Mouse motion speed reader6/3/2023 ![]() Ĭonsiderable research has been conducted on human motion modes and pose pattern recognition. HAR also provides guidance measures for patient treatment, and has thus attracted increased attention in the medical treatment field. However, an accelerometer is an excellent option for phoning and typing recognition. For instance, a gyroscope is an optimal sensor for pedestrian dead reckoning (PDR) when users carry their smartphone in a trouser pocket. Additionally, the optimal type of sensor during positioning varies for each human pose. Therefore, awareness of user motion modes and pose patterns can determine the correct misalignment estimation model, and potentially improve positioning solutions. Moreover, the models of misalignment estimation (i.e., differentiating between pedestrian heading and smartphone orientation) differ for each motion and pose. ![]() When using an escalator is detected, horizontal and vertical displacements should be updated. When riding an elevator is detected, the horizontal location should be fixed, whereas the vertical location must be updated. For instance, when walking is detected, users’ vertical locations should be fixed, whereas horizontal displacement and direction must be updated. Varying motion modes and pose patterns require different algorithms and constraints to obtain accurate positioning results. These activities are particularly important for pedestrian navigation applications, because they support the robustness and accuracy of the navigation. Scholars regard human motion, such as walking, being at rest, and riding an elevator, and posing, which includes activities such as calling and typing, as two highly interesting types of human activity. HAR can be used in many applications, such as precise marketing and human psychology. Analyzing human activities is an effective method for understanding the human context, living habits, and demands. Human activity recognition (HAR) has become a popular research topic. In addition, all of the tested methods performed well. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. Adaptive boosting outperformed the other methods. For motion mode recognition, a window length between 2.5–3.5 s can provide an optimal tradeoff between recognition performance and speed. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. ![]() From the results, we provide recommendations for choosing the appropriate window length. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. However, the window length is generally randomly selected without systematic study. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. Different approaches have been proposed and applied to HAR. Human activity recognition (HAR) is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology.
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