작업 관련 근골격계 질환의 위험성 저감을 위한 작업 자세 및 동작의 인간공학 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022.2. 박우진.육체적 부하가 큰 자세 및 동작으로 작업을 수행하는 것은 작업자의 근골격계 질환의 위험성을 증가시킨다. 작업자의 근골격계에 가해지는 육체적 부하의 양상은 수행하는 작업의 종류에 따라 달라진다. 장시간 앉은 자세로 작업을 수행하는 경우, 작업자의 근육, 인대와 같은 연조직에 과도한 부하가 발생하여 목, 허리 등 다양한 신체 부위에서 근골격계 질환의 위험성이 증가할 수 있다. 따라서, 착좌 시 발생할 수 있는 근골격계 질환의 위험성을 저감하기 위해서는 작업자의 착좌 자세를 실시간으로 모니터링하고, 이에 대한 피드백을 제공하는 것이 필요하다. 들기 작업과 같은 동적인 움직임이 포함된 작업을 수행하는 경우, 작업자의 체중이 신체적 부하에 영향을 미칠 수 있다. 전세계적인 비만의 유행으로 인해 많은 작업자들이 체중 증가를 겪고 있고, 들기 작업과 같은 동적인 작업에서 비만은 신체적 부하에 악영향을 미칠 수 있다. 따라서, 비만과 작업 관련 근골격계 질환의 위험성은 잠재적인 연관성을 가지고 있고, 비만이 들기 작업에 미치는 생체역학적 영향을 논의할 필요성이 있다. 작업장에서의 근골격계 질환의 위험성을 저감하기 위해 다양한 연구들이 수행되어 왔지만, 작업 시스템의 인간공학적 설계 측면에서 추가적인 연구가 필요하다. 장시간 의자에 앉아 정적인 작업을 수행하는 작업자의 근골격계 질환을 저감하기 위한 유망한 방법 중 하나로, 작업자의 자세를 실시간으로 모니터링하고 분류하는 시스템을 개발하는 것이 제안되고 있다. 이러한 시스템은 작업자가 근골격계 질환의 위험성이 낮은 자세를 작업 시간 동안 유지하도록 돕는 데 활용될 수 있을 것이다. 기존의 대부분의 자세 모니터링 시스템에서는 분류할 자세를 정의하는 과정에서 인간공학적 문헌이 거의 고려되지 않았고, 사용자가 실제로 활용하기에는 여러 한계점들이 존재하였다. 들기 작업의 경우, 체질량 지수(BMI) 40 이상의 초고도 비만 작업자의 동작 패턴을 논의한 연구는 거의 찾아볼 수 없었다. 또한, 다양한 들기 작업 조건 하에서 전신 관절들의 움직임을 생체역학적 측면에서 분석한 연구는 부족한 실정이다. 따라서, 본 연구에서의 연구 목적은 1) 다양한 센서 조합을 활용한 실시간 착좌 자세를 분류하는 시스템을 개발하고, 2) 들기 작업 시 초고도 비만이 개별 관절의 움직임과 들기 동작 패턴에 미치는 영향을 이해하여, 다양한 종류의 작업에서 발생할 수 있는 근골격계 질환의 위험성을 저감하는 것이다. 연구 목적을 달성하기 위해 다음의 두 가지 연구를 수행하였다. 첫번째 연구에서는 실시간으로 착좌 자세를 분류하는 스마트 의자 시스템을 개발하였다. 스마트 의자 시스템은 각각 여섯 개의 거리 센서와 압력 센서를 조합하여 구성되었다. 착좌 관련한 근골격계 질환에 대해 문헌 조사를 수행하였고, 이를 바탕으로 결정된 자세들에 대해 서른 여섯 명의 데이터를 수집하였다. 스마트 의자 시스템에서 자세를 분류하기 위해 kNN 알고리즘을 활용하였고, 성능을 검증하기 위해 단일 종류의 센서로 구성된 기준 모델들과 비교를 수행하였다. 분류 성능을 비교한 결과, 센서를 조합한 스마트 의자 시스템이 가장 우수한 결과를 보였다. 두번째 연구에서는 들기 작업을 수행할 때 초고도 비만이 개별 관절의 움직임과 동작 패턴에 미치는 영향을 분석하기 위해 모션 캡쳐 실험을 수행하였다. 들기 실험에는 근골격계 질환 이력이 없는 서른 다섯 명이 참여하였다. 수집된 데이터를 바탕으로 주요 관절(발목, 무릎, 엉덩이, 허리, 어깨, 팔꿈치) 별 운동역학적 변수들과, 들기 동작의 패턴을 표현하는 동작 지수들을 계산하였다. 들기 작업 조건과 비만 수준에 따라, 대부분의 변수에서 통계적으로 유의한 차이를 보였다. 전체적으로 비만인은 정상체중인에 비해 다리 보다 허리를 사용하여 들기 작업을 수행하였고, 동작 수행 시 상대적으로 적은 관절 각도 변화와 느린 움직임을 보였다. 들기 작업에서 박스의 이동에 개별 관절이 기여하는 비율도 정상체중인과 비만인은 다른 패턴을 보였다. 본 연구의 결과를 활용하여 다양한 종류의 신체적 부하에 노출된 작업자들의 근골격계 질환의 위험성을 저감할 수 있고, 궁극적으로 업무의 생산성과 개인의 건강을 제고할 수 있을 것이다. 첫번째 연구에서 개발된 스마트 의자 시스템은 기존 자세 분류 시스템의 단점들을 완화하였다. 개발된 시스템은 저렴한 소수의 센서만을 활용하여 근골격계 측면에서 중요한 자세들을 높은 정확도로 분류하였다. 이러한 자세 분류 시스템은 작업자에게 실시간으로 자세 피드백을 제공하여, 근골격계 질환의 위험성이 낮은 자세를 유지하는 데 활용될 수 있을 것이다. 두번째 연구의 결과는 동적인 작업 시 초고도 비만으로 인한 잠재적인 근골격계 질환의 위험성을 이해하는 데 활용될 수 있다. 초고도 비만인과 정상체중인 간 관절의 움직임과 동작의 차이를 이해하여, 비만을 고려한 인간공학적 작업장 설계와 동작 가이드라인을 제공할 수 있을 것이다.Working in stressful postures and movements increases the risk of work-related musculoskeletal disorders (WMSDs). The physical stress on a worker’s musculoskeletal system depends on the type of work task. In the case of sedentary work, stressful sitting postures for prolonged durations could increase the load on soft connective tissues such as muscles and ligaments, resulting in the incidence of WMSDs. Therefore, to reduce the WMSDs, it is necessary to monitor a worker’s sitting posture and additionally provide ergonomic interventions. When the worker performs a task that involves dynamic movements, such as manual lifting, the worker’s own body