A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data
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Citations
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References
The Fractal Geometry of Nature
A theory for multiresolution signal decomposition: the wavelet representation
The Fractal Geometry of Nature
Singularity detection and processing with wavelets
Activity recognition from user-annotated acceleration data
Related Papers (5)
Frequently Asked Questions (14)
Q2. What are the future works mentioned in the paper "A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data" ?
Further work is required to determine the most appropriate features sets for other subjects groups, such as the elderly or neurologically impaired. ~18~
Q3. What is the popular method of evaluating the accuracy of the kNN classifier?
Cross validation is a popular statistical resampling procedure [44] and the authors use it here to evaluate the accuracy of the kNN classifier for a given set of features.
Q4. What is the way to collect and store accelerometer data?
With recent advances in miniaturised sensing technology, it is now possible to collect and store acceleration data from individual body segments over extended periods of time.
Q5. What was used to secure the monitors in place?
To secure each unit in place specialised bandage (FabriFoam®) was first positioned around each of the body segments and the activity monitors, which were backed with Velcro®, adhered to the underwrapped bandage.
Q6. What is the role of the accelerometer in the prediction of fall risk?
Ambulatory monitoring of various activities, including the time spent in sit-stand transitions have shown promise as predictors of fall-risk [8].
Q7. What is the role of portable systems in a ubiquitous computing scenario?
In addition to health-related applications, portable systems which can accurately identify the activity of the user have the potential to play a fundamental role in a ubiquitous computing scenario [9, 10].
Q8. How many steps did the subjects have to do to perform the first activity?
For the first of these two activities, subjects were instructed to perform a gentle jog over a 50m distance and for the second to perform a fast run over the same distance.
Q9. What was the method used for the video problem?
The video method, used~17~in this study, was selected as it was believed to be more accurate than self observation by the subject.
Q10. What is the role of self reported measures in epidemiological studies?
As self reported measures have been shown to be unreliable [4, 5], systems for activity profiling are beginning to play an important role in large-scale epidemiological studies in this area [6, 7].
Q11. What frequency bands were used to calculate the features?
Both Nyan et al. [24] and Sekine et al. [22] collected data at 256Hz, therefore as before, wavelet coefficients corresponding to appropriate frequency bands were used to calculate of each of the features.
Q12. What is the highest classification accuracy for a single sensor?
The highest classification accuracy for a single sensor was obtained for the FFT component feature set and the ankle-mounted sensor.
Q13. What is the method for calculating the classification accuracy of a single sensor?
for the three-activity problem, the highest classification accuracy for a single sensor (97±3%) was obtained using FFT components derived from the ankle-mounted unit.
Q14. What is the way to interpret the accelerometer data?
Although this technology offers the ideal platform for monitoring daily activity patterns, effective algorithms are also required to interpret the accelerometer data in the context of~3~different activities.