Download Human Activity Recognition: Using Wearable Sensors and by Miguel A. Labrador PDF

By Miguel A. Labrador

Learn find out how to layout and enforce HAR structures

The pervasiveness and diversity of features of today’s cellular units have enabled a large spectrum of cellular purposes which are reworking our day-by-day lives, from smartphones built with GPS to built-in cellular sensors that collect physiological info. Human task acceptance: utilizing Wearable Sensors and Smartphones specializes in the automated id of human actions from pervasive wearable sensors—a an important part for future health tracking and in addition acceptable to different components, akin to leisure and tactical operations.

Developed from the authors’ approximately 4 years of rigorous examine within the box, the e-book covers the speculation, basics, and functions of human job attractiveness (HAR). The authors research how desktop studying and development popularity instruments aid make certain a user’s job in the course of a definite time period. They suggest structures for acting HAR: Centinela, an offline server-oriented HAR method, and Vigilante, a very cellular real-time job reputation procedure. The publication additionally offers a realistic consultant to the improvement of task acceptance functions within the Android framework.

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Additional resources for Human Activity Recognition: Using Wearable Sensors and Smartphones

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All the computations are done in an embedded system that should be carried by the user as an additional device. This has some disadvantages with respect to a mobile phone in terms of portability, comfort, and cost. Moreover, the size of the time window was chosen to be 160 ms. Given the nature of the recognized activities, this excessive granularity causes accidental movements when swinging or knocking may be confused with running, for instance. Such a small window length also induces more overhead due to the classification algorithm being triggered very often, and it is not beneficial for the feature extraction performance, as time domain features require O(n) computations.

A key issue in Bayesian Networks is the topology construction, as it is necessary to make assumptions on the independence among features. For instance, the NB classifier assumes that all features are conditionally independent given a class value, yet such assumption does not hold in many cases. As a matter of fact, acceleration signals are highly correlated, as well as physiological signals such as heart rate, respiration rate, and ECG amplitude. • Instance-Based Learning (IBL) [111] methods classify an instance based upon the most similar instance(s) in the training set.

Two accelerometers, placed on the subject’s wrist and waist, are connected to a PDA via a serial port. The PDA sends the raw data via Bluetooth to a computer which processes the data. This configuration is obtrusive and uncomfortable because the user has to wear wired links that may interfere with the normal course of activities. The extracted features are the angular velocity and the 3D deviation of the acceleration signals. The classification of activities operates in two stages. , walking and running).

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