mass affects the physical stress on the musculoskeletal system. In the global prevalence of obesity in the workforce, an increase in the body weight of the workers could adversely affect the musculoskeletal system during the manual lifting task. Therefore, obesity could be associated with the development of WMSDs, and the impacts of obesity on workers’ movement during manual lifting need to be examined. Despite previous research efforts to prevent WMSDs, there still exist research gaps concerning ergonomics design of work systems. For sedentary workers, a promising solution to reduce the occurrence of WMSDs is the development of a system capable of monitoring and classifying a seated worker's posture in real-time, which could be utilized to provide feedback to the worker to maintain a posture with a low-risk of WMSDs. However, the previous studies in relation to such a posture monitoring system lacked a review of the ergonomics literature to define posture categories for classification, and had some limitations in widespread use and user acceptance. In addition, only a few studies related to obesity impacts on manual lifting focused on severely obese population with a body mass index (BMI) of 40 or higher, and, analyzed lifting motions in terms of multi-joint movement organization or at the level of movement technique. Therefore, the purpose of this study was to: 1) develop a sensor-embedded posture classification system that is capable of classifying an instantaneous sitting posture as one of the posture categories discussed in the ergonomics literature while not suffering from the limitations of the previous system, and, 2) identify the impacts of severe obesity on joint kinematics and movement technique during manual lifting under various task conditions. To accomplish the research objectives, two major studies were conducted. In the study on the posture classification system, a novel smart chair system was developed to monitor and classify a worker’s sitting postures in real-time. The smart chair system was a mixed sensor system utilizing six pressure sensors and six infrared reflective distance sensors in combination. For a total of thirty-six participants, data collection was conducted on posture categories determined based on an analysis of the ergonomics literature on sitting postures and sitting-related musculoskeletal problems. The mixed sensor system utilized a kNN algorithm for posture classification, and, was evaluated in posture classification performance in comparison with two benchmark systems that utilized only a single type of sensors. The mixed sensor system yielded significantly superior classification performance than the two benchmark systems. In the study on the manual lifting task, optical motion capture was conducted to examine differences in joint kinematics and movement technique between severely obese and non-obese groups. A total of thirty-five subjects without a history of WMSDs participated in the experiment. The severely obese and non-obese groups show significant differences in most joint kinematics of the ankle, knee, hip, spine, shoulder, and elbow. There were also significant