Machine learning classifiers are often used to evaluate the predicting accuracy of human activity recognition. This study aimed to evaluate the performance of random forest (RF) compared to other classifiers with considering the time taken to build the models. Human activity daily living data, namely walking, walking upstairs, walking downstairs, sitting, standing, and lying down were collected from smartphone-based accelerometer with sampling frequency of 50Hz. The dataset was evaluated using artificial neural network (ANN), k-nearest neighbors (KNN), linear discriminant analysis (LDA), naïve Bayes (NB), support vector machine (SVM), and random forest (RF). The results of the study showed that RF indeed predicted the activities with the highest accuracy. However, the time taken to build the models using RF was the second-longest after ANN.
<p style="text-align: justify;">University students working and studying at the same time for various reasons. The aim of the study is to review the impact of students’ dual role as students and workers on the depression rate of working university students in Batam, Indonesia. A comprehensive review of the literature revealed that working while studying does not necessarily affect students’ academic performance. However, depression is the most common health problem in university students. However, working students tend to have higher depression rate than non-working students. Depression in students may be the cause of the high dropout rate in Batam. It is important to administer depression-prevention intervention as early as possible, since the first- and second-year students are the most likely to get depressed</p><p> </p><p style="text-align: justify;"><em>Beragam alasan melatarbelakangi mahasiswa bekerja selama berkuliah. Penelitian ini mengulas dampak peran ganda sebagai mahasiswa dan pekerja kecenderungan depresi pada mahasiswa bekerja di Batam, Indonesia. Ulasan komprehensif dari literatur dan observasi langsung menunjukkan bahwa bekerja sambil berkuliah tidak mempengaruhi kinerja akademis mahasiswa. Meskipun demikian, mahasiswa bekerja cenderung memiliki tingkat depresi yang lebih tinggi daripada mahasiswa yang tidak bekerja. Depresi pada mahasiswa dapat menjadi penyebab tingginya tingkat dropout di Batam. Melakukan intervensi pencegahan depresi pada mahasiswa sedini mungkin merupakan hal yang sangat penting dikarenakan mahasiswa tingkat pertama dan kedua adalah yang paling rentan terhadap depresi.</em><em></em></p>
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.
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.
This work studies the effect of playing mobile phones while walk-ing to pedestrian safety. Thirty young-adults were observed while walking around Skanderbeg square with and without playing Pokemon GO. Results show that the walking performance deteri-orated when the participants played Pokemon GO as can be seen from the average number of laps decreased from 2.47 to 1.58 laps, the average number of collisions increased from 0.27 to 3.93, and the average number of slip, trip and fall increased from 0.03 to 2.07. It can be concluded that using mobile phone while walking could be dangerous for pedestrian safety.
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