Recognizing Human Activities Based on Wearable Inertial Measurements - Methods and Applications

On April 10 of 2015 Pekka Siirtola defended his PhD thesis, called “Recognizing Human Activities Based on Wearable Inertial Measurements - Methods and Applications” [1]. The thesis was supervised by Professor Juha Röning and pre-eximined by Associate Professors Ulf Johansson from University of Borås, Sweden, and Daniel Roggen from University of Sussex, United Kingdom. Pekka Siirtola successfully defended his thesis against his opponent Professor Barbara Hammer from University of Bielefeld, Germany. This publicly open defence was held in Auditorium TS101 at University of Oulu, Finland

Recognizing Human Activities Userindependently on Smartphones Based on Accelerometer Data

Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian^3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent