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A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data Abstract Driven by the demands on healthcare resulting from the shift toward more sedentary lifestyles, considerable effort has been devoted to the monitoring and classification of human activity.
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In these methods, the machine data (such as the current, power, and so on) from the machine elements (such as spindle or axis motors) 5,6,7 or signals from the sensors integrated into the machine tool (such as piezosensor, accelerometer, strain gauge, thermocouple, acoustic emission sensor, and so on) 8,9,10,11,12,13,14,15,16 are analyzed to recognize the possible
2.1 Data collection Accelerometer data was collected using Pegasus activity monitors developed by ETB, UK. Each of these units contained a tri-axial accelerometer, with dynamic range of &177;5g, which was sampled a with 10-bit resolution. With these devices it is possible to sample
Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The
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Feature Learning for Accelerometer based Gait Recognition IEEE Conference Publication IEEE Xplore Feature Learning for Accelerometer based Gait Recognition Abstract Recent advances in pattern matching, such as speech or object recognition support the viability of feature extraction with deep learning solutions for gait recognition.
Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The
The deep learning framework has immensely impacted the field of image processing and signal processing by automating the feature extraction process , , , , , .For signal based classification, 1-dimensional convolutional neural network (1D-CNN) is used which comprises layers of kernels that convolve with the input over only a single spatial or temporal dimension
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The sampling frequency of the accelerometer was assumed to be 50Hz. Feature extraction The dataset consists of raw tri-axial accelerometer data and hence one may need to extract the useful features from this raw data to help identify the gait and the user performing the gait. The