Recognition and classification of human activities using wearable sensors
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We 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.
human activity recognition
principal components analysis
linear discriminant analysis
dynamic time warping
subsequence dynamic time warping