Sensors and Materialsに下記4件の論文が採択され、8月2日にてパブリッシュされました。
Kazuki Oi; Yugo Nakamura; Yuki Matsuda; Manato Fujimoto; Keiichi Yasumoto
Inertial Measurement Unit-sensor-based Short Stick Exercise Tracking to Improve Health of Elderly People Journal Article
In: Sensors and Materials, vol. 34, no. 8, pp. 2911-2928, 2022.
@article{sam2022_oi,
title = {Inertial Measurement Unit-sensor-based Short Stick Exercise Tracking to Improve Health of Elderly People},
author = {Kazuki Oi and Yugo Nakamura and Yuki Matsuda and Manato Fujimoto and Keiichi Yasumoto},
doi = {10.18494/SAM3968},
year = {2022},
date = {2022-08-02},
journal = {Sensors and Materials},
volume = {34},
number = {8},
pages = {2911-2928},
abstract = {Short stick exercises have been attracting attention from the viewpoint of preventing falls and improving the health of elderly people and are generally performed under the guidance of instructors and nursing staff at nursing homes. However, in situations such as the COVID-19 pandemic, where people should refrain from unnecessary outings, it is advisable that individuals perform short stick exercises at home and record their exercise implementation status. In this paper, we propose an inertial measurement unit (IMU)-sensor-based short stick exercise tracking method that can automatically record the types and amounts of exercises performed using a short stick equipped with an IMU sensor. The proposed method extracts time-domain and frequency-domain features from linear acceleration and quaternion time-series data obtained from the IMU sensor and classifies the type of exercise using an inference model based on machine learning algorithms. To evaluate the proposed method, we collected sensor data from 21 young subjects (in their 20s) and 14 elderly subjects (79\textendash95 years old), where the participants performed three sets (10 times per set) of eight basic types of short stick exercises (five types for elderly people). As a result of evaluating the proposed method using this data set, we confirmed that when LightGBM was used as the learning algorithm, it achieved F values of 90.0% and 86.6% for recognizing the type of exercise for young and elderly people, respectively.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Short stick exercises have been attracting attention from the viewpoint of preventing falls and improving the health of elderly people and are generally performed under the guidance of instructors and nursing staff at nursing homes. However, in situations such as the COVID-19 pandemic, where people should refrain from unnecessary outings, it is advisable that individuals perform short stick exercises at home and record their exercise implementation status. In this paper, we propose an inertial measurement unit (IMU)-sensor-based short stick exercise tracking method that can automatically record the types and amounts of exercises performed using a short stick equipped with an IMU sensor. The proposed method extracts time-domain and frequency-domain features from linear acceleration and quaternion time-series data obtained from the IMU sensor and classifies the type of exercise using an inference model based on machine learning algorithms. To evaluate the proposed method, we collected sensor data from 21 young subjects (in their 20s) and 14 elderly subjects (79–95 years old), where the participants performed three sets (10 times per set) of eight basic types of short stick exercises (five types for elderly people). As a result of evaluating the proposed method using this data set, we confirmed that when LightGBM was used as the learning algorithm, it achieved F values of 90.0% and 86.6% for recognizing the type of exercise for young and elderly people, respectively.
Haruki Harashima; Shuta Matsuo; Yutaka Arakawa
Effect of Circadian Rhythm Control Light on Sleep State and Mental Health of Students Journal Article
In: Sensors and Materials, vol. 34, no. 8, pp. 2973-2983, 2022.
@article{sam2022_harashima,
title = {Effect of Circadian Rhythm Control Light on Sleep State and Mental Health of Students},
author = {Haruki Harashima and Shuta Matsuo and Yutaka Arakawa},
doi = {10.18494/SAM3952},
year = {2022},
date = {2022-08-02},
journal = {Sensors and Materials},
volume = {34},
number = {8},
pages = {2973-2983},
abstract = {Many office workers are exposed to fluorescent light from mid-morning to after dark. The changes in the amount of light in the natural world, from bright during the day to dark at night, play an important role in maintaining a regular daily rhythm. In this study, we evaluated the effects of LED lighting equipped with a circadian rhythm control function, Lavigo, by comparing it with conventional fluorescent lighting on students’ sleep quality and stress. Lavigo’s Visual Timing Light (VTL) function adjusts the color and brightness of light according to the biological rhythm of about 24 h cycles. We measured the sleep state using Fitbit and collected subjective responses through a stress-checking questionnaire. A salivary amylase monitor was used to measure objective stress values from a medical perspective. No significant difference in stress values was observed during the experiment. However, only during the week that Lavigo was used, a certain subject showed a significant decrease in waking time during sleep, and another subject showed an increase in deep sleep. It was also found that these subjects tended to lead an irregular life, such as staying up until midnight.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Many office workers are exposed to fluorescent light from mid-morning to after dark. The changes in the amount of light in the natural world, from bright during the day to dark at night, play an important role in maintaining a regular daily rhythm. In this study, we evaluated the effects of LED lighting equipped with a circadian rhythm control function, Lavigo, by comparing it with conventional fluorescent lighting on students’ sleep quality and stress. Lavigo’s Visual Timing Light (VTL) function adjusts the color and brightness of light according to the biological rhythm of about 24 h cycles. We measured the sleep state using Fitbit and collected subjective responses through a stress-checking questionnaire. A salivary amylase monitor was used to measure objective stress values from a medical perspective. No significant difference in stress values was observed during the experiment. However, only during the week that Lavigo was used, a certain subject showed a significant decrease in waking time during sleep, and another subject showed an increase in deep sleep. It was also found that these subjects tended to lead an irregular life, such as staying up until midnight.
