Continuous Arm Gesture Recognition Based On Natural Features And Logistic Regression.

In this project, we propose a new gesture modeling method by combining natural features with logistic regression to recognize continuous natural gestures, which is considered as a challenge. Instead of manually modeling gestures with criteria, gesture models can be derived from a few gesture instances. Arm gestures are modeled with three types of intuitive features, and logistic regression is employed to obtain the optimal weights to linearly combine all the features, which can deal with the differences among instances with the same gesture automatically and facilitate distinguishing different gestures, and as a result, improve the recognition accuracy. With these features, numbers of natural arm gestures can be modeled with good comprehensibility. To evaluate our method, we defined some natural gestures, including static gestures and dynamic gestures. A continuous gesture database which includes 154024 data frames (102 minutes) and 1628 gesture instances was collected from subjects with a wearable multi-IMU sensing system. Both subject-dependent and subject-mixed recognition tests were conducted with the database. In the subject-dependent test, models were trained with about 30% data, and the average accuracy over all subjects was 91.54%. In the subject-mixed test, only about 10% data was used for training, and the accuracy is 88.75%. The recognition accuracy is highly competitive with previous gesture recognition approaches.