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 COMPREHENSIVE VALIDATION OF ACTIVITY TRACKERS FOR ESTIMATING PHYSICAL ACTIVITY AND SEDENTARY BEHAVIOR IN FREE-LIVING SETTINGS

The aim of study one of this dissertation was to compare consumer activity trackers (ATs) with the research-grade ActiGraph™ GT3X-BT accelerometer (AG) in estimating energy expenditure (EE) and steps during orbital shaking at different frequencies. To address this aim, we utilized an electronic orbital shaking protocol (twenty-four, 3-minute trials; 2-hour trials). For all comparisons, the AG served as the reference measure. In the 3-min protocol, we showed that on average, the NL-1000 pedometer (NL) produced the lowest error (-9 steps/3-min) at 0.9 Hz (corresponding to moderate intensity). The magnitude of the error for the NL was 14 steps/3-min at a 3.0 Hz frequency (corresponding to very vigorous intensity). For the 2-hr protocol, estimates from all others were equivocal, with some overestimating steps (bias range: 1,331 steps/2-hrs for the Misfit Shine to 1,921 steps/2-hrs for the Misfit Flash [MFF]). For estimated EE bias ranged from26.6 kcals/2-hrs for the MFF to 45.8 kcals/2-hrs for the Misfit Shine. For other ATs, steps were underestimated (bias range: -5,770 steps/2-hrs for the Garmin Vivofit [GV] to -570 steps/2-hrs for the NL). For EE, the bias ranged from -436.8 kcals/2-hrs for the GV to -261.7 kcals/2-hrs for the Fitbit Flex [FBF]). This study provides evidence about the differences in prediction algorithms by device across a broad range of oscillation frequencies that corresponded to different PA intensity levels. For study two, we sought determine the accuracy and precision of activity trackers (ATs) in estimating steps, EE, activity minutes and sedentary time compared to direct observation (DO)-derived measures (criterion measures) in free-living settings. We also validated commonly used research-grade devices (e.g. hip-worn AG (AGhip), wrist-worn AG (AGwrist). Thirty-two healthy men and women (50% female, 37.5% minority; mean ± SD: Age = 32.3 ± 13.3 years; BMI = 24.4 ± 3.3 kg∙m-2) were directly observed while completing three, 2-hour visits on different days while wearing ten ATs, three research-grade devices and a biometric shirt. A validated DO system was used to derive criterion measures for activity and sedentary time (ST) outcomes. ATs were accurate with varying precision in estimating physical activity (PA) behaviors in free-living settings. Additionally, ATs and research-grade accelerometers performed similarly (e.g. more accurate in estimating steps and less accurate in estimating moderate-to-vigorous PA [MVPA] minutes). For all devices, step estimates were accurate and strongly correlated (r range: 0.91 for the Apple iWatch to 0.97 for the AGhip) with criterion measures but EE and MVPA estimates were less accurate and more variable (EE: r = 0.32 [GV] to r = 0.85 [AGhip]; MVPA: r = 0.2 [NL] to r = 0.75 [AGhip]). For ATs, estimates of sedentary time were the least accurate and weakly correlated (r=0.06 Fitbit One [FBO] and FBF) with criterion measures derived from DO. Implications from this study are that consumers and the research community using ATs such as Fitbit (FB) to track steps can be confident in estimating steps but less confident in estimating sedentary time. This study advances our understanding of the performance characteristics of ATs in free-living natural settings using a validated DO method to derive PA and ST measures. This work significantly advances the field of activity monitor validation that should set the standard for future work. The aims of study three were: 1) to examine the ability of ATs to detect change in PA and ST in free-living settings and 2) to examine the ability of research-grade accelerometers to detect change in PA and ST in free-living settings. To address these aims, we used an innovative approach to analyze data from study two. We defined change as a visit-to-visit difference that was greater than the within-subject standard deviation of the criterion measure (estimated by a linear-mixed model). Confusion matrices were used to examine percent agreement between DO visit-to-visit change and device visit-to-visit change. Key findings were focused on the widely used FBO and FBF and research-grade devices. We showed that, there was similar agreement between the hip-worn FBO and FBF with AGhip and AGwrist in estimates of change in steps (89.1% FBO, 88.8% FBF and 88.3% AGwrist, 91.4% AGhip correct classification), EE (73.4% FBO, 70.6% FBF and 77.0% AGhip correct classification) and MVPA minutes (accept FBF) (79.7% FBO, 65.2% FBF and 71.2% AGwrist, 77.0% AGhip correct classification) with criterion measured change. However, change in ST was more difficult to detect for the FB and AGhip (46.8% FBO, 42.3% FBF, 53.1% AGhip and 72.7% AGwrist correct classification). This novel study provides evidence that as an alternative to research-grade accelerometers, researchers may employ FB to measure step accumulation pre- and post-intervention and have a satisfactory level of confidence in FB change detection. This work significantly advances the field of activity monitor validation research and informs intervention practices that should set the standard for future work. This body of work provides the first comprehensive validation of ATs from highly controlled orbital shaker testing to directly-observed free-living settings. This translational research which has broad applications for using ATs for surveillance and intervention research and by the consumer

Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms

Nowadays in modern societies, a sedentary lifestyle is almost inevitable for a majority of the population. Long hours of sitting, especially in wrong postures, may result in health complications. A smart chair with the capability to identify sitting postures can help reduce health risks induced by a modern lifestyle. This paper presents the design, realization and evaluation of a new smart chair sensors system capable of sitting postures identification. The system consists of eight pressure sensors placed on the chair's sitting cushion and the backrest. A signal acquisition board was designed from scratch to acquire data generated by the pressure sensors and transmit them via a Wi-Fi network to a purposely developed graphical user interface which monitors and stores the acquired sensors' data on a computer. The designed system was tested by means of an extensive sitting experiment involving 40 subjects, and from the acquired data, the classification of the respective sitting postures out of eight possible postures was performed. Hereby, the performance of seven deep-learning algorithms was assessed. The best accuracy of 91.68% was achieved by an echo memory network model. The designed smart chair sensors system is simple and versatile, low cost and accurate, and it can easily be deployed in several smart chair environments, both for public and private contexts

Providing NHS staff with height-adjustable workstations and behaviour change strategies to reduce workplace sitting time: protocol for the Stand More AT (SMArT) Work cluster randomised controlled trial

BACKGROUND. High levels of sedentary behaviour (i.e., sitting) are a risk factor for poor health. With high levels of sitting widespread in desk-based office workers, office workplaces are an appropriate setting for interventions aimed at reducing sedentary behaviour. This paper describes the development processes and proposed intervention procedures of Stand More AT (SMArT) Work, a multi-component randomised control (RCT) trial which aims to reduce occupational sitting time in desk-based office workers within the National Health Service (NHS). METHODS/DESIGN. SMArT Work consists of 2 phases: 1) intervention development: The development of the SMArT Work intervention takes a community-based participatory research approach using the Behaviour Change Wheel. Focus groups will collect detailed information to gain a better understanding of the most appropriate strategies, to sit alongside the provision of height-adjustable workstations, at the environmental, organisational and individual level that support less occupational sitting. 2) intervention delivery and evaluation: The 12 month cluster RCT aims to reduce workplace sitting in the University Hospitals of Leicester NHS Trust. Desk-based office workers (n = 238) will be randomised to control or intervention clusters, with the intervention group receiving height-adjustable workstations and supporting techniques based on the feedback received from the development phase. Data will be collected at four time points; baseline, 3, 6 and 12 months. The primary outcome is a reduction in sitting time, measured by the activPALTM micro at 12 months. Secondary outcomes include objectively measured physical activity and a variety of work-related health and psycho-social measures. A process evaluation will also take place. DISCUSSION. This study will be the first long-term, evidence-based, multi-component cluster RCT aimed at reducing occupational sitting within the NHS. This study will help form a better understanding and knowledge base of facilitators and barriers to creating a healthier work environment and contribute to health and wellbeing policy. TRIAL REGISTRATION. ISRCTN10967042. Registered 2 February 2015

