A method for structuring meeting logs using wearable sensors

Ohnishi, Ayumi; Murao, Kazuya; Terada, Tsutomu; Tsukamoto, Masahiko

doi:10.1016/j.iot.2019.01.005

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…This approach allowed collecting nodding recognition of 76.4% and head shaking recognition of 80.0% [ 39 ]. A. Onishi and Terada used the IMU sensor to collect participant’s head movements [ 40 ]. They achieved the recognition of 97.5% in utterance, 52.4% in nodding , and 53.6% for looking around actions.…”

Section: Related Workmentioning

confidence: 99%

DisCaaS: Micro Behavior Analysis on Discussion by Camera as a Sensor

Watanabe

Soneda

Matsuda

et al. 2021

Sensors

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The emergence of various types of commercial cameras (compact, high resolution, high angle of view, high speed, and high dynamic range, etc.) has contributed significantly to the understanding of human activities. By taking advantage of the characteristic of a high angle of view, this paper demonstrates a system that recognizes micro-behaviors and a small group discussion with a single 360 degree camera towards quantified meeting analysis. We propose a method that recognizes speaking and nodding, which have often been overlooked in existing research, from a video stream of face images and a random forest classifier. The proposed approach was evaluated on our three datasets. In order to create the first and the second datasets, we asked participants to meet physically: 16 sets of five minutes data from 21 unique participants and seven sets of 10 min meeting data from 12 unique participants. The experimental results showed that our approach could detect speaking and nodding with a macro average f1-score of 67.9% in a 10-fold random split cross-validation and a macro average f1-score of 62.5% in a leave-one-participant-out cross-validation. By considering the increased demand for an online meeting due to the COVID-19 pandemic, we also record faces on a screen that are captured by web cameras as the third dataset and discussed the potential and challenges of applying our ideas to virtual video conferences.

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Section: Related Workmentioning

confidence: 99%

DisCaaS: Micro Behavior Analysis on Discussion by Camera as a Sensor

Watanabe

Soneda

Matsuda

et al. 2021

Sensors

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“…The identification of human body postures through wearable devices has become feasible. However, for human activity recognition, single or individual types of sensors have certain limitations ( Mathie et al, 2003 , Cheng and Jhan, 2012 , Ohnishi et al, 2019 , Catal et al, 2015 ). Previous studies have mainly focused on coarse-grained activities such as walking, running, and sitting ( Qiu et al, 2022 ), with relatively fewer studies on complex sports activities.…”

Section: Introductionmentioning

confidence: 99%

Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors

Wang,

Tang,

Xiao

et al. 2024

Preventive Medicine Reports

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“…For the last decades, the advances in consumer-directed wearable biosensing devices have shown that the measurement of egocentric views, cardiac activity, and body movement in the wild is not only possible, but that the general public is open to adopt such technology for everyday monitoring of physical health and activity (e.g., [ 86 , 87 , 88 ]), but also in various professional settings (e.g., [ 89 , 90 , 91 ]). Although such technology has been predominantly developed for adults, its ubiquitousness suggests that similar technologies for infants and young children could be received with a fair degree of openness for researching development in the ‘wild’.…”

Section: Introductionmentioning

confidence: 99%

EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory–Visual Statistics and Autonomic Nervous System Function ‘in the Wild’

Geangu,

Smith,

Mason

et al. 2023

Sensors

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There have been sustained efforts toward using naturalistic methods in developmental science to measure infant behaviors in the real world from an egocentric perspective because statistical regularities in the environment can shape and be shaped by the developing infant. However, there is no user-friendly and unobtrusive technology to densely and reliably sample life in the wild. To address this gap, we present the design, implementation and validation of the EgoActive platform, which addresses limitations of existing wearable technologies for developmental research. EgoActive records the active infants’ egocentric perspective of the world via a miniature wireless head-mounted camera concurrently with their physiological responses to this input via a lightweight, wireless ECG/acceleration sensor. We also provide software tools to facilitate data analyses. Our validation studies showed that the cameras and body sensors performed well. Families also reported that the platform was comfortable, easy to use and operate, and did not interfere with daily activities. The synchronized multimodal data from the EgoActive platform can help tease apart complex processes that are important for child development to further our understanding of areas ranging from executive function to emotion processing and social learning.

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A method for structuring meeting logs using wearable sensors

Cited by 8 publications

References 2 publications

DisCaaS: Micro Behavior Analysis on Discussion by Camera as a Sensor

DisCaaS: Micro Behavior Analysis on Discussion by Camera as a Sensor

Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors

EgoActive: Integrated Wireless Wearable Sensors for Capturing Infant Egocentric Auditory–Visual Statistics and Autonomic Nervous System Function ‘in the Wild’

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