differences between the groups in the movement technique index, which represents a motion in terms of the relative contribution of an individual joint degree of freedom to the box trajectory in a manual lifting task. Overall, the severely obese group adopted the back lifting technique (stoop) rather than the leg lifting technique (squat), and showed less joint range of excursions and slow movements compared to the non-obese group. The findings mentioned above could be utilized to reduce the risk of WMSDs among workers performing various types of tasks, and, thus, improve work productivity and personal health. The mixed sensor system developed in this study was free from the limitations of the previous posture monitoring systems, and, is low-cost utilizing only a small number of sensors; yet, it accomplishes accurate classification of postures relevant to the ergonomic analyses of seated work tasks. The mixed sensor system could be utilized for various applications including the development of a real-time posture feedback system for preventing sitting-related musculoskeletal disorders. The findings provided in the manual lifting study would be useful in understanding the potential risk of WMSDs for severely obese workers. Differences in joint kinematics and movement techniques between severely obese and non-obese groups provide practical implications concerning the ergonomic design of work tasks and workspace layout.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Objectives 5 1.3 Dissertation Outline 6 Chapter 2. Literature Review 8 2.1 Work-related Musculoskeletal Disorders Among Sedentary Workers 8 2.1.1 Relationship Between Sitting Postures and Musculoskeletal Disorders 8 2.1.2 Systems for Monitoring and Classifying a Seated Worker's Postures 10 2.2 Impacts of Obesity on Manual Works 22 2.2.1 Impacts of Obesity on Work Capacity 22 2.2.2 Impacts of Obesity on Joint Kinematics and Biomechanical Demands 24 Chapter 3. Developing and Evaluating a Mixed Sensor Smart Chair System for Real-time Posture Classification: Combining Pressure and Distance Sensors 27 3.1 Introduction 27 3.2 Materials and Methods 33 3.2.1 Predefined posture categories for the mixed sensor system 33 3.2.2 Physical construction of the mixed sensor system 36 3.2.3 Posture Classifier Design for the Mixed Sensor System 38 3.2.4 Data Collection for Training and Testing the Posture Classifier of the Mixed Sensor System 41 3.2.5 Comparative Evaluation of Posture Classification Performance 43 3.3 Results 46 3.3.1 Model Parameters and Features 46 3.3.2 Posture Classification Performance 47 3.4 Discussion 50 Chapter 4. Severe Obesity Impacts on Joint Kinematics and Movement Technique During Manual Load Lifting 57 4.1 Introduction 57 4.2 Methods 61 4.2.1 Participants 61 4.2.2 Experimental Task 61 4.2.3 Experimental Procedure 64 4.2.4 Data Processing 65 4.2.5 Experimental Variables 67 4.2.6 Statistical Analysis 71 4.3 Results 72 4.3.1 Kinematic Variables 72 4.3.2 Movement Technique Indexes 83 4.4 Discussion 92 Chapter 5. Conclusion 102 5.1 Summary 102 5.2 Implications 105 5.3 Limitations and Future Directions 106 Bibliography 108 국문초록 133박

