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dc.contributor.advisorBarshan, Billuren_US
dc.contributor.authorYurtman, Arasen_US
dc.date.accessioned2016-01-08T18:24:52Z
dc.date.available2016-01-08T18:24:52Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/11693/15803
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2012.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractWe address the problem of detecting and classifying human activities using two different types of wearable sensors. In the first part of the thesis, a comparative study on the different techniques of classifying human activities using tag-based radio-frequency (RF) localization is provided. Position data of multiple RF tags worn on the human body are acquired asynchronously and non-uniformly. Curves fitted to the data are re-sampled uniformly and then segmented. The effect of varying the relevant system parameters on the system accuracy is investigated. Various curve-fitting, segmentation, and classification techniques are compared and the combination resulting in the best performance is presented. The classifiers are validated through the use of two different cross-validation methods. For the complete classification problem with 11 classes, the proposed system demonstrates an average classification error of 8.67% and 21.30% for 5-fold and subject-based leave-one-out (L1O) cross validation, respectively. When the number of classes is reduced to five by omitting the transition classes, these errors become 1.12% and 6.52%. The system demonstrates acceptable classification performance despite that tag-based RF localization does not provide very accurate position measurements. In the second part, data acquired from five sensory units worn on the human body, each containing a tri-axial accelerometer, a gyroscope, and a magnetometer, during 19 different human activities are used to calculate inter-subject and interactivity variations in the data with different methods. Absolute, Euclidean, and dynamic time-warping (DTW) distances are used to assess the similarity of the signals. The comparisons are made using time-domain data and feature vectors. Different normalization methods are used and compared. The “best” subject is defined and identified according to his/her average distance to the other subjects.Based on one of the similarity criteria proposed here, an autonomous system that detects and evaluates physical therapy exercises using inertial sensors and magnetometers is developed. An algorithm that detects all the occurrences of one or more template signals (exercise movements) in a long signal (physical therapy session) while allowing some distortion is proposed based on DTW. The algorithm classifies the executions in one of the exercises and evaluates them as correct/incorrect, identifying the error type if there is any. To evaluate the performance of the algorithm in physical therapy, a dataset consisting of one template execution and ten test executions of each of the three execution types of eight exercise movements performed by five subjects is recorded, having totally 120 and 1,200 exercise executions in the training and test sets, respectively, as well as many idle time intervals in the test signals. The proposed algorithm detects 1,125 executions in the whole test set. 8.58% of the executions are missed and 4.91% of the idle intervals are incorrectly detected as an execution. The accuracy is 93.46% for exercise classification and 88.65% for both exercise and execution type classification. The proposed system may be used to both estimate the intensity of the physical therapy session and evaluate the executions to provide feedback to the patient and the specialist.en_US
dc.description.statementofresponsibilityYurtman, Arasen_US
dc.format.extentxiv, 118 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectradio-frequency localizationen_US
dc.subjectradio-frequency identificationen_US
dc.subjecthuman activity recognitionen_US
dc.subjectpattern recognitionen_US
dc.subjectclassificationen_US
dc.subjectfeature extractionen_US
dc.subjectfeature reductionen_US
dc.subjectprincipal components analysisen_US
dc.subjectlinear discriminant analysisen_US
dc.subjectPfold cross-validationen_US
dc.subjectleave-one-out cross-validationen_US
dc.subjectabsolute distanceen_US
dc.subjectEuclidean distanceen_US
dc.subjectdynamic time warpingen_US
dc.subjectsubsequence dynamic time warpingen_US
dc.subjectdynamic programmingen_US
dc.subjectnormalizationen_US
dc.subjectinertial sensorsen_US
dc.subjectaccelerometersen_US
dc.subjectgyroscopesen_US
dc.subjectmagnetometersen_US
dc.subjectpattern searchen_US
dc.subjectmovement detectionen_US
dc.subjectphysical therapyen_US
dc.subject.lccTA1650 .Y87 2012en_US
dc.subject.lcshOptical pattern recognition.en_US
dc.subject.lcshComputer vision.en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshBody, Human--Computer simulation.en_US
dc.subject.lcshSensors.en_US
dc.subject.lcshHuman locomotion.en_US
dc.subject.lcshIntelligent control systems.en_US
dc.subject.lcshDetectors.en_US
dc.titleRecognition and classification of human activities using wearable sensorsen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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