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

A Smart System for Sitting Posture Detection Based on Force Sensors and Mobile Application

The employees health and wellbeing are an actual topic in our fast-moving world. The employers losing money when their employees suffer from different health problems and cannot work. The major problem is the spinal pain caused by the poor sitting posture on the office chair. This paper deals with the proposal and realization of the system for the detection of incorrect sitting positions. The smart chair has six flexible force sensors. The Internet of Things (IoT) node based on Arduino connects these sensors into the system. The system detects wrong seating positions and notifies the users. In advance, we develop a mobile application to receive those notifications. The user gets feedback about sitting posture and additional statistical data. We defined simple rules for processing the sensor data for recognizing wrong sitting postures. The data from smart chairs are collecting by a private cloud solution from QNAP and are stored in the MongoDB database. We used the Node-RED application for whole logic implementation.Comment: 19 pages, 13 figures, 3 tables, article in journa

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

Healthy Sitting Behaviour Enhancement using a Smart Chair System

The aim of this paper is to present a smart chair prototype to monitor the sitting behaviour of people in wheelchair to re-educate them about long periods of time standing still and in the same position and giving them a feedback about this. The project is mainly focused on those who have been in a wheelchair for a short time. The sitting posture monitoring in the developed smart chair system can help or promote people to achieve and maintain healthy sitting behaviour, and prevent or reduce diseases caused by poor sitting behaviour, like bedsores (pressure ulcers)

Biomechanics

Biomechanics is a vast discipline within the field of Biomedical Engineering. It explores the underlying mechanics of how biological and physiological systems move. It encompasses important clinical applications to address questions related to medicine using engineering mechanics principles. Biomechanics includes interdisciplinary concepts from engineers, physicians, therapists, biologists, physicists, and mathematicians. Through their collaborative efforts, biomechanics research is ever changing and expanding, explaining new mechanisms and principles for dynamic human systems. Biomechanics is used to describe how the human body moves, walks, and breathes, in addition to how it responds to injury and rehabilitation. Advanced biomechanical modeling methods, such as inverse dynamics, finite element analysis, and musculoskeletal modeling are used to simulate and investigate human situations in regard to movement and injury. Biomechanical technologies are progressing to answer contemporary medical questions. The future of biomechanics is dependent on interdisciplinary research efforts and the education of tomorrow’s scientists

Effects of Adjustments to Wheelchair Seat to Back Support Angle on Head, Neck, and Shoulder Postures

Background: People spend a long time in the sitting position may have poor alignment that leads to neck and back pain. A wheelchair represents mobility for people with cerebral palsy, who are unable to walk. They spend long periods of time sitting in their wheelchair. Opining the seat to back support angle of the wheelchair enable realignment body segments and improves posture. Objective: 1) assessed the validity/reliability of Coach’s Eye (CE) smart device application, 2) examined the effect of seat to back support angle adjustments on head, neck, and shoulder posture in the sitting position, and 3) compared changes in cervical rotation at each seat to back support angle. Methods: Thirty-four subjects between the ages of 18 and 45 years abled subjects and subjects with cerebral palsy. All subjects sat in a research wheelchair with seat to back support angle at (90°, 100°, and 110°). Photographs were taken and analyzed by ImageJ and cache’s Eye (CE) software. Three body posture angles were used: sagittal head angle (SHA), cervical angle (CVA), and shoulder angle (SA). Results: There were highly significant differences on abled subjects for CVA and SA (p \u3c 0.001) among the three seat to back support angles. CE had high validity for all angles (r = 0.99, 0.98, 0.99 respectively, p \u3c 0.001). Inter-rater reliability for SHA, CVA, and SA among the three seat to back support angles was high (ICC ranged from 0.95 to 0.99). There were highly significant differences on abled subjects for CVA and SA (p \u3c 0.001). There were highly significant differences on subjects with cerebral palsy for SHA and CVA (p \u3c 0.001) among the three seat to back support angles. Conclusion: Head (CVA) and shoulder (SA) alignment was closest to neutral posture for abled subjects with seat to back support angles set at 110° and 90°, respectively. Head (SHA) and (CVA) alignment was closest to neutral posture for subjects with CP with seat to back support angles set at 110°

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

Comparing clothing-mounted sensors with wearable sensors for movement analysis and activity classification

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing

Inertial sensor-based knee flexion/extension angle estimation

A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth’s magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 miles/hour. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions