![]() Costa, R.M.: Human activity data discovery from triaxial accelerometer sensor: non-supervised learning sensitivity to feature extraction parametrization. Koshiba, M.: Potential of a smartphone as a stress-free sensor of daily human behaviour. Phung, D.: Nonparametric discovery of movement patterns from accelerometer signals. Minton, S.: The digital universe of opportunities: rich data and the increasing value of the internet of things. Redline, S.: Practical considerations in using accelerometers to assess physical activity, sedentary behavior, and sleep. ![]() Lin, Z.-Y.: Using accelerometer for counting and identifying swimming strokes. Jarchi, D.: Gyroscope versus accelerometer measurements of motion from wrist PPG during physical exercise. Forsman, M.: An iPhone application for upper arm posture and movement measurements. In: 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. Aloul, F.: Emotion recognition using mobile phones. Kocabag, G.: On the use of ensemble of classifiers for accelerometer-based activity recognition. Ramirez, G.A.: A mobile system for sedentary behaviors classification based on accelerometer and location data. The proposed system was tested on a developed mobile application prototype, and its applicability was shown through experiments.Ĭeron, J.D. The system is capable of device-based training and can distinguish the device owner’s typing behavior from those of others with 100% accuracy. In the user studies, we achieved accuracy of 98.55% for ANN, 100% for k-NN, 99.8% for SVM and 99.5% for RFC. Artificial neural networks (ANN), k-nearest neighbors ( k-NN), support vector machines (SVM) and RandomForest Classifier (RFC) algorithms, which are some of the most common algorithms, were applied for classification. Typing behaviors are classified by various machine learning techniques with the data inputted from accelerometer and gyroscope sensors. ![]() We investigate users’ unique typing and phone holding behaviors by examining the soft biometric (age, gender) and statistical features. In this study, we propose a system to classify users’ typing behaviors based on the data produced by the built-in sensors and present a login use case scenario to validate the results. The amount of personal data stored on mobile devices has risen significantly during the past several years as a result of two developments: More people are using them, and sensors have become more advanced, capable of analyzing and classifying human activities such as walking, running, sleeping and cycling, and swimming.
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