Sedentary behaviour in office workers: correlates and interventions

Background: The concept of sedentary behaviour has emerged since the turn of the millennium and research into this area is rapidly developing. Sedentary behaviours are activities that require very little energy expenditure whilst in a sitting or reclining posture thus are distinct from physical inactivity. Previous observational studies have demonstrated that high amounts of sedentary behaviour are associated with an increased risk of obesity, type 2 diabetes, metabolic syndrome, cardiovascular disease, cancer, depression and all-cause, cardiovascular and cancer mortality. Experimental studies suggest that prolonged sedentary time causes metabolic dysregulation and could be the explanation for the associated negative health effects. Breaks in prolonged sedentary time where standing or stepping occurs have shown beneficial effects on metabolic risk markers but the threshold for these effects is ambivalent and may depend on the population. The increasing prevalence of sedentary behaviours due to advances in technology are concerning but there is a lack of large-scale studies from the UK identifying the extent of sedentary behaviour prevalence and where the majority of sedentary time is accumulated in working-aged adults. A number of correlates are associated with sedentary behaviour including individual, social and environmental factors but the extent to which multiple other health behaviours correlate with specific sedentary behaviours is unknown. Interventions to reduce sedentary time have focused on the workplace where office workers spend large amounts of time sedentary. Multicomponent workplace interventions have reported reductions in sedentary time but there is limited research in the UK investigating the long-term effects of these interventions on working and non-working hours sedentary time. Additionally, the use of persuasive technology in the form of a wearable device to reduce sedentary time has rarely been explored as an intervention strategy.Aims: Study One aimed to assess the prevalence of domain-specific sedentary behaviour in a large sample of office workers from the UK and links with multiple other health behaviours. Study Two aimed to investigate the effectiveness of a pilot multicomponent workplace intervention to reduce sedentary time over the short (3 months) and long-term (12 months). Study Three aimed to explore the feasibility of a self-monitoring and prompting device to reduce sedentary time in a sample of office workers who have sit-stand desks.Methods: Study One performed a secondary data analysis on a large sample of office workers (n=7,170) who self-reported their domain-specific sitting time, physical activity level, smoking status, alcohol consumption, and fruit and vegetable intake in a 2012 and/or 2014 survey. Multiple logistic regression models explored the association between sedentary behaviours and multiple other health behaviours. A separate analysis was performed to investigate how these associations tracked over time (n=806). Study Two implemented a multicomponent workplace intervention in a sample of office workers (baseline n=30) and measured the effects 3 and 12-months post-baseline compared to a control group (baseline n=30). activPAL sedentary time was the primary outcome with accelerometer-determined physical activity and markers of health measured as secondary outcomes. Study Three provided a sample of office workers who had sit-stand desks (n=19 baseline, n=17 follow-up) with a wearable device to self-monitor their sedentary time through an application and prompt reductions in prolonged sedentary time through haptic feedback (LUMO). Feasibility and acceptability of the 4-week intervention were measured through wear time, engagement with application, questionnaire and interview feedback. The effect on sedentary time was measured with the LUMO and activPAL in addition to health and work-related measures.Results: Study One found that 643±160 minutes on a workday and 491±210 minutes on a non-workday were spent sitting. The majority of workday sitting took place at work (383±95 minutes/day) and whilst TV viewing on a non-workday (173±101 minutes/day). ≥7 hours sitting at work and ≥2 hours TV viewing on a workday both more than doubled the odds of partaking in ≥3 unhealthy behaviours [Odds ratio, OR=2.03, 95% CI, (1.59-2.61); OR=2.19 (1.71-2.80)] and ≥3 hours of TV viewing on a non-workday nearly tripled the odds [OR= 2.96 (2.32-3.77)]. No associations between domain-specific sitting time at baseline and change in unhealthy behaviour score were found over two years with the majority of participants maintaining baseline levels of all behaviours. Study Two found a trend towards reduced sedentary time at work by -7.9±25.1% and -18.4±12.4% per day at 3- (n=25 intervention, n=18 control group) and 12-months (n=11 intervention, n=7 control group) post-baseline in addition to overall workday by -4.6±13.8% and -8.0±8.3%. The intervention group showed an increase in sedentary time outside of work on a workday (4.2±9.5%) and overall on a non-workday (3.5±10.8%) after 12 months compared to baseline. However, the results found at the 3-month follow-up were not statistically significant and no significant differences in physical activity or health measures between groups were observed. Furthermore, due to the reduced sample size at the 12-month follow-up, no statistical testing was performed. Study Three found that the LUMO was a feasible intervention device in the short-term demonstrating high wear time (mean=60.6% of measurement days) and application engagement (mean=26.2±33.2 sessions, 30.3±26.5 minutes per week) with sedentary time being the most engaged with aspect of the application. The acceptability of the LUMO depended on the task undertaken, experience of problems with the device and preference towards the application or the prompt but overall, it increased awareness of behaviour. A trend towards reductions in sedentary time (-4%) and prolonged bouts of sedentary time >60 mins (-3%) on a workday were observed. Improvements were found in fat percentage and mass, blood pressure, job performance, work engagement, need for recovery and job satisfaction. Non-workday sedentary time >60 min bouts increased (4.8%) and increases in non-working hours sedentary time were apparent in weeks 3 and 4.Conclusions: Office workers are highly sedentary at work and whilst TV viewing which is associated with partaking in other multiple unhealthy behaviours. Multicomponent workplace interventions result in a trend towards reductions in occupational sedentary behaviour over the short and long-term. However, compensation during non-working hours could attenuate overall sedentary behaviour reductions resulting from workplace interventions. Wearable technology as an intervention strategy to reduce sedentary time shows promise and further research is needed in fully-powered studies. Future interventions should target multiple unhealthy behaviours in addition to sedentary time during work and non-working hours.</div