Pametne uredske stolice sa senzorima za otkrivanje položaja i navika sjedenja – pregled literature

The health consequences of prolonged sitting in the office and other work chairs have recently been tried to be alleviated or prevented by the application of modern technologies. Smart technologies and sensors are installed in different parts of office chairs, which enables monitoring of seating patterns and prevents positions that potentially endanger the health of users. The aim of this paper is to provide an overview of previous research in the field of the application of smart technologies and sensors built into office and other types of chairs in order to prevent diseases. The articles published in the period 2010-2020 and indexed in WoS CC, Scopus, and IEEE Xplore databases, with the keywords “smart chair” and “sensor chair” were analysed. 15 articles were processed, with their research being based on the use of different types of sensors that determine the contact pressures between the user’s body and stool parts and recognise different body positions when sitting, which can prevent negative health consequences. Analysed papers prove that the use of smart technology and a better understanding of sitting, using various sensors and applications that read body pressure and determine the current body position, can act as preventive health care by detecting proper heart rate and beats per minute, the activity of individual muscle groups, proper breathing and estimates of blood oxygen levels. In the future research, it is necessary to compare different types of sensors, methods used and the results obtained in order to determine which of them are most suitable for the future development of seating furniture for work.Posljedice dugotrajnog sjedenja na uredskim i drugim radnim stolicama u posljednje se vrijeme pokušavaju ublažiti ili spriječiti primjenom suvremenih tehnologija. U različite dijelove uredskih stolica ugrađuju se pametne tehnologije i senzori, što omogućuje praćenje rasporeda sjedenja i izbjegavanje položaja koji potencijalno ugrožavaju zdravlje korisnika. Cilj ovog rada jest davanje pregleda dosadašnjih istraživanja u području primjene suvremenih pametnih tehnologija i senzora ugrađenih u uredske i ostale vrste stolica radi prevencije obolijevanja korisnika. Analizirani su članci objavljeni u razdoblju od 2010. do 2020. i indeksirani su u bazama podataka WoS CC, Scopus i IEEE Xplore, a izdvojeni su prema ključnim riječima pametna stolica i senzorska stolica. Obrađeno je 15 članaka u kojima su se istraživanja temeljila na primjeni različitih vrsta senzora koji određuju kontaktne tlakove između korisnikova tijela i dijelova stolice te raspoznaju različite položaje tijela pri sjedenju, čime se mogu prevenirati negativne posljedice za zdravlje. U analiziranim istraživanjima autori su dokazali da primjena pametne tehnologije i bolje razumijevanje sjedenja uporabom različitih senzora i aplikacija kojima se očitava pritisak tijela i određuje njegov trenutačni položaj može preventivno djelovati zahvaljujući praćenju rada srca i broja otkucaja u minuti, aktivnosti pojedinih mišićnih skupina, pravilnog disanja, procjene razine kisika u krvi i sl. U budućim istraživanjima potrebno je usporediti različite tipove senzora, primijenjene metode i dobivene rezultate kako bi se uočilo koji su od njih najprikladniji za budući razvoj radnog namještaja za sjedenje

