Abstract
Human Activity Tracking by Mobile Phones Through Hebbian Learning
Highlights
Human activity recognition (HAR) has been intensively studied over recent years [1]
The current paper introduces an activity tracking method that uses signals from sensors worn by the subject, signals coming from accelerometers and gyroscopes, which are typically mounted on modern smartphones
One way of obtaining a set of orientation-invariant motion signals is by applying principal component analysis (PCA) to the original accelerometer and gyroscope signals
Summary
Human activity recognition (HAR) has been intensively studied over recent years [1]. From a broad perspective, activity monitoring can be done by processing signals from sensors external to subject’s body [2], video processing [3], and sensors mounted on the subject’s body [4] [5] [6]. For this type of activities, simple features such as the location and height of a peak in the frequency domain, the angle with respect to gravity, or a measurable period are not enough information to classify the activities In such cases testing by cross-validation is possible because a data can be used for training. Validation based on the percentage of frames that are classified correctly without performing data partitioning for training and testing applies to the algorithm proposed in [13] In this case, the algorithm can discriminate between resting and walking by calculating an Average Magnitude Difference Function (AMDF) at a given delay and finding the location of peaks. When the inertial sensors or the smartphone device are attached to a body part, the orientation of the signals in the training and testing stages will be the same In this case, high accuracy results can be expected.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Artificial Intelligence & Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