Chenhao Chen; Yutaka Arakawa; Ko Watanabe; Shoya Ishimaru
Quantitative Evaluation System for Online Meetings Based on Multimodal Microbehavior Analysis Journal Article
In: Sensors and Materials, vol. 34, no. 8, pp. 3017-3027, 2022.
@article{sam2022_chen,
title = {Quantitative Evaluation System for Online Meetings Based on Multimodal Microbehavior Analysis},
author = {Chenhao Chen and Yutaka Arakawa and Ko Watanabe and Shoya Ishimaru},
doi = {10.18494/SAM3959},
year = {2022},
date = {2022-08-02},
journal = {Sensors and Materials},
volume = {34},
number = {8},
pages = {3017-3027},
abstract = {Maintaining a positive interaction is the key to a healthy and efficient meeting. Aiming to improve the quality of online meetings, we present an end-to-end neural-network-based system, named MeetingPipe, which is capable of quantitative microbehavior detection (smiling, nodding, and speaking) from recorded meeting videos. For smile detection, we build a neural network framework that consists of an 18-layer residual network for feature representation, and a self-attention layer to explore the correlation between each receptive field. To perform nodding detection, we obtain head rotation data as the key nodding feature. Then we use a gated recurrent unit followed by a squeeze-and-excitation mechanism to capture the temporal information of nodding patterns from head pitch angles. In addition, we utilize TalkNet, an active speaker detection model, which can effectively recognize active speakers from videos. Experiments demonstrate that with K-fold cross validation, the F1 scores of the smile, nodding, and speaking detection are 97.34, 81.26, and 94.90%, respectively. The processing can be accelerated with multiple GPUs due to the multithread design. The code is available at https://github.com/humanophilic/MeetingPipe.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Maintaining a positive interaction is the key to a healthy and efficient meeting. Aiming to improve the quality of online meetings, we present an end-to-end neural-network-based system, named MeetingPipe, which is capable of quantitative microbehavior detection (smiling, nodding, and speaking) from recorded meeting videos. For smile detection, we build a neural network framework that consists of an 18-layer residual network for feature representation, and a self-attention layer to explore the correlation between each receptive field. To perform nodding detection, we obtain head rotation data as the key nodding feature. Then we use a gated recurrent unit followed by a squeeze-and-excitation mechanism to capture the temporal information of nodding patterns from head pitch angles. In addition, we utilize TalkNet, an active speaker detection model, which can effectively recognize active speakers from videos. Experiments demonstrate that with K-fold cross validation, the F1 scores of the smile, nodding, and speaking detection are 97.34, 81.26, and 94.90%, respectively. The processing can be accelerated with multiple GPUs due to the multithread design. The code is available at https://github.com/humanophilic/MeetingPipe.
Yuki Matsuda; Shogo Kawanaka; Hirohiko Suwa; Yutaka Arakawa; Keiichi Yasumoto
ParmoSense: Scenario-based Participatory Mobile Urban Sensing Platform with User Motivation Engine Journal Article
In: Sensors and Materials, vol. 34, no. 8, pp. 3063-3091, 2022.
@article{sam2022_matsuda,
title = {ParmoSense: Scenario-based Participatory Mobile Urban Sensing Platform with User Motivation Engine},
author = {Yuki Matsuda and Shogo Kawanaka and Hirohiko Suwa and Yutaka Arakawa and Keiichi Yasumoto},
doi = {10.18494/SAM3961},
year = {2022},
date = {2022-08-02},
journal = {Sensors and Materials},
volume = {34},
number = {8},
pages = {3063-3091},
abstract = {The rapid proliferation of mobile devices with various sensors has enabled participatory mobile sensing (PMS). Several PMS platforms suffer from open issues including the limited use of their functions to a specific scenario/case and the necessity of technical knowledge for organizers. This paper proposes a novel PMS platform named ParmoSense for easy and flexible data collection. To reduce the burden on both organizers and participants, we employ two novel features. First, essential PMS functions implemented as modules can be easily chosen and combined for sensing in different scenarios. Second, the scenario-based description feature allows organizers to easily and quickly prepare a new instance of PMS and enable people to easily participate in the sensing. It also provides multiple functions to motivate participants for sustainable operation. Through a performance comparison with existing PMS platforms, we confirmed that ParmoSense shows the best cost performance in terms of the workload for preparation and the variety of functions. In addition, to evaluate the availability and usability of ParmoSense, we conducted 19 case studies over four years with ordinary citizens. As the result of a questionnaire survey carried out during the case studies, we confirmed that ParmoSense can be easily operated by ordinary citizens without technical skills.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The rapid proliferation of mobile devices with various sensors has enabled participatory mobile sensing (PMS). Several PMS platforms suffer from open issues including the limited use of their functions to a specific scenario/case and the necessity of technical knowledge for organizers. This paper proposes a novel PMS platform named ParmoSense for easy and flexible data collection. To reduce the burden on both organizers and participants, we employ two novel features. First, essential PMS functions implemented as modules can be easily chosen and combined for sensing in different scenarios. Second, the scenario-based description feature allows organizers to easily and quickly prepare a new instance of PMS and enable people to easily participate in the sensing. It also provides multiple functions to motivate participants for sustainable operation. Through a performance comparison with existing PMS platforms, we confirmed that ParmoSense shows the best cost performance in terms of the workload for preparation and the variety of functions. In addition, to evaluate the availability and usability of ParmoSense, we conducted 19 case studies over four years with ordinary citizens. As the result of a questionnaire survey carried out during the case studies, we confirmed that ParmoSense can be easily operated by ordinary citizens without technical skills.