LifeChair: A Conductive Fabric Sensor-Based Smart Cushion for Actively Shaping Sitting Posture.

The LifeChair is a smart cushion that provides vibrotactile feedback by actively sensing and classifying sitting postures to encourage upright posture and reduce slouching. The key component of the LifeChair is our novel conductive fabric pressure sensing array. Fabric sensors have been explored in the past, but a full sensing solution for embedded real world use has not been proposed. We have designed our system with commercial use in mind, and as a result, it has a high focus on manufacturability, cost-effectiveness and adaptiveness. We demonstrate the performance of our fabric sensing system by installing it into the LifeChair and comparing its posture detection accuracy with our previous study that implemented a conventional flexible printed PCB-sensing system. In this study, it is shown that the LifeChair can detect all 11 postures across 20 participants with an improved average accuracy of 98.1%, and it demonstrates significantly lower variance when interfacing with different users. We also conduct a performance study with 10 participants to evaluate the effectiveness of the LifeChair device in improving upright posture and reducing slouching. Our performance study demonstrates that the LifeChair is effective in encouraging users to sit upright with an increase of 68.1% in time spent seated upright when vibrotactile feedback is activated

인체 동작 및 자세 분석을 위한 심화 학습 인공신경망 설계 및 적용

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업·조선공학부, 2020. 8. 윤명환.Ergonomic research is conducted through observation, measurement, and analysis. Ergonomic research has also been developed due to the development of technologies related to observation, measurement, and analysis. Deep-learning technology is a core technology for artificial intelligence development. Various attempts have been made to complement and replace human capabilities like observation, measurement, and analysis, using deep-learning technologies. This deep-learning technology can be applied to various stages of the ergonomic research process. Therefore, in this research, various attempts were made to prepare methods for applying deep-learning to ergonomic research. This thesis attempted to analysis via deep-learning to various kinds of data, such as numerical data, image data, and video data. Besides, to identify the characteristics of data that can be applied to deep-learning, different data collecting methods were applied. The data types were data collected for deep- learning, data collected without considering deep-learning, and data collected and released by the government. The first research is to detect sitting posture from body pressure distribution data. Back health is closely related to the users sitting posture, so it is crucial to have a good sitting posture when young. In a controlled environment, body pressure distribution image data for seven postures were collected from children. The deep-learning method used for posture classification is a convolutional neural network (CNN). The classification performance of logistic regression and CNN is compared. As a result, CNN showed a 20% improvement over logistic regression in the overall classification performance. The second research is to derive work risk assessments using assembly process videos. The data used in the study were those used in the work risk assessment. The performance was evaluated by applying LSTM, one of the deep- learning methods, to the work risk assessment methods OWAS, RULA, and REBA. As a result, when performing OWAS with deep-learning, it showed better performance than RULA and REBA. The third research estimates the stature from hand dimensions. The data used in this research were investigated and released by the government. In the previous study, the stature was estimated from hand dimensions using linear regression. Linear regression, RNN, and the recursive generalized linear model (RGLM) were applied to compare the performance of stature estimation. As a result, deep learning techniques RNN and RGLM performed better than linear regression. Through three research, it was confirmed that the deep-learning method could replace the existing research method. Although the absolute performance was not excellent, it showed relatively good performance than the existing method. The deep-learning method was different depending on the data format and condition. The performance difference also occurred according to the kind of deep-learning method. If the various cases were not learned, no results were obtained for the missing parts. Therefore, data selection and pre-processing must be preceded while applying deep-learning. In ergonomic research, deep-learning will make it easy to reflect the results of ergonomic research into reality. Deep-learning will not replace the researcher but will broaden the research subjects scope and make the research results widely available.인간공학 연구는 관찰, 측정, 분석을 통해 이루어진다. 관찰, 측정, 분석과 관련된 기술의 발달로 인해 인간공학 연구 역시 발달해 왔다. 딥러닝 기술은 인공지능 개발을 위한 핵심기술이다. 딥러닝을 활용하여 인간의 관찰, 측정, 분석 능력을 보 완하고, 대체 하려는 다양한 시도들이 이루어지고 있다. 이러한 딥러닝은 인간공학 연구 과정의 다양한 단계에 적용될 수 있다. 이에, 본 연구에서는 인간공학 연구에 딥러닝을 응용할 수 있는 방안을 마련하기 위해 다양한 시도를 하였다. 본 연구에서는 수치 데이터, 이미지 데이터, 영상 데이터와 같은 다양한 형태의 데이터를 대상으로 딥러닝을 적용하려는 시도를 하였다. 또한, 딥러닝을 적용할 수 있는 데이터의 특성을 파악하기 위해 데이터 수집형태를 달리 적용했다. 그 데이 터 형태는 딥러닝을 위해 수집된 데이터, 딥러닝을 고려하지 않고 수집된 데이터, 정부가 수집해 공개한 데이터이다. 첫 번째 연구는 체압분포 데이터로부터 앉은 자세를 감지하는 것이다. 허리 건강은 앉은 자세 습관과 밀접하므로, 어렸을 때 좋은 앉은 자세를 갖게 하는 것이 중요하다. 어린이를 대상으로 통제된 환경에서 7가지 자세에 따른 압력분포 이미 지 데이터가 수집되었다. 자세 분류에 사용한 딥러닝 방법은 합성곱 신경망(CNN) 이며, 로지스틱 회귀 (logistic regression)와 그 분류 성능을 비교하였다. 그 결과, 전체 분류 성능에서 CNN이 로지스틱 회귀보다 20%가량 향상을 보여주었다. 두 번째 연구는 조립 공정 영상으로부터 작업 위해도 평가 결과를 도출하는 것 이다. 딥러닝을 위해 준비된 데이터가 아닌, 작업 위해도 평가를 위해 촬영되었던 영상 데이터와 평가 결과를 대상으로 하였다. 작업 위해도 평가를 위해 사용되는 OWAS, RULA, REBA 세 가지 평가 방법에 딥러닝 방법인 LSTM을 적용하여 그 성능을 비교하였다. 그 결과, 딥러닝으로 OWAS 평가를 했을 때, RULA, REBA에 비해 좋은 성능을 보여주었다. 세 번째 연구는 손의 여러 치수로부터 키를 추정하는 것이다. 정부 단위로 조 사하여 공개한 데이터를 대상으로 하였다. 기존 연구에서는 선형회귀를 이용하여 손의 수치로부터 키를 추정하였다. 이에 본 연구에서는 딥러닝 방법인 RNN과 재 귀적 일반화 선형 모형 (RGLM)을 적용하여 그 추정 성능을 비교하였다. 그 결과, RGLM과 RNN은 선형회귀에 비해 좋은 성능을 보여주었다. 세 연구를 통해, 딥러닝 방법이 기존의 연구 방법을 대체할 수 있음을 확인하 였다. 절대적인 성능이 좋지는 않았지만, 기존 방법보다 상대적으로 좋은 성능을 보여주었다. 데이터 형식에 따라 적용할 수 있는 딥러닝 방법이 달랐으며, 딥러닝 방법에 따라서도 성능 차이가 발생했다. 다양한 케이스에 대해 학습이 되지 않은 경우, 누락된 부분에 대해서는 결과를 도출하지 못했다. 따라서, 딥러닝 적용에는 데이터 선별 및 가공이 선행되어야 한다. 인간공학 연구에 있어서, 인간공학 연구 결과물이 딥러닝을 통해 현실에 쉽게 반영될 수 있을 것이다. 딥러닝은 연구자를 대체하는 것이 아니라 연구 대상 범위와 활용 범위를 넓혀줄 것이다.Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Purpose of This Study 4 1.3 Organization of the thesis 5 Chapter 2 Literature Reviews 9 2.1 Sitting Posture 9 2.2 Working Posture Measurement 15 2.3 Anthropometric Dimension Estimation 20 2.4 Deep-learning Application 22 Chapter 3 An Ergonomic Analysis of Seated Posture using a Deep-learning Method 25 3.1 Overview 25 3.2 Data Characteristics 26 3.2.1 Body Pressure Distribution on Seat Cushion 26 3.2.2 Data Collection 27 3.2.3 Data Pre-processing 29 3.3 Data Analysis 32 3.3.1 Convolutional Neural Network 32 3.3.2 Performance Comparison Method 34 3.4 Results 36 3.4.1 Logistic Regression 36 3.4.2 Convolutional Neural Networks 39 3.4.3 Comparison of Logistic Regression Results and Convolutional Neural Networks Results 42 3.5 Discussion 44 Chapter 4 Applying Deep-learning Methods to Human Motion Analysis of Automobile Assembly Tasks 47 4.1 Overview 47 4.2 Data Characteristics 48 4.2.1 Work-related Musculoskeletal Disorders(WMSDs) in FactoryWorkers 48 4.2.2 Data Collection 49 4.2.3 Data Pre-processing 50 4.3 Data Analysis 52 4.4 Results 52 4.4.1 OWAS Prediction Model 52 4.4.2 RULA Prediction Model 53 4.4.3 REBA Prediction Model 54 4.5 Discussion 55 Chapter 5 Estimation of Hand Anthropometric Dimensions Using a Deep-learning Method 59 5.1 Overview 59 5.2 Data Characteristics 60 5.2.1 Size Korea; A National Anthropometric Survey of Korea 60 5.2.2 Hand Anthropometric Measurement Data 61 5.2.3 Data Selection and Hand Dimension 62 5.2.4 Training Data and Test Data 64 5.3 Data Analysis 65 5.4 Result 66 5.4.1 Comparison of Relative Absolute Error(RAE) 68 5.4.2 Comparison of Relative Squared Error(RSE) 70 5.4.3 Comparison of Mean Absolute Percentage Error(MAPE) 72 5.4.4 Comparison of Mean Absolute Scaled Error(MASE) 74 5.4.5 Comparison of Root Mean Square Error(RMSE) 76 5.4.6 Comparison of Mean Absolute Error(MAE) 78 5.4.7 Comparison of Mean Squared Error(MSE) 80 5.4.8 Clustering the Results Along with the Performance 82 5.5 Discussion 83 Chapter 6 Discussion and Conclusions 87 6.1 Summary of findings 87 6.2 Contributions of this study 89 6.3 Limitations and further studies 92 Bibliography 95 Appendix A Confusion Matrix from Chapter III 104 Appendix B Python Code for Chapter III 125 Appendix C Python Code for Chapter IV 129 Appendix D Python Code for Chapter V 141Docto