Care-Chair: Opportunistic health assessment with smart sensing on chair backrest

A vast majority of the population spend most of their time in a sedentary position, which potentially makes a chair a huge source of information about a person\u27s daily activity. This information, which often gets ignored, can reveal important health data but the overhead and the time consumption needed to track the daily activity of a person is a major hurdle. Considering this, a simple and cost-efficient sensory system, named Care-Chair, with four square force sensitive resistors on the backrest of a chair has been designed to collect the activity details and breathing rate of the users. The Care-Chair system is considered as an opportunistic environmental sensor that can track each and every activity of its occupant without any human intervention. It is specifically designed and tested for elderly people and people with sedentary job. The system was tested using 5 users data for the sedentary activity classification and it successfully classified 18 activities in laboratory environment with 86% accuracy. In an another experiment of breathing rate detection with 19 users data, the Care-Chair produced precise results with slight variance with ground truth breathing rate. The Care-Chair yields contextually good results when tested in uncontrolled environment with single user data collected during long hours of study. --Abstract, page iii

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

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)

Therapeutic Strategies in Architecture for Senior Care and Rehabilition

My research is in developing a new building typology for the elderly retirement population. Retirement funds are often eaten up by poor planning and hasty decisions which can jeopardize their health. Hawaii has a large elderly population and I see a great need to address this problem now, as the largest demographic group is now retiring. Hypothesis: Retirement hangs as the preverbal carrot for most people in our rapidly paced society. The reward for life of hard work too often becomes a sedentary activity that encourages the degeneration of our physical body. Architecture for retirees often facilitates this and designs for a lethargic lifestyle. The consistent pattern for elderly is a ‘fall’, which then leads to a back-and-forth to the hospital. Most of the time, the fall occurs within a ‘designed’ space. The research goal is to develop design strategies, design components, and awareness of the problems. Just as ADA (American’s with Disabilities Act) is the product of awareness and energy to a neglected demographic, the elderly should have strong design influences. The desired outcome for the project is to prepare for a design that addresses the needs for this elderly age group . Gaining an understanding of the demographic, the needs, hazards, and opportunities will prepare me for the design process. Specific solutions ranging from therapeutic spaces to technical solutions for improved mobility and independence will be investigated. In urban or suburban places, our mobility is based on options presented to us. These are intentional designs and understanding how ‘designed circulation’ develops certain muscles while others are lost, helps me design spaces that become therapeutic and incorporate the muscles that are lost. Case studies will be investigated to gain parameters on cost, and design solutions. Emerging theories in senior health care incorporate more activity throughout the day compared to a periodic ‘exercise’ time. Architecture can facilitate this approach of a steady flow of stimulus and activity.My research is in developing a new building typology for the elderly retirement population. Retirement funds are often eaten up by poor planning and hasty decisions which can jeopardize their health. Hawaii has a large elderly population and I see a great need to address this problem now, as the largest demographic group is now retiring. Hypothesis: Retirement hangs as the preverbal carrot for most people in our rapidly paced society. The reward for life of hard work too often becomes a sedentary activity that encourages the degeneration of our physical body. Architecture for retirees often facilitates this and designs for a lethargic lifestyle. The consistent pattern for elderly is a ‘fall’, which then leads to a back-and-forth to the hospital. Most of the time, the fall occurs within a ‘designed’ space. The research goal is to develop design strategies, design components, and awareness of the problems. Just as ADA (American’s with Disabilities Act) is the product of awareness and energy to a neglected demographic, the elderly should have strong design influences. The desired outcome for the project is to prepare for a design that addresses the needs for this elderly age group . Gaining an understanding of the demographic, the needs, hazards, and opportunities will prepare me for the design process. Specific solutions ranging from therapeutic spaces to technical solutions for improved mobility and independence will be investigated. In urban or suburban places, our mobility is based on options presented to us. These are intentional designs and understanding how ‘designed circulation’ develops certain muscles while others are lost, helps me design spaces that become therapeutic and incorporate the muscles that are lost. Case studies will be investigated to gain parameters on cost, and design solutions. Emerging theories in senior health care incorporate more activity throughout the day compared to a periodic ‘exercise’ time. Architecture can facilitate this approach of a steady flow of stimulus and activity.My research is in developing a new building typology for the elderly retirement population. Retirement funds are often eaten up by poor planning and hasty decisions which can jeopardize their health. Hawaii has a large elderly population and I see a great need to address this problem now, as the largest demographic group is now retiring. Hypothesis: Retirement hangs as the preverbal carrot for most people in our rapidly paced society. The reward for life of hard work too often becomes a sedentary activity that encourages the degeneration of our physical body. Architecture for retirees often facilitates this and designs for a lethargic lifestyle. The consistent pattern for elderly is a ‘fall’, which then leads to a back-and-forth to the hospital. Most of the time, the fall occurs within a ‘designed’ space. The research goal is to develop design strategies, design components, and awareness of the problems. Just as ADA (American’s with Disabilities Act) is the product of awareness and energy to a neglected demographic, the elderly should have strong design influences. The desired outcome for the project is to prepare for a design that addresses the needs for this elderly age group . Gaining an understanding of the demographic, the needs, hazards, and opportunities will prepare me for the design process. Specific solutions ranging from therapeutic spaces to technical solutions for improved mobility and independence will be investigated. In urban or suburban places, our mobility is based on options presented to us. These are intentional designs and understanding how ‘designed circulation’ develops certain muscles while others are lost, helps me design spaces that become therapeutic and incorporate the muscles that are lost. Case studies will be investigated to gain parameters on cost, and design solutions. Emerging theories in senior health care incorporate more activity throughout the day compared to a periodic ‘exercise’ time. Architecture can facilitate this approach of a steady flow of stimulus and activity