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Papers - Ogawa, Ami

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11099 PC 内蔵カメラを用いたリアルタイム集中力推定手法の提案と評価

福場凜太郎，小川愛実，三田彰

情報システム技術, 209-210 ( 2020 ) 209 - 210 2020

11098 非接触かつマーカレスなセンサを用いた関節トルクの推定および座位姿勢の力学的評価

國分萌々子，小川愛実，三田彰

情報システム技術, 207-208 ( 2020 ) 207 - 208 2020

床反力データを用いない下肢関節角度を入力とした膝関節モーメント推定

小川愛実，三田彰，佐藤貢一，石井喬之

Dynamics & Design Conference 2019, 458 (The Japan Society of Mechanical Engineers) 2019 458 2019

The joint moment is effective for quantitative physical load evaluation. We normally solve the joint moment by inverse dynamics with ground reaction force. However, a force plate which acquires ground reaction force data is too expensive for daily use. In this paper, we present a simple model for joint moment calculation based on the slope deflection method which does not require ground reaction force. We focused on a knee joint for basic study as it can be assumed to be a hinge joint. We estimated the knee joint moment during steady state walking and validated with three-dimensional motion analysis and force plate.

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たわみ角法を用いた定常歩行時の膝関節モーメントの推定に関する基礎検討

小川愛実，三田彰

生体医工学, S233_2-S233_2 (公益社団法人日本生体医工学会) Annual57 ( Abstract ) S233_2 - S233_2 2019

ISSN 1347-443X

高齢化に伴い予防医療の重要性が高まっている。生活自立度の維持において特に重要な運動機能の評価指標として歩行評価が広く実施されている。中でも歩行時の関節モーメントは部位毎の筋活動を反映しており、各関節にかかる負荷を定量的に示すことが可能である。関節モーメントは一般に床反力計および三次元動作解析装置を用いて計測される床反力情報と関節角度情報を剛体リンクモデルに代入することで求められる。しかし、医療機関などの計測環境では自然な歩行を取得することが難しく、日常的に行われる歩行を把握することが困難である。そこで本研究では居住空間で日常的に関節モーメントの計測を行うため、居住空間への導入が困難な床反力計を用いない手法を提案する。建築の構造計算に用いられるたわみ角法を用いた関節モーメント推定手法を新たに考案し、その基礎検討として膝関節モーメントにおいて従来手法による推定値と比較を行った。提案モデルでは関節角度のみを入力として膝関節モーメントを推定可能であり、三次元動作解析装置と床反力計から算出した値と高い相関を示した。今後は被験者を増やし、更なる検討を重ねたい。

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居住者の健康な生活を支援するための歩行パラメタ推定に関する研究 (本文)

小川愛実

慶應義塾大学 2019

11069 非接触センサを用いた定常歩行動作および立ち上がり動作の予測

小川愛実，木村洋介，三田彰

情報システム技術, 153-154 ( 2019 ) 153 - 154 2019

Usability evaluation of a gait monitoring robot in a living space

A Ogawa, A Mita

Journal of Environmental Engineering (Japan) 83 (752), 801-810 (Journal of Environmental Engineering (Japan)) 83 ( 752 ) 801 - 810 2018.10

Research paper (scientific journal), Joint Work, Accepted, ISSN 13480685

© 2018 Architectural Institute of Japan. All rights reserved. We have been suggesting a gait monitoring system using a small sensor agent robot. This system estimates knee joint positions and heel positions in order to detect the decline of the residents' physical performances in an early stage in living spaces. However, its accuracy has not been evaluated, and its performance in a real living environment has not been tested. In this paper, (1) we examined the important gait parameters to be monitored, (2) evaluated our system using a 3D motion analysis, and (3) considered the usability of our system in a real living environment.

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11037 居住空間における見守りロボットの人追随に関する研究

小川愛実，三田彰

情報システム技術, 73-74 ( 2018 ) 73 - 74 2018

居住空間における歩行見守りロボットの有用性の検証

小川愛実，三田彰

日本建築学会環境系論文集 83 (752), 801-810 2018

Performance of a Sensor Agent Robot in a Real Living Environment for Monitoring in Houses

A Ogawa, A Mita

Proceeding of International Conference on Smart, Sustainable and Sensuous … 2018

Markerless knee joint position measurement using depth data during stair walking

Ami Ogawa, Akira Mita, Ayanori Yorozu, Masaki Takahashi

Sensors (Switzerland) 17 ( 11 ) 2017.11

Accepted, ISSN 1424-8220

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Climbing and descending stairs are demanding daily activities, and the monitoring of them may reveal the presence of musculoskeletal diseases at an early stage. A markerless system is needed to monitor such stair walking activity without mentally or physically disturbing the subject. Microsoft Kinect v2 has been used for gait monitoring, as it provides a markerless skeleton tracking function. However, few studies have used this device for stair walking monitoring, and the accuracy of its skeleton tracking function during stair walking has not been evaluated. Moreover, skeleton tracking is not likely to be suitable for estimating body joints during stair walking, as the form of the body is different from what it is when it walks on level surfaces. In this study, a new method of estimating the 3D position of the knee joint was devised that uses the depth data of Kinect v2. The accuracy of this method was compared with that of the skeleton tracking function of Kinect v2 by simultaneously measuring subjects with a 3D motion capture system. The depth data method was found to be more accurate than skeleton tracking. The mean error of the 3D Euclidian distance of the depth data method was 43.2 ± 27.5 mm, while that of the skeleton tracking was 50.4 ± 23.9 mm. This method indicates the possibility of stair walking monitoring for the early discovery of musculoskeletal diseases.

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A face recognition system for automated door opening with parallel health status validation using the kinect v2

A. Ogawa, A. Mita, C. Georgoulas, T. Bock

ISARC 2016 - 33rd International Symposium on Automation and Robotics in Construction 132 - 140 2016

Accepted

Nowadays it is said that extending the healthy life expectancy is important in the aging society target group. Of course it is important for every singleperson household to be regularly checked upon their safety, but especially for elderly people the importance is higher at the point of healthy living, because this particular target group comprises a higher accidents rate. Therefore, the authors here propose the design and implementation of an unobtrusive vision system for single-person households, particularly for elderly people, based on the Microsoft Kinect v2. The entrance area of the home environment is here considered, where the monitoring system is activated upon while the user approaches, in order to first detect and calculate the user's fatigue levels, as well as to secondly identify the user by facial recognition, in order to actuate a door opening mechanism. The proposed system algorithm is divided into two distinct phases. The first phase concerns the acquisition of the user knee position while walking up a staircase in order to calculate fatigue. The second phase comprises the face recognition based door opening method. The proposed system has been successfully tested, and it could undoubtedly comprise an unobtrusive health status validation and automated door opening solution for elderly people in single-person households.

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Human activity recognition based on the activity categorization using depth data for "biofied building"

Ami Ogawa, Akira Mita

Journal of Environmental Engineering (Japan) (Architectural Institute of Japan) 81 ( 722 ) 403 - 410 2016

Accepted, ISSN 1348-0685

The number of single households is gradually increasing in Japan. The majority of them are consisting of elderly people. The "Biofied Building" that can monitor and control living spaces will help those people in many aspects. One of the key items to be acquired is the information about the residents activity. In this paper, we suggest an activity recognition method for two categories of activities, "Single type" and "Multiple type." We use the consecutive depth data obtained by Kinect. The R transformation, variance, PCA, LDA, and k-means classification are used for classification. Ten activities data sets consisting of six "Single type" and four "Multiple type" conducted by eleven subjects are used for verification.

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Gait parameters extraction by using mobile robot equipped with Kinect v2

Ami Ogawa, Akira Mita, Ayanori Yorozu, Masaki Takahashi

Proceedings of SPIE - The International Society for Optical Engineering 9803 2016

Accepted, ISSN 0277-786X

© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. The needs for monitoring systems to be used in houses are getting stronger because of the increase of the single household population due to the low birth rate and longevity. Among others, gait parameters are under the spotlight to be examined as the relations with several diseases have been reported. It is known that the gait parameters obtained at a walk test are different from those obtained under the daily life. Thus, the system which can measure the gait parameters in the real living environment is needed. Generally, gait abilities are evaluated by a measurement test, such as Timed Up and Go test and 6-minute walking test. However, these methods need measurers, so the accuracy depends on them and the lack of objectivity is pointed out. Although, a precise motion capture system is used for more objective measurement, it is hard to be used in daily measurement, because the subjects have to put the markers on their body. To solve this problem, marker less sensors, such as Kinect, are developed and used for gait information acquisition. When they are attached to a mobile robot, there is no limitation of distance. However, they still have challenges of calibration for gait parameters, and the important gait parameters to be acquired are not well examined. Therefore, in this study, we extract the important parameters for gait analysis, which have correlations with diseases and age differences, and suggest the gait parameters extraction from depth data by Kinect v2 which is mounted on a mobile robot aiming at applying to the living environment.

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Consistency verification of marker-less gait assessment system for stair walking

Ami Ogawa, Ayanori Yorozu, Akira Mita, Masaki Takahashi, Christos Georgoulas, Thomas Bock

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9656 653 - 663 2016

Accepted, ISSN 0302-9743

© Springer International Publishing Switzerland 2016. The number of elderly people is drastically increasing. To support them, the gait information is under the spotlight since it has the relationship between the fall risk and dementia. Among other scenarios, a relatively higher level of ability is needed for the stair walking as it requires balancing and loading. Conventionally, 3D motion capture devices have been used to acquire the parameters of stair walking. However, it is difficult to acquire daily parameters as the equipment needs complicated preparation and body-worn markers. In this study, we propose a system which can acquire daily stair walking parameters using only depth data obtained by Kinect v2 without restraining by markers. We confirmed the accuracy of our proposed system compared with a 3D motion capture system.

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Recognition of human activities using depth images of Kinect for biofied building

Ami Ogawa, Akira Mita

Proceedings of SPIE - The International Society for Optical Engineering 9435 2015

Accepted, ISSN 0277-786X

© 2015 SPIE. These days, various functions in the living spaces are needed because of an aging society, promotion of energy conservation, and diversification of lifestyles. To meet this requirement, we propose "Biofied Building". The "Biofied Building" is the system learnt from living beings. The various information is accumulated in a database using small sensor agent robots as a key function of this system to control the living spaces. Among the various kinds of information about the living spaces, especially human activities can be triggers for lighting or air conditioning control. By doing so, customized space is possible. Human activities are divided into two groups, the activities consisting of single behavior and the activities consisting of multiple behaviors. For example, "standing up" or "sitting down" consists of a single behavior. These activities are accompanied by large motions. On the other hand "eating" consists of several behaviors, holding the chopsticks, catching the food, putting them in the mouth, and so on. These are continuous motions. Considering the characteristics of two types of human activities, we individually, use two methods, R transformation and variance. In this paper, we focus on the two different types of human activities, and propose the two methods of human activity recognition methods for construction of the database of living space for "Biofied Building". Finally, we compare the results of both methods.

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