Estimation of Human Activities by Fuzzy Classification Systems for Understanding Subject Persons Situation Human activities like stay, walk, squat, jogging, stair up and down have been estimated by our proposed fuzzy classification system. Data obtained by a wearable biometric sensor device have been used for estimating human activities. The sensor device can obtain electrocardiograms (ECG), 3axis acceleration, body surface temperature, humidity, ambient temperature, and atmospheric pressure. Calculated bodyangle, body vibration, and amount of change of pressure from sensor data have been used for making fuzzy rules. FIR filters have been used for preprocessing of data. Components of the attitude were extracted from the acceleration data. Fivefold and threefold crossvalidation methods have been used when the same person dataset and different person datasets were used for validation, respectively. MHEALTH datasets has also been validated on the fuzzy classification system. Three movements(stay, walk and jogging) have been classified with this system. From the experimental results, in every cases, every movement has been classified with more than 93% accuracy by our proposed method.

REALTime Smartphone Activity Classification Using Inertial Sensorsxe2x80x94Recognition of Scrolling, Typing, and Watching Videos While Sitting or Walking By developing awareness of smartphone activities that the user is performing on their smartphone, such as scrolling feeds, typing and watching videos, we can develop application features that are beneficial to the users, such as personalization. It is currently not possible to access realtime smartphone activities directly, due to standard smartphone privileges and if internal movement sensors can detect them, there may be implications for access policies. Our research seeks to understand whether the sensor data from existing smartphone inertial measurement unit (IMU) sensors (triaxial accelerometers, gyroscopes and magnetometers) can be used to classify typical human smartphone activities. We designed and conducted a study with human participants which uses an Android app to collect motion data during scrolling, typing and watching videos, while walking or seated and the baseline of smartphone nonuse, while sitting and walking. We then trained a machine learning (ML) model to perform realtime activity recognition of those eight states. We investigated various algorithms and parameters for the best accuracy. Our optimal solution achieved an accuracy of 78.6% with the Extremely Randomized Trees algorithm, data sampled at 50 Hz and 5s windows. We conclude by discussing the viability of using IMU sensors to recognize common smartphone activities.