Impacts of Mobile Phone Distractions on Walking Performance

Accelerometers have been widely used for human activity recognition as an early prediction of fall risk. However, acceleration data do not consider the force of gravity. Recent studies found that jerk, the derivative of acceleration, can describe the changes of body accelerations without considering the sensor orientation. This might overcome the issues caused by the displacement of the sensor, especially if a smartphone-based accelerometer is used as the sensor. This study aimed to compare the performance of acceleration and jerk in detecting postural stability using the postural stability index (PSI). Slightly different daily activity living such as walking on a flat surface, walking upstairs, and walking downstairs were chosen to compare the sensitiveness of acceleration and jerk in detecting the slight postural sway in healthy subjects. The collected data were pre-processed using the 8-modes of ensemble empirical mode decomposition (EEMD). Then, the multiscale entropy (MSE) of each intrinsic mode function (IMF) was calculated, and in the end, the PSI values were obtained. The paired t-test calculation using acceleration data showed that walking on a flat surface and walking downstairs are significantly different (p = 0.039). Whereas, the jerk dataset could not distinguish walking on a flat surface and walking downstairs (p = 0.228). From this result, it is evident that acceleration is better in recognizing human activities than jerk.

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Human daily activities recognition using decision tree

Nurwulan

Selamaj

2021

J. Phys.: Conf. Ser.

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Decision tree is a supervised classifier that is easy to understand. There are various decision tree methods. This study aimed to compare the performance of decision tree methods in human activity recognition using acceleration and jerk data. The subjects performed human activity daily living, namely walking on a flat surface, walking upstairs, walking downstairs, sitting, standing, and lying down. The features were grouped into three categories: acceleration features, jerk features, and combined features of acceleration and jerk. The evaluation was done using Random Forest, J48, Logistic Model Tree, Reduced Error Pruning Tree, Decision Stump, Random Tree, and Hoeffding Tree. The results showed that Random Forest outperformed the other classifiers with acceleration features performed better than the jerk features. However, the combined acceleration and jerk features yielded the highest accuracy. In conclusion, Random Forest is the best decision tree technique in recognizing the pattern in human activity.

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Impacts of Mobile Phone Distractions on Walking Performance

Cited by 3 publications

References 6 publications

A Comparative Evaluation of Acceleration and Jerk in Human Activity Recognition Using Machine Learning Techniques

A Comparative Evaluation of Acceleration and Jerk in Human Activity Recognition Using Machine Learning Techniques

Performance evaluation of acceleration and jerk in unstable walking detection

Human daily activities recognition using decision tree

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