The game is not, however, a dominance PD. Indeed, there is no dominant move for either player.
Correspondence should be addressed to Zhiqiang Peng ; moc. This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract Correctly identifying human activities is very significant in modern life. Almost all feature extraction methods are based directly on acceleration and angular velocity. However, we found that some activities have no difference in acceleration and angular velocity.
Therefore, we believe that for these activities, any feature extraction method based on acceleration and angular velocity is difficult to achieve good results. After analyzing the difference of these indistinguishable movements, we propose several new features to improve accuracy of recognition.
We compare the traditional features and our custom features. In addition, we A dilemma and my solution based whether the time-domain features and frequency-domain features based on acceleration and angular velocity are different.
The results show that 1 our custom features significantly improve the precision of the activities that have no difference in acceleration and angular velocity; and 2 the combination of time-domain features and frequency-domain features does not significantly improve the recognition of different activities.
Introduction The classification of human motion based on inertial sensors has been proven to have many important applications in the medical and health fields.
In previous studies, time-domain and frequency-domain features are widely used for feature calculation. There are many studies that use wavelet transform to extract features to classify human activities. However, the research of Preece et al.
Some time-domain features are derived to classify human activities, such as the mean, median, variance, skewness, kurtosis [ 2 ], and interquartile range [ 3 ].
In order to extract frequency-domain features, the sensor data window is first changed to the frequency domain using discrete Fourier Transform [ 4 ].
Then, we can extract some features from the frequency domain to distinguish different activities, such as power spectral density PSD [ 5 ], peak frequency [ 56 ], entropy [ 7 ], DC component [ 7 ], median frequency [ 8 ], spectral energy [ 9 ], and frequency-domain entropy [ 10 ]. Of course, there are other methods that process data from accelerometers and gyroscopes.
But all in all, to the best of our knowledge, these features are extracted directly from acceleration and angular velocity, which inevitably have some common drawbacks.
We studied 12 kinds of activities and found it easy to confuse elevator up and elevator down.
These two kinds of activities do not have obvious differences in acceleration and angular velocity, so the time and frequency-domain features based on acceleration and angular velocity cannot achieve good classification results.
Therefore, for those motions that have no significant difference in angular velocity and acceleration, no matter how the features are extracted from the acceleration and angular velocity, it is difficult to achieve good results.
In addition, we begin to wonder if there is any essential difference between time-domain features and frequency-domain features based on acceleration and angular velocity. In order to solve this discredit, we have separately tested the effects of time-domain features and frequency-domain features.
Then, we tested the combination of the two kinds of features and found that the combination of time-domain features and frequency-domain features was slightly higher than only time-domain features. From the experimental results analysis, we believe that, for human activity classification problem, the time-domain features and the frequency-domain features are two aspects of the same rules, and there is no essential difference.
Our contributions in this paper are two-fold: Methods Indeed, the time-domain and frequency-domain features based on acceleration and angular velocity have achieved some success.
However, for activities without obvious difference between acceleration and angular velocity, such as elevator up and elevator down, the traditional method of extracting features based on acceleration and angular velocity is difficult to work. In order to solve this problem, we carefully analyze the two activities of elevator up and elevator down, summarize the differences between them, and propose some new features for distinguishing such activities.
After analysis, we sum up the following rules: Based on the above evidences, we propose four features to distinguish these activities on each axis of the accelerometer. First, according to the first rule, we introduce three features, namely, the starting speed, the ending speed, and the displacement.
Second, according to the second and third rules, we introduce the fourth feature. As some activities have just started and are about to stop, their speed is small and difficult to distinguish.initiativeblog.com area is the closest to an off-the-shelf solution and a great entry portal into AI for companies.
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