hz, the proposed studies focused on determining head motion patterns, which has applicability in medical fields [28,40]. The most common acquisition frequency was observed to be within a range of 48 Hz to 100 Hz, with diverse applicability (general HCI solutions, physical and mental activity analyses, sports training, and medical) [23,36,41,42,47,62,[64][65][66]. Another range which we determined had been used in the papers reviewed was an acquisition frequency of 120 Hz to 48 kHz.…”

Section: Head Motion Systemsmentioning

confidence: 99%

“…In head motion recognition (HMR) systems, the most common approach is characterized by digital and statistical filters [30,33,34,48,50], data normalization [41], and feature extraction [69][70][71]. The feature extraction step explores two domains: time [66] and frequency [43]. Approaches based on handling inertial signals in the time domain are used the most, because these have a small computational time compared with the frequency domain.…”

Section: Preprocessing and Feature Extractionmentioning

confidence: 99%

Using Inertial Sensors to Determine Head Motion—A Review

Severin

Dobrea

2021

J. Imaging

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Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.

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“…For instance, one study showed that if an interviewer nods in response to an interviewee's remarks, the interviewee seems to talk more and to become more confident [5] . Another study showed that when two individuals have a conversation, they nod in synchronization when a context is shared [6] . Although the causality of the relationship between the synchronization of nods and the sharing of contexts has yet to be revealed, other studies suggest that the synchronization of body movement is based on smooth communication through shared contexts [7] – [13] .…”

Section: Introductionmentioning

confidence: 99%

Interpersonal Similarity between Body Movements in Face-To-Face Communication in Daily Life

et al. 2014

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Individuals are embedded in social networks in which they communicate with others in their daily lives. Because smooth face-to-face communication is the key to maintaining these networks, measuring the smoothness of such communication is an important issue. One indicator of smoothness is the similarity of the body movements of the two individuals concerned. A typical example noted in experimental environments is the interpersonal synchronization of body movements such as nods and gestures during smooth face-to-face communication. It should therefore be possible to estimate quantitatively the smoothness of face-to-face communication in social networks through measurement of the synchronization of body movements. However, this is difficult because social networks, which differ from disciplined experimental environments, are open environments for the face-to-face communication between two individuals. In such open environments, their body movements become complicated by various external factors and may follow unstable and nonuniform patterns. Nevertheless, we consider there to be some interaction during face-to-face communication that leads to the interpersonal synchronization of body movements, which can be seen through the interpersonal similarity of body movements. The present study aims to clarify such interaction in terms of body movements during daily face-to-face communication in real organizations of more than 100 people. We analyzed data on the frequency of body movement for each individual during face-to-face communication, as measured by a wearable sensor, and evaluated the degree of interpersonal similarity of body movements between two individuals as their frequency difference. Furthermore, we generated uncorrelated data by resampling the data gathered and compared these two data sets statistically to distinguish the effects of actual face-to-face communication from those of the activities accompanying the communication. Our results confirm an interpersonal similarity of body movements between two individuals in face-to-face communication, for all the organizations studied, and suggest that some body interaction is behind this similarity.

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Relation between synchronization of head movements and degree of understanding on interpersonal communication

Cited by 7 publications

References 6 publications

Interpersonal Coevolution of Body Movements in Daily Face-to-Face Communication

Interpersonal Coevolution of Body Movements in Daily Face-to-Face Communication

Using Inertial Sensors to Determine Head Motion—A Review

Interpersonal Similarity between Body Movements in Face-To-Face Communication in Daily Life

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