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11	IntelliChair : a non-intrusive sitting posture and sitting activity recognition system Fu, Teng January 2015 (has links) Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required. 006.3
12	Understanding the Association Between Cognitive Workload Imposed by Computer Tasks and Computer Users' Biomechanical Responses Wang, Xueke January 2020 (has links) No description available. Industrial Engineering Biomechanics Occupational Health Design
13	Placement of Controls in Construction Equipment Using Operators´Sitting Postures : Process and Recommendations Jalkebo, Charlotte January 2014 (has links) An ergonomically designed work environment may decrease work related musculoskeletal disorders, lead to less sick leaves and increase production time for operators and companies all around the world. Volvo Construction Equipment wants to deepen the knowledge and investigate more carefully how operators are actually sitting whilst operating the machines, how this affects placement of controls and furthermore optimize controls placements accordingly. The purpose is to enhance their product development process by suggesting guidelines for control placement with improved ergonomics based on operators’ sitting postures. The goal is to deliver a process which identifies and transfers sitting postures to RAMSIS and uses them for control placement recommendations in the cab and operator environments. Delimitations concerns: physical ergonomics, 80% usability of the resulted process on the machine types, and the level of detail for controls and their placements. Research, analysis, interviews, test driving of machines, video recordings of operators and the ergonomic software RAMSIS has served as base for analysis. The analysis led to (i) the conclusion that sitting postures affect optimal ergonomic placement of controls, though not ISO-standards, (ii) the conclusion that RAMSIS heavy truck postures does not seem to correspond to Volvo CE’s operators’ sitting postures and (iii) and to an advanced engineering project process suitable for all machine types and applicable in the product development process. The result can also be used for other machines than construction equipment. The resulted process consists of three independent sub-processes with step by step explanations and recommendations of; (i) what information that needs to be gathered, (ii) how to identify and transfer sitting postures into RAMSIS, (iii) how to use RAMSIS to create e design aid for recommended control placement. The thesis also contains additional enhancements to Volvo CE’s product development process with focus on ergonomics. A conclusion is that the use of motion capture could not be verified to work for Volvo Construction Equipment, though it was verified that if motion capture works, the process works. Another conclusion is that the suggested body landmarks not could be verified that they are all needed for this purpose except for those needed for control placement. Though they are based on previous sitting posture identification in vehicles and only those that also occur in RAMSIS are recommended, and therefore they can be used. This thesis also questions the most important parameters for interior vehicle design (hip- and eye locations) and suggests that shoulder locations are just as important. The thesis concluded five parameters for control categorization, and added seven categories in addition to those mentioned in the ISO-standards. Other contradictions and loopholes in the ISO-standards were identified, highlighted and discussed. Suggestions for improving the ergonomic analyses in RAMSIS can also be found in this report. More future research mentioned is more details on control placement as well as research regarding sitting postures are suggested. If the resulted process is delimited to concern upper body postures, other methods for posture identification may be used. Motion capture controls placement posture identification construction equipment operator environment volvo ce depth sensor inertial optical zone of reach zone of comfort H-point SIP Seat index point product development ergonomics Work Related Musculoskeletal Disorders Human Machine Interaction HMI WRMD Anthropometry Catia RAMSIS GEometric Kinematic backhoe loader wheel loader articulated hauler excavator Faro-Arm CMM Skeleton points skin points sitting posture operating posture categorization V model visual fields percentile road condition skill level sight point Abrasion speed visibility FOV field of view primary secondary tertiary ZoC ZoR mid-eye Design aid safety heavy truck

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