Investigation into the sitting comfort of train seats by using numerical analysis and psychophysiological evaluation （数値解析と心理生理評価を用いた鉄道車両用シートの座り心地に関する研究）

信州大学(Shinshu university)博士（工学）Thesis山口　穂高. Investigation into the sitting comfort of train seats by using numerical analysis and psychophysiological evaluation （数値解析と心理生理評価を用いた鉄道車両用シートの座り心地に関する研究）. 信州大学, 2015, 博士論文. 博士（工学）, 甲第625号, 平成27年3月20日授与.doctoral thesi

Non-invasive monitoring of vital signs using recliner chair and respiratory pattern analysis

In-home monitoring has the potential to help track health changes for older adults with chronic health conditions, thereby making early treatment possible when exacerbations arise. A recliner chair is often used by older adults, even for sleeping at night, for those with breathing difficulty, neck and back problems, or other pain. Here, we present a sensor system for recliner chairs that can be used to monitor heart rate and respiration rate. The system uses two accelerometers placed strategically to capture these vital signs noninvasively and without direct contact with the body, while at same time being hidden from view. The system was tested with 45 subjects, with an average age of 78.8 years for both upright and reclined configurations of the chair. We also tested the system on 6 different types of recliner models. An accuracy of 99% for heart rate and 93% for respiratory rate was obtained. An analysis of the error distribution patterns according to age, gender and recliner configurations are considered. A validation study of a commercially available sensor, Murata SCA11H, which is primarily designed for use on the bed is tested on the chair and the results are presented in this thesis. We have also developed a measure known as the "Breathing Pattern Index" that can be useful in determining the respiratory health of the occupants on the chair. Initial studies of the effectiveness of this index and algorithm are evaluated and the results are presented. This new system and index have the potential to help in identifying very early health changes and improve health outcomes for older adults.Includes bibliographical reference

Smart system for aircraft passenger neck support

Air travel is becoming increasingly more accessible to people due to the availability of low cost air travel. However, long distance air travel is not a normal activity for human. During air travel, people experience different levels of physiological and psychological discomfort. The discomfort may affect the passenger’s health and feeling. With the rapid development of technology, the comfort of service has become an important issue. Nowadays, comfort is an attribute which is highly demanded by aircraft passengers. The comfort of aircraft passengers depends on different features and the cabin environment during air travel. Seat is one of the important features for the passengers and in which a passenger spends almost all their time during air travel. Different seat aspects have to be seen and taken into account in the comfort model. The research has five goals. First goal, literature research starts with the study on the state of the art and recent development of vehicle seat design which is available in current literature and products. The literature review gives a general idea about the research and the measurement method related to seating comfort and discomfort. Second goal, four surveys were conducted to identify the comfort factor of economy class aircraft passenger, body discomfort for truck driver, body discomfort for economy class aircraft passenger and relationship between seat location and sitting posture. The first survey is to identify and investigate the comfort factors for economy class aircraft passenger seat. Subsequently, survey on the body back sitting discomfort over travel time was conducted for truck driver and economy class aircraft passenger. The third survey is to investigate the relationship of the seat location and sitting posture of passengers in the economy class aircraft cabin. The postures of subjects were observed and recorded based on seven predefined sitting postures. Third goal, we contributed to develop a smart neck support system for economy class aircraft passenger. Our system aims to support and reduce neck muscle stress. A functional and working prototype was built to demonstrate the design concept and to perform experimental validation. Forth goal, we developed a low cost aircraft cabin simulator and we utilized it to validate our developed smart neck support system. The aircraft cabin simulator was built with motion platform and it is able to simulate a broad range of flight procedures. Next, a calibration experiment was conducted to investigate SCM muscle stress in relation to different support conditions, time interval and head rotation angle. Fifth goal, a validation experiment was conducted in the aircraft cabin simulator to evaluate the smart neck support system. The objective and subjective results show that the smart neck support system is able to reduce SCM muscle stress adaptively in a fully automate manner

Development of an approach for interface pressure measurement and analysis for study of sitting

Master'sMASTER OF ENGINEERIN

Intelligent Sitting Posture Classifier for Wheelchair Users

In recent years, there has been growing interest in postural monitoring while seated, thus preventing the appearance of ulcers and musculoskeletal problems in the long term. To date, postural control has been carried out by means of subjective questionnaires that do not provide continuous and quantitative information. For this reason, it is necessary to carry out a monitoring that allows to determine not only the postural status of wheelchair users, but also to infer the evolution or anomalies associated with a specific disease. Therefore, this paper proposes an intelligent classifier based on a multilayer neural network for the classification of sitting postures of wheelchair users. The posture database was generated based on data collected by a novel monitoring device composed of force resistive sensors. A training and hyperparameter selection methodology has been used based on the idea of using a stratified K-Fold in weight groups strategy. This allows the neural network to acquire a greater capacity for generalization, thus allowing, unlike other proposed models, to achieve higher success rates not only in familiar subjects but also in subjects with physical complexions outside the standard. In this way, the system can be used to support wheelchair users and healthcare professionals, helping them to automatically monitor their posture, regardless physical complexions.This work was supported in part by the Ministry of Science and Innovation-StateResearch Agency/Project funded by MCIN/State Research Agency(AEI)/10.13039/501100011033 under Grant PID2020-112667RB-I00,in part by the Basque Government under Grant IT1726-22, and in part by the Predoctoral Contracts of the Basque Government under Grant PRE-2021-1-0001 and Grant PRE-2021-1-021