Investigating Optimal Smartphone Placement for Identifying Stairs Movement using Machine Learning
The identification of gait activities such as stair ascending and descending poses a significant challenge due to the proximity of data provided by the sensory pathway. Accurate identification of gait activities is crucial in conveying essential gait information to users for the recognition of human movement activities. However, gait patterns can vary significantly between individuals, making it challenging to develop a generalized algorithm for identifying incline surface gait activity. Factors such as walking speed, stride length, and body mechanics can all influence gait patterns, making it difficult to establish a consistent framework. Despite various research on gait event detection for level ground walking, the identification of gait activities on an inclined surface such as stairs, especially using smartphones as sensors, is currently lacking. The goal of this study is to investigate and develop a reliable and accurate method for detecting gait activities on an inclined surface such as stairs using smartphones as the sensing device. Specifically, this study focuses on investigating optimal placement of smartphones to extract tri- axis accelerometer data from the inertial sensors during stair movement. The inertial sensor data was collected from the smartphone at two different positions and two different orientations. The data was trained against 6 machine learning algorithms namely Decision Tree, Logistic Regression, Naive Bayes, Random Forest, Neural Networks and KNN. It was observed that, by using Decision Tree and Random Forest algorithm 100% accuracy was achieved, when smartphone was placed at the thigh during stair movement. Successful identification of stair movement activity by using a smartphone can significantly contribute to future research and could also prove useful to the wider community such as amputees and those with pathological gait. In addition, since smartphones are available to a wide group of people, a low-cost solution for gait activity identification can be realized, without requiring the use of external sensors and circuitry.
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